Category: Knowledge

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Mengatasi Masalah Pengurusan Tanah Perkuburan melalui Inovasi Teknologi

Laman Web Tanah Perkuburan Islam Kampung Melayu Kangkar Pulai
https://kppusara.kstutm.com/jenazahmap.php

Oleh Shahabuddin Amerudin

Dalam dunia yang semakin berkembang dan penuh dengan kemajuan teknologi, pengurusan tanah perkuburan menghadapi pelbagai cabaran yang semakin rumit. Masyarakat yang berkembang pesat memerlukan pendekatan baharu untuk mengurus dan memudahkan akses kepada tanah perkuburan. Permasalahan ini menuntut penyelesaian yang inovatif dan lebih efisien. Artikel ini akan mengetengahkan kepentingan Laman Web Tanah Perkuburan Islam Kampung Melayu Kangkar Pulai (https://kppusara.kstutm.com) sebagai satu contoh inovasi yang berjaya menangani cabaran dalam pengurusan tanah perkuburan dengan pendekatan yang lebih berkesan.

Cabaran dalam Pengurusan Tanah Perkuburan

Pertumbuhan penduduk yang pesat telah memberi tekanan kepada penggunaan tanah, termasuklah tanah perkuburan, menjadikannya satu keperluan penting untuk diurus dengan efisien. Salah satu cabaran utama adalah kesukaran waris mencari kubur ahli keluarga disebabkan saiz tanah perkuburan yang besar dan susunan yang rumit, yang sering kali memakan masa dan tenaga. Tambahan pula, dengan peningkatan jumlah kubur baru, masalah kesesakan dan kekeliruan dalam menentukan lokasi kubur yang tepat semakin ketara, terutama bagi waris yang mencari kubur ahli keluarga yang telah lama dikebumikan. Kehilangan rekod jenazah yang biasanya diurus secara manual menjadi ancaman serius, kerana rekod-rekod ini mudah rosak atau hilang, menyebabkan maklumat penting mengenai jenazah turut terjejas. Selain itu, perkembangan teknologi telah menuntut pengurusan data yang lebih sistematik dan cekap, kerana data yang tidak teratur boleh menyebabkan kekeliruan serta ketidakcekapan dalam pengurusan maklumat jenazah dan susunan kubur.

Solusi Inovatif Melalui Laman Web Tanah Perkuburan Kangkar Pulai

Laman Web Tanah Perkuburan Islam Kampung Melayu Kangkar Pulai diperkenalkan sebagai solusi inovatif untuk mengatasi cabaran pengurusan tanah perkuburan. Melalui teknologi berasaskan laman web, pengguna dapat melayari peta tanah perkuburan dan mencari lokasi kubur dengan mudah tanpa memerlukan aplikasi tambahan. Ciri interaktif peta yang disediakan memberikan pengalaman pencarian yang lebih lancar dan terperinci. Antara kelebihan utama laman web ini termasuklah sistem pencarian jenazah yang lebih mudah dan cepat, pengurusan data yang teratur, serta visualisasi peta kawasan perkuburan melalui imej ortofoto. Laman web ini telah diuji oleh penjaga kubur serta beberapa pengguna secara rawak sebelum ia dilancarkan kepada umum, yang membuktikan keberkesanannya dalam memudahkan waris mencari kubur ahli keluarga dan membantu pengurusan maklumat dengan lebih efisien.

Impak dan Kesimpulan

Penerapan teknologi inovatif seperti Laman Web Tanah Perkuburan Islam Kampung Melayu Kangkar Pulai ini menunjukkan bahawa teknologi mampu menyelesaikan cabaran-cabaran kompleks dalam pengurusan tanah perkuburan. Teknologi ini memudahkan proses pencarian kubur, mengurangkan kekeliruan dalam pengurusan data, dan yang paling penting, memastikan jenazah diberi penghormatan yang sewajarnya. Melalui projek ini, dapat dilihat bahawa teknologi berperanan penting dalam memperbaiki sektor pengurusan tanah perkuburan, selari dengan keperluan masyarakat yang semakin moden.

Suggestion for Citation:
Amerudin, S. (2023). Mengatasi Masalah Pengurusan Tanah Perkuburan melalui Inovasi Teknologi. [Online] Available at: https://people.utm.my/shahabuddin/?p=6753 (Accessed: 22 August 2023).
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Belajar lain kerja pulak lain

Keputusan untuk belajar kursus tertentu di universiti tetapi kemudian bekerja dalam bidang yang tidak berkaitan dan berhutang adalah situasi yang kompleks dan boleh mencetuskan pelbagai isu. Di bawah ini adalah beberapa aspek yang perlu dipertimbangkan:

Pilihan Kursus Universiti

Keputusan memilih kursus di universiti merupakan langkah awal penting dalam membentuk kerjaya. Sekiranya seseorang memilih kursus yang tidak sejajar dengan pekerjaan yang beliau diperolehi, ini boleh mencerminkan ketidakselarasan dalam matlamat dan minat seseorang. Penting untuk membuat kajian yang mendalam dan memilih kursus yang sesuai dengan aspirasi dan kebolehan.

Kerja Tidak Berkaitan

Mungkin ada sebab-sebab tertentu mengapa seseorang berakhir bekerja dalam bidang yang tidak berkaitan langsung dengan kursus universiti mereka. Ini boleh termasuk kekurangan peluang pekerjaan dalam bidang pilihan, tekanan ekonomi, atau perubahan minat selepas tamat pengajian. Walau bagaimanapun, perbezaan antara kursus dan pekerjaan ini boleh menyebabkan perasaan tidak puas dan kehilangan fokus dalam kerjaya.

Berhutang

Menanggung hutang adalah tanggungjawab kewangan yang serius. Sekiranya seseorang berhutang untuk membiayai pendidikan universiti atau lain-lain tujuan, ini boleh memberi tekanan tambahan kepada kehidupan kewangan mereka. Memahami kadar faedah, jangka masa, dan cara untuk mengurangkan hutang menjadi perkara penting bagi pengurusan kewangan yang berkesan.

Kesan Emosi dan Psikologi

Perasaan tidak sepadan antara kursus universiti dan pekerjaan boleh menyebabkan tekanan emosi dan psikologi. Kekecewaan dan tekanan boleh timbul apabila seseorang terperangkap dalam rutin kerja yang tidak diingini. Ini boleh memberi impak kepada prestasi kerja, motivasi, dan kebahagiaan umum.

Pertumbuhan dan Pembangunan

Walaupun bekerja dalam bidang yang tidak berkaitan dengan kursus universiti, pengalaman kerja tetap dapat memberi peluang untuk pembelajaran dan pertumbuhan. Kemahiran umum seperti pengurusan masa, komunikasi, dan kepimpinan boleh diasah dalam pelbagai persekitaran.

Rancangan Masa Depan

Penting untuk memikirkan tentang apa yang ingin dicapai dalam jangka masa panjang. Jika kursus universiti dan pekerjaan yang tidak berkaitan merupakan salah satu fasa peralihan atau langkah strategik untuk mencapai matlamat kerjaya tertentu, maka langkah tersebut mungkin tidaklah sia-sia.

Alternatif Pilihan

Seseorang boleh mempertimbangkan alternatif pilihan untuk mencari kerjaya yang lebih berkaitan dengan kursus universiti mereka, seperti kursus latihan tambahan, pekerjaan separuh masa, atau projek sampingan yang berkaitan. Ini boleh membantu mengurangkan jurang antara kursus dan pekerjaan.

Penting untuk mengambil langkah-langkah yang tepat bagi mengatasi keadaan ini. Ini boleh melibatkan perancangan semula matlamat kerjaya, mempertimbangkan peluang pembelajaran tambahan, atau mencari peluang dalam bidang yang lebih berkaitan dengan kursus universiti. Menguruskan hutang dengan bijak juga merupakan elemen penting dalam pengurusan kewangan seseorang.

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Unveiling the Power of Geospatial Artificial Intelligence (GeoAI) and its Applications

Source: https://buntinglabs.com/blog/what-is-geoai-and-how-you-can-use-it

By Shahabuddin Amerudin

Introduction

The term Geospatial Artificial Intelligence (GeoAI) lacks a universally agreed-upon definition. Initially, GeoAI referred to the utilisation of machine learning tools within Geographic Information Systems (GISs) to predict future scenarios by classifying data. This included disaster occurrence, human health epidemiology, and ecosystem evolution, aimed at bolstering community resilience through traditional geographic information in digital cartography (Esri, 2018). A broader interpretation considers GeoAI as processing Geospatial Big Data (GBD) encompassing various sources, such as digital cartography, remote-sensing-based multidimensional data, and georeferenced texts. The focal point is the geographic dimension (Janowicz et al., 2019. Thus, GeoAI merges AI techniques and data science with GBD to comprehend natural and social phenomena. A comprehensive definition views GeoAI as utilizing artificial intelligence methods like machine learning and deep learning to extract insights from spatial data and imagery (Hernandez, 2020). GeoAI serves as an emerging analytical framework, facilitating data-intensive geographic information science and environmental and social sensing, thereby understanding human mobility patterns and societal dynamics.

GeoAI’s Challenges and Research Topics

The distinctive geospatial dimension, conceptual diversity between “place” and “space,” varied spatial information formats, and diverse scales create challenges and opportunities for GeoAI (Bordogna and Fugazza, 2023). Addressing the unique geosemantics and analytical needs dictated by application goals poses new hurdles with AI integration. Research directions encompass topics like multi-resolution GBD fusion, multi-source data integration, geosummarization for enhanced data quality, and deep learning exploration in remote sensing imagery (CNN, RCNN, LSTM, GANs) (Janowicz et al., 2019). A crucial goal is bridging the gap between complex AI technologies like deep learning and transparent methods such as decision trees, clustering, and data mining. This convergence can promote explainable AI features, critical for safety-critical domains like healthcare and law enforcement.

GeoAI for Analyzing Geotagged User-Generated Content and Traces

This section delves into innovative approaches to classify and mine geotagged user-generated content and traces within social networks:

  • In the study “Spatio-Temporal Sentiment Mining of COVID-19 Arabic Social Media” by Elsaka et al. (2022), diverse AI techniques combine NLP and GeoAI to analyze geotagged Arabic tweets addressing the COVID-19 pandemic. Techniques for inferring geospatial data from non-geotagged tweets were developed, followed by sentiment analysis at various location resolutions and topic abstraction levels. Correlation-based analysis between Arabic tweets and official health data was also presented. Results indicated enhanced location-enabled tweets (from 2% to 46%) and identified correlations between topics like lockdowns, vaccines, and COVID-19 cases. The study underscores social media’s role as a valuable “social sensing” tool.
  • “Automatic Classification of Photos by Tourist Attractions Using Deep Learning Model and Image Feature Vector Clustering” by Kim and Kang (2022) exemplifies social sensing. The study automates the classification of tourist photos based on attractions using deep learning and image feature clustering. The method, applied to TripAdvisor photos, offers flexibility in extracting categories for each destination and robust classification performance with limited data.
  • “Detecting People on the Street and the Streetscape Physical Environment from Baidu Street View Images and Their Effects on Community-Level Street Crime in a Chinese City” by Yue et al. (2022) showcases social sensing’s potential. Utilizing Baidu Street View images, deep learning, and spatial statistical regression models, the study assesses street crime through user traces. This pioneering approach quantifies street inhabitants and streetscape features impacting crime, revealing the positive correlation between street population and crime assessments.

Discussion

The evolution of GeoAI has illuminated its pivotal role in unraveling complex spatial phenomena and providing valuable insights across diverse domains. The multifaceted definitions of GeoAI reflect its adaptability to a wide range of applications, from predicting disasters and tracking health trends to understanding human mobility patterns through social sensing. This adaptability, however, presents challenges related to the uniqueness of geospatial data, the heterogeneity of spatial information, and the need for transparent AI solutions.

One of the key takeaways from the exploration of GeoAI’s applications is its capacity to extract actionable insights from geotagged user-generated content and traces. The studies discussed shed light on the potency of combining advanced AI techniques with geospatial data to tackle real-world challenges. For instance, the analysis of Arabic tweets during the COVID-19 pandemic not only improved geotagging accuracy but also revealed correlations between sentiment and health outcomes. Similarly, the automatic classification of tourist photos based on attractions exemplified how GeoAI can contribute to enhancing the tourism experience through personalized recommendations.

Furthermore, the discussion around the use of GeoAI in assessing street crime via user traces demonstrates the potential of AI to leverage previously untapped data sources. By harnessing Baidu Street View images and deep learning, researchers were able to quantify the relationship between street population and crime assessments. This underscores the transformative potential of GeoAI in contributing to urban planning, crime prevention, and public safety.

Conclusion

In conclusion, Geospatial Artificial Intelligence (GeoAI) presents an exciting frontier for innovation and understanding across various domains. Its ability to analyze spatial data, extract patterns from geotagged content, and predict future scenarios is reshaping how we approach complex challenges. GeoAI’s versatility, as showcased through applications like sentiment analysis during the pandemic, tourist attraction classification, and crime assessment, underscores its potential to drive positive change in society.

However, the journey of GeoAI is not without obstacles. The diversity of geospatial data sources, the need for transparent and explainable AI models, and the integration of multi-source data pose challenges that require ongoing research and development. As GeoAI continues to advance, striking a balance between harnessing the power of complex AI techniques and ensuring interpretability and accountability becomes crucial.

Ultimately, GeoAI’s evolution will rely on collaborative efforts between AI experts, geospatial specialists, domain experts, and policymakers. By combining their expertise, we can navigate the intricate landscape of GeoAI, harnessing its potential to create a safer, more sustainable, and more informed world. Through continued exploration, research, and refinement, GeoAI is poised to revolutionize how we understand and interact with the intricate spatial dynamics of our planet.

References

Bordogna, G. and Fugazza, C. (2023). Artificial Intelligence for Multisource Geospatial Information. ISPRS Int. J. Geo-Inf., 12, 10. https://doi.org/10.3390/ijgi12010010

Elsaka, T., Afyouni, I., Hashem, I., and Al Aghbari, Z. (2022). Spatio-Temporal Sentiment Mining of COVID-19 Arabic Social Media. ISPRS Int. J. Geo-Inf., 11, 476.

Esri (2018). What is GeoAI? Available online: https://ecce.esri.ca/mac-blog/2018/04/23/what-is-geoai/ (accessed on 10 May 2023).

Hernandez, L. (2020). ELISEWebianar: GeoAI—Presentation: Geospatial Data and Artificial Intelligence—A Deep Dive into GeoAI. Available online: https://joinup.ec.europa.eu/collection/elise-european-location-interoperability-solutions-e-government/document/presentation-geospatial-data-and-artificial-intelligence-deep-dive-geoai (accessed on 10 May 2023).

Kim, J. and Kang, Y. (2022). Automatic Classification of Photos by Tourist Attractions Using Deep Learning Model and Image Feature Vector Clustering. ISPRS Int. J. Geo-Inf. 2022, 11, 245.

Janowicz, K., Gao, S., McKenzie, G., Hu, Y., and Bhaduri, B. (2019). GeoAI: Spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. Int. J. Geogr. Inf. Sci., 34, 625–636.

Yue, H., Xie, H., Liu, L., and Chen, J. (2022). Detecting People on the Street and the Streetscape Physical Environment from Baidu Street View Images and Their Effects on Community-Level Street Crime in a Chinese City. ISPRS Int. J. Geo-Inf., 11, 151.

Suggestion for Citation:
Amerudin, S. (2023). Unveiling the Power of Geospatial Artificial Intelligence (GeoAI) and its Applications. [Online] Available at: https://people.utm.my/shahabuddin/?p=6716 (Accessed: 21 August 2023).
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Navigating the Expansive Horizon of Spatial Data Science

By Shahabuddin Amerudin

Abstract

In recent times, the realm of spatial data science has witnessed an unprecedented surge, propelled by the exponential growth of spatial data and its potential applications across diverse domains. This review article delves into the multifaceted world of spatial data science, spanning its foundational principles, practical applications, inherent challenges, and the evolving research trends that are shaping its trajectory. By exploring the intricate interplay of spatial data, complexities, and novel methodologies, this review aims to provide a holistic understanding of this dynamic and interdisciplinary field.

Unveiling the Essence of Spatial Data Science

The advent of the digital age has ushered in an era of unprecedented data generation and availability. In response to this data deluge, spatial data science has emerged as a multidisciplinary discipline, seamlessly integrating methodologies from computer science, statistics, mathematics, and various specialized domains. This holistic approach is harnessed to acquire, store, preprocess, and unearth previously obscured insights from spatial data. The lifecycle of spatial data science encompasses five vital stages, namely spatial data acquisition, storage and preprocessing, spatial data mining, validation of outcomes, and the interpretation within the specific domain. Across various sectors, ranging from national security and public health to transportation and public safety, the pivotal role of spatial data science in shaping informed decisions and policies is increasingly evident.

The Landscape of Challenges in Spatial Data Science

The interdisciplinary essence of spatial data science brings forth a spectrum of challenges that must be effectively navigated. Its core engagement with tangible objects and phenomena necessitates a profound grasp of the underlying physics or theories within the pertinent domain, resulting in results that are not only interpretable but also trustworthy. The complexities posed by diverse spatial data types—ranging from object data types (such as points, lines, and polygons) to field data types like remote sensing images and digital elevation models—exceed those found in non-spatial data science. Further complexity arises from the distinctive attributes of spatial data, including spatial autocorrelation and heterogeneity. Tobler’s first law of geography—asserting that “everything is related to everything else, but near things are more related than distant things”—pervades spatial phenomena and influences analyses. The transition from discrete data inputs to continuous spatial datasets introduces an added layer of intricacy, rendering conventional non-spatial methods less applicable.

Navigating Emerging Research Trajectories in Spatial Data Science

This review article spotlights the emerging frontiers steering the evolution of spatial data science research. A key trajectory revolves around the integration of spatial and temporal information in observational data, unlocking new dimensions of understanding spatiotemporal patterns, associations, tele-coupling, prediction, forecasting, partitioning, and summarization. Expanding the realm of exploration, spatial data science is making strides within spatial networks. Cutting-edge methodologies, such as network K function and network spatial autocorrelation, are being developed to tackle spatial network data challenges. Innovations extend to the resolution of intricate puzzles like the linear hotspot discovery problem within spatial networks. An exciting avenue unfurls with spatial prediction within spatial networks, utilizing the wealth of information from GPS trajectories and on-board diagnostics (OBD) data collected from vehicles. Pioneering work by Li et al. (2018, 2019 and 2023) introduces an energy-efficient path selection algorithm grounded in historical OBD data.

Charting the Course Forward

As spatial data science continues to evolve, its centrality in diverse sectors remains pivotal. The capacity to extract actionable insights from spatial data empowers decision-makers to reimagine how they perceive and address challenges across domains. Yet, the enduring interdisciplinary nature and intrinsic attributes of spatial data pose ongoing challenges that require thoughtful consideration. By embracing these challenges and capitalizing on emerging trends, spatial data science stands poised to redefine the manner in which spatial information is harnessed. This review endeavors to guide both researchers and practitioners in navigating the intricate terrain of spatial data science, offering insights into its foundation, applications, challenges, and future horizons.

References

Li, Y., Shekhar, S., Wang, P., Northrop, W.: Physics-guided Energy-efficient Path Selection: A Summary of Results. In: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL ’18, pp. 99–108. ACM, Seattle, WA, USA (2018). https://doi.org/10.1145/3274895.3274933

Li, Y., Kotwal, P., Wang, P., Shekhar, S., Northrop, W.: Trajectory-aware Lowest-cost Path Selection: A Summary of Results. In: Proceedings of the 16th International Symposium on Spatial and Temporal Databases, SSTD ’19, pp. 61–69. ACM, Vienna, Austria (2019). https://doi.org/10.1145/3340964.3340971

Li, Y., Xie, Y., Shekhar, S. (2023). Spatial Data Science. In: Rokach, L., Maimon, O., Shmueli, E. (eds) Machine Learning for Data Science Handbook. Springer, Cham. https://doi.org/10.1007/978-3-031-24628-9_18

Suggestion for Citation:
Amerudin, S. (2023). Navigating the Expansive Horizon of Spatial Data Science. [Online] Available at: https://people.utm.my/shahabuddin/?p=6707 (Accessed: 21 August 2023).
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The Dynamic Potential of Named Entity Recognition (NER) in Extracting and Analyzing Geospatial Data

Source: https://www.esri.com/arcgis-blog/products/api-python/analytics/deep-learning-models-in-arcgis-learn/

By Shahabuddin Amerudin

Named Entity Recognition (NER), an integral component of Natural Language Processing (NLP), plays a pivotal role in extracting meaningful information from unstructured text. This technique involves the identification and classification of specific entities within text, ranging from names of people and organizations to temporal expressions and geographic locations. The applications of NER are wide-ranging and impactful across diverse industries. In this comprehensive article, we will delve deeper into the mechanics of NER, explore its diverse applications, and focus on a specific use case: geospatial data extraction facilitated by the EntityRecognizer model.

The Mechanism Behind NER

At its core, NER operates through a two-step process. The initial step involves the identification of words or phrases in the text that represent entities, which can span categories like “Person,” “Organization,” “Time,” “Location,” and more. Following this, these identified entities are categorized into predefined classes, resulting in structured information extraction from seemingly chaotic text data. This process contributes to converting unstructured text into structured data that can be utilized for further analysis.

Diverse Applications of NER

The versatility of NER transcends industries, offering valuable insights and solutions. In the realm of finance, NER is employed to extract critical information about companies, stock market trends, and financial events from news articles and reports. In healthcare, NER aids in the identification of medical terms, diseases, and treatments, supporting research and patient care. Furthermore, NER finds application in social media sentiment analysis, legal document processing, and academic research, exemplifying its widespread impact.

Application in Geospatial Data Extraction

A notable application of NER lies in geospatial data extraction, a field where unstructured text often conceals valuable location-based insights. Traditional Geographic Information Systems (GIS) primarily rely on structured data, making the integration of unstructured text a challenge. The EntityRecognizer model, as part of arcgis.learn, disrupts this barrier by leveraging advancements in deep learning and NLP (Singh, 2020). This model transforms unstructured text, such as incident reports, into structured geospatial information like feature layers, enhancing spatial analysis capabilities.

Realising Geospatial Insights

Imagine a scenario where incident reports containing unstructured text describe crime occurrences. Extracting crucial geospatial details, such as the crime type, location, incident time, and reporting time, from these reports can be arduous. The fusion of NER and the EntityRecognizer model streamlines this process. By discerning relevant entities within the text, this approach yields actionable insights that can be organized into geospatial features. Consequently, spatial analysis becomes more efficient, empowering informed decision-making.

Source: https://www.esri.com/arcgis-blog/products/api-python/analytics/deep-learning-models-in-arcgis-learn/

Unlocking New Possibilities

The amalgamation of NER and Deep Learning techniques for geospatial data extraction opens novel avenues for harnessing information locked within unstructured text. Organizations can swiftly process vast quantities of textual data, transforming them into actionable insights. These insights encompass various facets, including deciphering crime trends, identifying points of interest, and conducting sentiment analysis in specific geographic areas. NER’s application in geospatial analysis magnifies the scope of actionable intelligence derived from textual data.

Conclusion

Named Entity Recognition transcends its label as a mere NLP tool to stand as a dynamic force in information extraction. Its proficiency in autonomously identifying and classifying entities within text extends across industries, redefining data utilization. When synergized with Deep Learning, epitomized by the EntityRecognizer model within arcgis.learn, NER unveils its potential in geospatial data extraction. This integration empowers organizations to glean geospatial insights from seemingly inscrutable text, propelling spatial analysis and facilitating astute decision-making. As we traverse the ever-evolving landscape of NER and emergent technologies, the possibilities for innovative solutions in text analysis and geospatial intelligence continue to flourish.

Further Reading

  • Named Entity Extraction Workflow with: https://developers.arcgis.com/python/guide/how-named-entity-recognition-works/
  • Information extraction from Madison city crime incident reports using Deep Learning: https://developers.arcgis.com/python/samples/information-extraction-from-madison-city-crime-incident-reports-using-deep-learning/

Reference: Singh, R. (2020). Deep learning models in arcgis.learn. [Online] Available at: https://www.esri.com/arcgis-blog/products/api-python/analytics/deep-learning-models-in-arcgis-learn/ (Accessed: 19 August 2023).

Suggestion for Citation:
Amerudin, S. (2023). The Dynamic Potential of Named Entity Recognition (NER) in Extracting and Analyzing Geospatial Data. [Online] Available at: https://people.utm.my/shahabuddin/?p=6699 (Accessed: 20 August 2023).
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Unlocking Textual Insights: The Power and Applications of Named Entity Recognition (NER)

Source: https://www.analyticsvidhya.com/blog/2021/11/a-beginners-introduction-to-ner-named-entity-recognition/

By Shahabuddin Amerudin

Named Entity Recognition (NER), often referred to as entity chunking, extraction, or identification, is a vital process in the realm of Natural Language Processing (NLP). It revolves around the identification and classification of crucial information, known as entities, within text. These entities can be single words or phrases consistently referring to the same concept. Through NER, we can automatically categorize these entities into predetermined classes, such as “Person,” “Organization,” “Time,” “Location,” and more. This computational feat yields valuable insights from extensive textual data and finds its application across a plethora of scenarios.

The Mechanism Behind NER

NER models primarily operate through a two-step approach:

  1. Detecting Named Entities: This pivotal step involves the identification of words or phrases representing entities. For instance, consider the sentence “Google’s headquarters are situated in Mountain View.” Here, the entities “Google” and “Mountain View” are discerned.
  2. Categorizing Entities: Once pinpointed, these entities are then assigned to predefined categories, such as identifying “Google” as an “Organization” and “Mountain View” as a “Location.”

Categories of Recognised Entities

Typical entity categories encompass:

  • Person: Names of individuals like “Shah Deans” and “Zai Jane.”
  • Organization: References to companies or institutions, such as “Google” or “University of Nottingham.”
  • Time: Temporal indications like “2003,” “16:34,” or “2am.”
  • Location: Place names including “Forest Fields” and “Hyson Green.”
  • Work of Art: Titles of creative works like “Bohemian Rhapsody” or “The Eiffel Tower in Paris, France”

Importantly, these categories can be tailored to the task’s specific requirements or custom ontologies.

The Real-World Significance of NER

NER proves invaluable across a diverse array of contexts, including:

  • Human Resources: Condensing CVs for efficient hiring processes, categorizing employee inquiries.
  • Customer Support: Grouping user requests, complaints, and questions for quicker responses.
  • Search and Recommendation Engines: Elevating the speed and relevance of search results, much like Booking.com.
  • Content Classification: Profiling themes and subjects within blog posts and news articles.
  • Healthcare: Extracting crucial details from medical reports.
  • Academia: Summarizing research papers and making historical newspapers searchable.

Getting Started with NER

For those interested in harnessing NER’s capabilities for their projects or enterprises, a systematic approach is recommended (Marshall, 2019):

  1. Choose an NER Library: Opt for established open-source libraries like NLTK, SpaCy, or Stanford NER.
  2. Label Your Data: Assemble a dataset with annotated entities and relevant categories tailored to your task.
  3. Train Your Model: Employ the annotated dataset to train your NER model to proficiently recognize and categorize entities.
  4. Implement NER: Deploy the trained model to analyze and process text data, unveiling crucial information.

Conclusion

Named Entity Recognition stands as a formidable tool in NLP, facilitating automatic identification and categorization of specific entities in text. Its potential is far-reaching, from streamlining customer support to optimizing search engines and content classification. With accessible NER libraries and customizable labeled datasets, integrating NER into your projects is an achievable endeavor that promises enhanced insights and efficiency.

Reference: Marshall, C. (2019). What is named entity recognition (NER) and how can I use it? [Online] Available at: https://medium.com/mysuperai/what-is-named-entity-recognition-ner-and-how-can-i-use-it-2b68cf6f545d (Accessed: 19 August 2023).

Suggestion for Citation:
Amerudin, S. (2023). Unlocking Textual Insights: The Power and Applications of Named Entity Recognition (NER). [Online] Available at: https://people.utm.my/shahabuddin/?p=6696 (Accessed: 20 August 2023).
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Simplifying Automated Building Footprint Extraction with Deep Learning in GIS

Source: https://www.esri.com/arcgis-blog/products/api-python/analytics/deep-learning-models-in-arcgis-learn/


By Shahabuddin Amerudin

Abstract

This paper delves into the realm of geospatial data processing, highlighting the amalgamation of Python scripting and advanced deep learning techniques for object detection. The resulting synergy offers an avenue to streamline complex tasks within this domain. The focus of this work is on the automation of building footprint extraction from aerial imagery using these integrated methodologies.

Automated Building Footprint Extraction via Deep Learning Techniques

Consider a scenario where the conventional approach of manually delineating building footprints from newly acquired aerial imagery demands weeks of laborious effort. Conversely, a technologically empowered approach leverages Python scripting in conjunction with deep learning for object detection. This paradigm shift not only improves operational efficiency but also obviates the need for labor-intensive manual interventions.

Efficiency in Object Detection

Human cognitive abilities can rapidly identify objects within images, often accomplished within a mere 5 seconds. This cognitive phenomenon can be emulated computationally through object detection, a technique where computers discern and localize objects within images. Despite the requirement for substantial training data and meticulous labeling, this goal is attainable. Esri, a renowned GIS technology enterprise, introduces pre-trained deep learning models termed DLPKs (deep learning packets) available on the ArcGIS Online platform. These models excel in recognizing diverse elements, including building footprints, vehicles, pools, solar panels, and roads within aerial imagery.

Practical Implementation

Initiating this transformative process requires specific prerequisites. These include access to ArcGIS Pro supplemented with the Image Analyst Extension, as well as aerial imagery featuring approximately 6-inch resolution. The ensuing steps provide a comprehensive guide for harnessing the capabilities of pre-trained models:

  1. Acquisition of Deep Learning Library Installers: Retrieve and install the Deep Learning Library Installers from the dedicated GitHub repository (https://github.com/Esri/deep-learning-frameworks/blob/master/README.md).
  2. Selection of Appropriate DLPK: Explore ArcGIS Online’s living atlas to identify the relevant DLPK suited for the intended object extraction task, such as building footprint identification.
  3. Integration of Aerial Imagery: Launch the ArcGIS Pro Project and import the targeted aerial imagery.
  4. Execution of Object Detection: Access the Geoprocessing window and select “Detect Objects Using Deep Learning.”
  5. Configuration of Object Detection: Specify the relevant raster image as input, provide an output name, and reference the downloaded DLPK. The tool will automatically populate the required parameters.
  6. Initiation of Automated Extraction: Commence the process by activating the “Run” button, subsequently witnessing the automated delineation of building footprints.

Overcoming Challenges and Enhancing Results

While maintaining optimistic expectations, acknowledge that processing speed is influenced by geographical extent and building density. It is recommended to perform preliminary tests on smaller image segments prior to achieving desired outcomes. Additionally, note that resulting building footprints might exhibit curvature and lack geometric precision. To address this, the “Regularize Building Footprints” Geoprocessing tool can rectify curvature issues by enforcing right-angle conformity (Fisher, 2021).

An optimization technique involves employing Model Builder to partition extensive raster images into manageable squares, thereby enhancing performance by processing a reduced dataset. Concluding this workflow, the merging of inferred building footprints into a cohesive layer is straightforward.

Performance Advantages and Future Prospects

The presented approach demonstrates operational efficiency, optimally utilizing computational hardware and system resources. Personal experience suggests the feasibility of background processing for an entire county over several days, concurrently managing other computer tasks (Fisher, 2021).

For those seeking in-depth engagement, the ArcGIS Pretrained Models documentation (https://doc.arcgis.com/en/pretrained-models/latest/get-started/intro.htm) offers a comprehensive resource for delving into the intricacies of these pre-trained models and their potential applications.

Reference

Fisher, C. (2021). Artificial Intelligence in GIS or “GeoAI”. [Online] Available at: https://www.linkedin.com/pulse/artificial-intelligence-gis-geoai-chase-fisher/ (Accessed: 19 August 2023).

Suggestion for Citation:
Amerudin, S. (2023). Simplifying Automated Building Footprint Extraction with Deep Learning in GIS. [Online] Available at: https://people.utm.my/shahabuddin/?p=6690 (Accessed: 20 August 2023).
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Geoscape Greens: A Journey of Innovation and Sustainability – Part 1

Source: https://climbinghi.com/tree-inventory-mapping/

By Shahabuddin Amerudin

In the vibrant city of Nusantara, nestled between towering skyscrapers and bustling streets, stood the headquarters of GeoSmart Solutions, a leading GIS firm. Among its talented employees was Ahmad, a brilliant System Analyst with a passion for technology, geography, and a penchant for turning ideas into reality.

Ahmad was entrusted with leading a remarkable team on an ambitious project: the development of an innovative GIS application that would redefine how tree information was collected, analyzed, and utilized. This application would seamlessly integrate a cloud-based web platform with both iOS and Android mobile devices, enabling users to capture intricate tree data with submeter precision using their smartphones.

The journey began with a brainstorming session that crackled with excitement. Ahmad and his team huddled in the sleek conference room, sketching out visions of a cutting-edge application that would blend powerful technology with environmental stewardship. The challenge was immense, but Ahmad’s leadership inspired the team to forge ahead.

To lay a solid foundation, the team chose to leverage open-source tools. They designed a captivating web-based map using Leaflet, ensuring a user-friendly interface and fluid navigation. The heart of the application, the online database powered by MySQL, was meticulously constructed to handle vast amounts of geospatial data collected from the field.

As lines of code transformed into functional features, the team unveiled a host of impressive functionalities. The dynamic visualization feature breathed life into the maps, allowing users to interact with the data in real time. A comprehensive dashboard provided an at-a-glance overview of critical metrics, aiding decision-makers in shaping urban green spaces effectively.

Spatial analysis tools were crafted with precision, empowering users to conduct intricate geospatial calculations. Clusters of trees, trends in vegetation growth, and patterns of disease outbreaks could be deciphered with a few clicks. The application was evolving into a veritable powerhouse of environmental insights.

The most thrilling phase, however, was the integration of AI for advanced analysis. Ahmad collaborated closely with data scientists to develop machine learning models that predicted not only tree growth and health but also the potential spread of diseases. The algorithms digested copious amounts of data, unveiling trends that human eyes might miss. The marriage of AI and GIS was poised to bring about a paradigm shift.

As months of intensive development came to a close, the team was consumed by both anxiety and exhilaration. Field trials were launched, putting the application’s accuracy and reliability to the test. The city’s parks, avenues, and botanical gardens transformed into testing grounds, with users capturing tree data with precision that was once deemed impossible.

And then, the day arrived—the day the GIS application was ready to be unveiled to the world. The launch event was a culmination of creativity, innovation, and sheer determination. The presentation of the application left the audience in awe, and excitement rippled through the room as the first downloads began.

Users across Nusantara embraced the application with fervour. Urban planners marveled at its potential to inform city development plans. Environmentalists saw an ally in their efforts to preserve green spaces. Scientists reveled in the wealth of data for research. The city’s trees, once overlooked, became stars of their own story.

As time passed, Ahmad looked back at the tumultuous yet triumphant journey. The application’s impact had exceeded all expectations, fostering a greener, more sustainable urban landscape. Ahmad’s team continued to refine the application, incorporating user feedback and pushing the boundaries of technology and GIS.

Through the collaboration of visionaries, analysts, data scientists, and users, the GIS application stood as a testament to the power of innovation. The story of Ahmad and his team echoed in the city’s parks, streets, and botanical gardens—a tale of how a single idea, when nurtured by dedication and expertise, could grow into something that changed the world.

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GeoAI: Merging Geospatial Data and AI for Enhanced Decision-Making

By Shahabuddin Amerudin

Geospatial Artificial Intelligence (GeoAI) is a specialized field that combines geospatial data, which includes geographic information such as location, coordinates, and spatial relationships, with artificial intelligence (AI) techniques to extract valuable insights, patterns, and predictions from spatially referenced data. In essence, GeoAI involves the application of AI algorithms and methodologies to geospatial data to solve complex problems and enhance decision-making in various domains.

Key Components of GeoAI

  1. Geospatial Data: GeoAI relies on various types of geospatial data, such as satellite imagery, GPS coordinates, maps, geographic databases, and sensor data. These data sources provide the spatial context necessary for understanding and analyzing patterns and phenomena.
  2. Artificial Intelligence Techniques: AI techniques employed in GeoAI include machine learning, deep learning, natural language processing, computer vision, and other AI subfields. These techniques help process and analyze geospatial data to extract meaningful information.
  3. Data Fusion: GeoAI often involves the integration of multiple data sources, which may include satellite imagery, sensor data, and demographic information. Data fusion techniques are used to combine these sources and generate more accurate and comprehensive insights.

Applications of GeoAI

  1. Urban Planning and Management: GeoAI can aid in urban planning by analyzing traffic patterns, identifying suitable locations for infrastructure development, and predicting urban growth trends. It can also assist in managing city resources more efficiently.
  2. Environmental Monitoring: GeoAI is crucial for monitoring and assessing environmental changes, such as deforestation, climate change impacts, and natural disasters. It helps in early detection, response planning, and mitigation strategies.
  3. Agriculture and Precision Farming: GeoAI can analyze satellite images and sensor data to provide insights into crop health, soil quality, and water availability. This information enables farmers to optimize crop yields and resource usage.
  4. Disaster Management: GeoAI aids in disaster preparedness and response by analyzing real-time data from various sources to assess the extent of damage, identify affected areas, and plan rescue and relief operations.
  5. Infrastructure Maintenance: It can predict maintenance needs for infrastructure like roads, bridges, and utility networks by analyzing usage patterns, wear and tear, and other relevant data.
  6. Natural Resource Management: GeoAI helps monitor and manage natural resources like forests, water bodies, and mineral deposits, assisting in sustainable resource utilization.
  7. Public Health: GeoAI can analyze disease spread patterns, healthcare facility locations, and demographic data to improve disease surveillance and healthcare resource allocation.

Tools and Software Platforms for GeoAI

There are several tools and software platforms available for working with GeoAI. These tools offer functionalities for processing, analyzing, visualizing, and deriving insights from geospatial data using AI techniques. Here are some commonly used tools and software in the GeoAI domain:

  1. GIS Software
    • ArcGIS: A widely used geographic information system (GIS) software suite that offers tools for geospatial analysis, mapping, and visualization.
    • QGIS: An open-source GIS software that provides similar capabilities to ArcGIS, making it a popular choice for users seeking cost-effective solutions.
  2. Remote Sensing and Image Analysis
    • ENVI: A software platform for remote sensing and image analysis, suitable for processing satellite and aerial imagery for various applications.
    • Google Earth Engine: A cloud-based platform for analyzing geospatial data, particularly satellite imagery, using Google’s computational resources.
  3. Machine Learning and Data Science
    • Python: A versatile programming language commonly used for data analysis and machine learning. Libraries like NumPy, pandas, scikit-learn, and TensorFlow can be used for GeoAI applications.
    • R: Another programming language often used for statistical analysis and data visualization, with packages like the sf package for geospatial data manipulation.
  4. Deep Learning Frameworks
    • TensorFlow: An open-source deep learning framework developed by Google, suitable for building and training neural networks for geospatial tasks like image analysis.
    • PyTorch: Another popular deep learning framework that provides flexibility and ease of use, suitable for various AI tasks including geospatial applications.
  5. Geospatial Data Libraries
    • Geopandas: A Python library that extends the capabilities of pandas to handle geospatial data, making it easier to manipulate, analyze, and visualize spatial data.
    • Rasterio: A library for reading and writing geospatial raster data, allowing manipulation of satellite and aerial imagery.
  6. Visualization Tools
    • Matplotlib: A popular Python library for creating static, interactive, and dynamic visualizations, useful for visualizing geospatial data and analysis results.
    • Folium: A Python library that enables the creation of interactive maps and visualizations using leaflet.js.
  7. Cloud Computing Platforms
    • Amazon AWS: Offers cloud-based solutions for geospatial data storage, processing, and analysis, with services like Amazon S3 and Amazon EC2.
    • Google Cloud Platform: Provides tools and services for working with geospatial data, including Google Earth Engine and BigQuery GIS.
  8. Specialized GeoAI Platforms
    • SpaceNet: A collaborative project that provides high-quality satellite imagery datasets for AI research and development in tasks such as building footprint detection and road network extraction.
    • Esri GeoAI: Offers tools and solutions specifically designed for combining GIS and AI techniques for spatial analysis and decision-making.

The choice of tools and software depends on the specific tasks, data sources, and expertise available. Many GeoAI practitioners use a combination of these tools to effectively handle geospatial data and apply AI techniques for meaningful insights.

Challenges and Considerations

  1. Data Quality: Geospatial data can vary in quality and resolution, which affects the accuracy of GeoAI models. Ensuring data quality is crucial for reliable insights.
  2. Interdisciplinary Expertise: GeoAI requires collaboration between AI experts, geospatial analysts, and domain specialists to effectively address complex challenges.
  3. Ethical Concerns: Privacy, security, and potential biases in data can pose ethical concerns, especially when dealing with location-based information.
  4. Computational Resources: Processing large volumes of geospatial data requires significant computational power, which can be a limiting factor.
  5. Regulations and Standards: Different regions might have varying regulations and standards for geospatial data collection, sharing, and usage, which need to be navigated.

GeoAI holds tremendous potential to revolutionize decision-making processes across various industries by providing actionable insights derived from spatial data. However, its successful implementation requires a combination of technical expertise, high-quality data, and a deep understanding of the specific domain in question.

Suggestion for Citation:
Amerudin, S. (2023). GeoAI: Merging Geospatial Data and AI for Enhanced Decision-Making. [Online] Available at: https://people.utm.my/shahabuddin/?p=6667 (Accessed: 18 August 2023).
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GeoAI: Unveiling Patterns and Shaping Futures at the Nexus of Geography and Artificial Intelligence

By Shahabuddin Amerudin

Introduction

In the contemporary era of technological advancements, the amalgamation of artificial intelligence (AI) with geography has ushered in a revolutionary field known as GeoAI. This interdisciplinary domain leverages the prowess of AI to decode intricate patterns concealed within geospatial data, enabling us to predict, analyze, and respond to a spectrum of events and phenomena. From predicting ecological shifts to deciphering human mobility trends, GeoAI stands as a beacon of innovation that reshapes our perception of the world. In this article, we delve deeper into the essence of GeoAI and its multifaceted applications, bringing to light its significance and impact.

Defining GeoAI: From Narrow to Expansive Horizons

GeoAI’s foundation rests on the seamless integration of machine learning, data science, and Geographic Information Systems (GIS), creating a synergy that enables the exploration of Earth’s intricacies. This dynamic field embraces a range of definitions, each reflective of its multifarious dimensions.

In a narrower context, GeoAI entails the application of machine learning toolkits within the framework of GISs to simulate potential future scenarios. Through techniques such as data classification and intelligent predictive analysis, this facet of GeoAI forecasts outcomes encompassing natural disasters, health epidemiology, and biodiversity evolution. By processing conventional geographic information represented through digital cartography, these insights bolster community resilience and facilitate informed decision-making.

Expanding the scope, GeoAI transcends into the realm of Geospatial Big Data (GBD), encompassing a myriad of heterogeneous forms and sources. This expansive view accommodates not only traditional digital cartography managed by GIS but also incorporates remote-sensing-derived multidimensional data, georeferenced texts, and complex geo-databases. The underlying emphasis remains steadfastly fixed on the spatial dimension, weaving together a holistic comprehension of our planet’s complexities.

GeoAI’s Integral Role in Revelation

GeoAI transcends the mere processing of data; its essence lies in unearthing hidden truths encapsulated within that data. By amalgamating AI methodologies with geographic information, GeoAI empowers us to unravel the mysteries inherent in both natural and social phenomena. Picture a scenario where AI algorithms meticulously analyze satellite images to forecast deforestation patterns, enabling authorities to enact proactive conservation measures. This vividly portrays the core of GeoAI: transforming raw data into actionable insights.

GeoAI: A Universally Applicable Paradigm

In its broader context, GeoAI functions as the nexus between AI methodologies and spatial data, employing a comprehensive toolkit including machine learning and deep learning techniques. This amalgamation facilitates the extraction of knowledge from spatial data and imagery, underpinning a groundbreaking spatial analytical framework. This framework is not confined solely to environmental studies; it encompasses the broader spectrum of “social sensing.” This entails harnessing the digital traces people leave behind as they engage with the Internet of Things (IoT) and generate content on social networks. GeoAI, thus, acts as a decoder of urban dynamics, illuminating human mobility trends and sociocultural phenomena through the analysis of these digital imprints.

The Uncharted Landscape of GeoAI: A Promising Future

In conclusion, as we navigate the frontiers of AI and geography, GeoAI emerges as a compelling terrain where the two disciplines converge and synergize. Its capacity to decipher complex patterns, predict future occurrences, and unveil concealed insights sets it apart as a transformative paradigm. From disaster preparedness to unraveling societal dynamics, GeoAI ushers in a future where information shapes action. For undergraduate students keen on exploring the intersection of technology, geography, and the power of data, GeoAI presents a captivating avenue of discovery. As the landscape of GeoAI continues to evolve, its potential to reshape our understanding of the world remains boundless, promising a future replete with innovation and insight.

Suggestion for Citation:
Amerudin, S. (2023). GeoAI: Unveiling Patterns and Shaping Futures at the Nexus of Geography and Artificial Intelligence. [Online] Available at: https://people.utm.my/shahabuddin/?p=6663 (Accessed: 18 August 2023).
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Analisis dan Komen Mengenai Laporan Perkembangan Ekonomi dan Kewangan Malaysia pada Suku Kedua 2023

Oleh Shahabuddin Amerudin

Sumber: https://www.bnm.gov.my/-/qb23q2_bm_pr


Pada tarikh 18 Ogos 2023, Bank Negara Malaysia telah menerbitkan laporan mengenai perkembangan ekonomi dan kewangan Malaysia pada suku kedua tahun 2023. Laporan ini memberikan gambaran yang mendalam mengenai prestasi ekonomi negara dalam tempoh yang diulas. Mari kita lihat secara kritikal dan memberikan komen mengenai beberapa aspek yang diberikan dalam laporan ini.

Pertumbuhan Ekonomi yang Sederhana

Menurut laporan, ekonomi Malaysia mengalami pertumbuhan sederhana sebanyak 2.9% pada suku kedua tahun 2023. Walaupun pertumbuhan ini lebih rendah berbanding suku pertama tahun yang sama (5.6%), ia masih dianggap memberangsangkan berdasarkan kepada cabaran daripada permintaan luaran yang lebih perlahan. Permintaan domestik terus menjadi pendorong utama pertumbuhan, disokong oleh sektor swasta dan pelaburan. Kecenderungan ini menunjukkan prestasi kestabilan dalam ekonomi Malaysia yang bergantung kepada pelaburan dan perbelanjaan domestik.

Pengawalan Inflasi yang Berjaya

Inflasi telah berjaya dikawal dengan baik pada suku kedua tahun ini, dengan kadar inflasi terutamanya pada tahap yang sederhana pada 2.8%. Ini merupakan pencapaian yang baik kerana inflasi yang terkawal akan mengekalkan daya beli rakyat dan menggalakkan pertumbuhan ekonomi yang mapan. Walau bagaimanapun, laporan juga menunjukkan bahawa inflasi dalam segmen tertentu seperti perkhidmatan tertentu masih tinggi berbanding purata jangka panjang, dan ini perlu diambil perhatian untuk memastikan tahap inflasi yang mapan.

Pengaruh Kadar Pertukaran Mata Wang

Perkembangan global tetap menjadi faktor dominan dalam mempengaruhi keadaan kewangan domestik Malaysia. Faktor seperti aliran pasaran kewangan global, harga komoditi global, dan permintaan semikonduktor global turut mempengaruhi pasaran kewangan dalam negara. Ringgit Malaysia telah merosot sebanyak 5.8% dalam suku kedua 2023 disebabkan oleh faktor-faktor ini. Namun, berita baik adalah bahawa ringgit telah mengalami peningkatan sebanyak 1.1% dalam suku ketiga, menunjukkan potensi pulihnya mata wang ke peringkat yang lebih stabil.

Keadaan Pembiayaan

Pertumbuhan kredit kepada sektor bukan kewangan swasta telah merosot sedikit kepada 3.8%, yang membawa kepada pemulihan pertumbuhan hutang korporat. Walau bagaimanapun, hutang korporat masih berkembang dengan baik pada kadar 4.9%, menunjukkan kemakmuran dalam sektor korporat. Hutang rakyat pula berkembang sebanyak 5.1%, terutamanya disebabkan oleh pinjaman bagi pembelian hartanah kediaman dan kenderaan.

Prospek Masa Hadapan

Laporan ini juga memberi pandangan mengenai prospek ekonomi Malaysia untuk tempoh yang akan datang. Dalam keadaan persekitaran global yang mencabar, pertumbuhan ekonomi Malaysia dijangka mencapai tahap rendah dalam julat 4.0% hingga 5.0% untuk tahun 2023. Walaupun begitu, pertumbuhan ini akan diperkukuhkan oleh permintaan domestik yang teguh, pelaksanaan projek-projek jangka panjang, dan kenaikan aktiviti pelancongan. Namun, risiko terhadap pertumbuhan ini terletak pada kadar pertumbuhan perlahan faktor global yang tidak dijangka.

Kesimpulan

Laporan ini memberikan wawasan yang menyeluruh mengenai perkembangan ekonomi dan kewangan Malaysia pada suku kedua tahun 2023. Prestasi yang sederhana dalam pertumbuhan ekonomi dan pengawalan inflasi yang baik menunjukkan kecekapan dan daya tahan dalam pentadbiran ekonomi negara. Namun, perhatian perlu diberikan kepada keadaan kewangan global dan impaknya terhadap mata wang serta pasaran kewangan tempatan. Dengan strategi yang tepat, Malaysia mampu menghadapi cabaran global dan terus melangkah ke hadapan dengan keyakinan.

Suggestion for Citation:
Amerudin, S. (2023). Analisis dan Komen Mengenai Laporan Perkembangan Ekonomi dan Kewangan Malaysia pada Suku Kedua 2023. [Online] Available at: https://people.utm.my/shahabuddin/?p=6658 (Accessed: 18 August 2023).
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Free Online Guide: Uploading a Website with PHP and MySQL Database

By Trending Youth

In this tutorial, the video creator will walk you through the steps of uploading your website with a database for free, without requiring any financial investment. The video creator has previously designed a signup-login website using PHP and MySQL, and now they will illustrate the process of effectively deploying it online.

By meticulously adhering to the instructions provided in the video, you can ensure that your website and its associated database are accessible to a global audience. Be assured that individuals with a stable internet connection can easily access your website from various devices such as smartphones and PCs.

For additional details and pertinent links pertaining to this tutorial, please visit: https://www.000webhost.com.

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Steps to Publish an HTML Website Online and Make it Accessible on the Internet

https://www.youtube.com/watch?v=p1QU3kLFPdg
By SuperSimpleDev

Learn how to put a website online on the Internet for free with GitHub Pages (using a free GitHub Pages domain name). Learn how to buy and set up a custom domain name (like “mywebsite.com”). Learn how to set up HTTPS SSL encryption for free.

Sample website you can practice with: https://github.com/SuperSimpleDev/git…

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0:00 Intro

0:24 1. Put a website on the Internet

3:34 Upload our code to GitHub

7:02 How GitHub Pages works

8:24 Add an index.html

10:51 2. Set up a domain name

12:34 Get a new domain name

15:37 How the Internet Works

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21:55 Find the IP addresses of GitHub Pages

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26:07 Link our domain name in GitHub Pages

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29:05 Thanks for watching!

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Implikasi dan Pertimbangan Kenaikan Gred Markah 4 dan 9 dalam Sistem Penilaian

Oleh Shahabuddin Amerudin

Markah dan Gred Baru

Keputusan untuk menggalakkan pensyarah supaya menaikkan gred markah kursus yang mempunyai angka 4 dan 9 dihujungnya adalah suatu isu yang mempunyai pelbagai sudut pandangan dan pertimbangan. Sebagai contoh, markah keseluruhan pelajar sepanjang semester, iaitu selepas dicampurkan dengan skor kuiz, ujian, tugasan, projek, dan peperiksaan akhir, jika markahnya adalah 64, maka perlu dinaikkan kepada 65. Begitu juga, jika markah pelajar adalah 69, maka perlu dinaikkan kepada 70. Ini akan menjadikan gred markah seolah-olah tidak lagi mempunyai angka 4 dan 9 di dalamnya. Di bawah ini adalah beberapa aspek yang perlu dipertimbangkan dalam situasi ini:

Kelebihan

  1. Motivasi Positif: Tindakan ini mungkin dapat memberikan motivasi positif kepada pelajar kerana mereka akan melihat peningkatan sedikit dalam gred walaupun markah mereka hanya sedikit kurang dari angka seterusnya.
  2. Psikologi Pelajar: Penghapusan angka 4 dan 9 dapat mengurangkan tekanan psikologi di kalangan pelajar yang seringkali mengaitkan angka ini dengan kegagalan atau prestasi rendah.
  3. Peningkatan Prestasi Pelajar: Sekiranya tindakan ini berjaya memotivasikan pelajar untuk lebih berusaha, ia mungkin dapat membantu meningkatkan prestasi pelajar dalam jangka panjang.

Kekurangan

  1. Kecekapan dan Keadilan: Menaikkan gred tanpa mengambil kira prestasi sebenar pelajar boleh menjejaskan kecekapan dan keadilan sistem penilaian. Pelajar yang sepatutnya mendapat gred rendah mungkin akan menerima gred yang tidak sepadan dengan prestasi mereka.
  2. Pengurangan Standard: Penghapusan angka 4 dan 9 boleh mengurangkan standard akademik universiti. Ini mungkin memberi gambaran yang tidak tepat tentang prestasi pelajar dan menyulitkan pembandingan prestasi antara pelajar.
  3. Kehilangan Pelajaran: Pelajar mungkin tidak belajar untuk menghadapi kegagalan atau mengatasi kesukaran jika mereka tahu gred mereka akan dinaikkan secara automatik.
  4. Kurangnya Pengiktirafan Prestasi: Pelajar yang benar-benar berusaha untuk mencapai markah lebih tinggi mungkin tidak akan mendapat pengiktirafan yang setimpal kerana markah mereka akan dinaikkan secara seragam.

Pertimbangan Alternatif

  1. Peningkatan Kaedah Penilaian: Lebih baik untuk mempertimbangkan untuk meningkatkan kaedah penilaian yang lebih berfokus pada pemahaman dan kebolehan pelajar dalam menguasai kandungan. Ini boleh membantu mengukuhkan prestasi pelajar secara beransur-ansur tanpa mengorbankan standard akademik.
  2. Penyediaan Bantuan Tambahan: Universiti boleh mempertimbangkan untuk menyediakan bantuan tambahan seperti kelas tambahan, sokongan akademik, atau sesi mentor bagi pelajar yang menghadapi kesulitan dalam prestasi akademik.
  3. Pemahaman Psikologi Pelajar: Lebih penting untuk memahami mengapa pelajar mengaitkan angka 4 dan 9 dengan prestasi rendah. Ini mungkin berkaitan dengan tekanan sosial atau kurangnya keyakinan diri. Dengan memahami akar masalah ini, universiti dapat memberikan sokongan yang lebih tepat kepada pelajar.

Keseluruhannya, keputusan untuk menggalakkan kenaikan gred markah 4 dan 9 mempunyai implikasi yang meluas dalam sistem pendidikan. Penting untuk mempertimbangkan kesan jangka panjang dan potensi perubahan dalam polisi penilaian serta impak terhadap kualiti pendidikan yang disampaikan.

Suggestion for Citation:
Amerudin, S. (2023). Implikasi dan Pertimbangan Kenaikan Gred Markah 4 dan 9 dalam Sistem Penilaian. [Online] Available at: https://people.utm.my/shahabuddin/?p=6641 (Accessed: 16 August 2023).
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Cabaran Sikap Pelajar Universiti Terhadap Kemerosotan Pencapaian Akademik di Era Digital

Oleh Shahabuddin Amerudin

Dalam era digital yang penuh dengan kemudahan dan akses kepada maklumat, sikap pelajar universiti terhadap pencapaian akademik telah menunjukkan corak yang mencabar. Walaupun pelajar kini mempunyai pelbagai kemudahan dan sokongan untuk berjaya dalam pengajian mereka, namun masih terdapat pelbagai isu yang perlu diberi perhatian bagi mengatasi kemerosotan pencapaian akademik pelajar terbabit. Artikel ini akan membincangkan beberapa isu utama yang telah dinyatakan dalam konteks sikap pelajar universiti pada masa kini terhadap pencapaian akademik mereka.

Salah satu isu yang ketara adalah kurangnya usaha dan keengganan pelajar dalam melaksanakan tugasan yang diberikan oleh pensyarah. Sikap ini seringkali merumitkan proses pembelajaran dan menghambat perkembangan akademik pelajar. Penerimaan terhadap tugas sebagai tanggungjawab yang harus diambil serius masih belum ditanamkan dalam diri sebahagian besar pelajar. Fenomena ini turut disebabkan oleh kecenderungan untuk menangguhkan kerja hingga akhirnya tugasan tersebut tidak dapat disiapkan dengan baik.

Selain itu, sikap tidak kisah dan sering tidak hadir ke kuliah tanpa alasan yang kukuh turut membayangkan kecenderungan kurang serius dalam pendidikan. Ketidakhadiran ini mengganggu proses pembelajaran dan menghalang pelajar daripada memahami dengan baik topik yang diajar. Disamping itu, kelemahan dalam pengurusan masa menyumbang kepada ketidakmampuan pelajar menghadiri kuliah dan menyiapkan tugasan dengan baik.

Isu pengurusan masa menjadi perbualan yang sering kedengaran dalam kalangan pelajar. Walaupun universiti telah mengingatkan berkali-kali tentang kepentingan pengurusan masa, masih terdapat segelintir pelajar yang acuh tak acuh dan tidak mengambil berat akan nasihat tersebut. Terdapat kes-kes pelajar yang menghadapi kesukaran untuk menyeimbangkan antara pelbagai komitmen seperti kuliah, tugasan, dan kegiatan sosial, yang mengakibatkan pencapaian akademik merosot.

Fenomena mencari bahan tugasan di Internet juga menjadi satu isu yang patut dibincangkan. Walaupun teknologi memberikan peluang untuk mendapatkan maklumat dengan mudah, pelajar sering terjebak dalam memilih bahan yang tidak relevan atau tidak sah. Hal ini merosakkan kualiti kerja yang dihasilkan dan menghalang pemahaman mendalam terhadap topik yang dipelajari.

Sistem portal pendidikan yang memudahkan penghantaran tugasan di dalam bentuk digital juga telah disia-siakan oleh sebahagian pelajar. Keadaan ini membuktikan bahawa walaupun peluang sudah sedia ada, pelajar masih belum mampu menguruskan masa dan komitmen dengan baik. Berbanding dengan pelajar di era sebelum digital yang perlu menggunakan mesin taip atau pun mencetak dengan printer apabila menghasilkan laporan.

Selain itu, masalah beban kerja yang terlalu banyak turut menyumbang kepada kemerosotan pencapaian akademik. Pelajar yang mengambil terlalu banyak kursus atau terlibat dalam pelbagai projek universiti, kokurikulum dan sosial berlebihan cenderung untuk terjebak dalam tekanan dan kesukaran menyiapkan tugasan.

Di samping itu, keterlibatan dalam projek kumpulan juga boleh merumitkan pencapaian akademik. Pelajar yang terlalu banyak berfokus kepada projek kumpulan boleh menghadapi kesukaran dalam menumpukan masa dan tenaga kepada tugas individu.

Dalam usaha untuk mengatasi isu-isu ini, universiti perlu memberi penekanan kepada aspek pengurusan masa dalam kurikulum pendidikan mereka. Sistem sokongan akademik perlu diperkukuhkan dengan menyediakan kaedah untuk membantu pelajar menguruskan masa dan komitmen dengan baik. Selain itu, kesedaran tentang kepentingan penghayatan tanggungjawab dan usaha peribadi dalam pendidikan harus ditanamkan dalam minda pelajar.

Pendidikan berkualiti adalah usaha bersama antara pensyarah, pelajar, dan pihak pentadbiran universiti. Dengan mengatasi isu-isu sikap pelajar yang merosot dalam era digital ini, kita dapat meningkatkan kualiti pendidikan dan membantu pelajar mencapai pencapaian akademik yang lebih baik.

Suggestion for Citation:
Amerudin, S. (2023). Cabaran Sikap Pelajar Universiti Terhadap Kemerosotan Pencapaian Akademik di Era Digital. [Online] Available at: https://people.utm.my/shahabuddin/?p=6633 (Accessed: 16 August 2023).
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Kemerosotan Pencapaian Akademik dan Sikap Pelajar Universiti Pasca Era COVID-19 dan Perintah Kawalan Pergerakan (PKP)

Oleh Shahabuddin Amerudin

Pendahuluan

Pandemik COVID-19 telah membawa transformasi yang signifikan dalam sistem pendidikan, terutama di peringkat universiti. Era pasca-COVID-19 dan tempoh Perintah Kawalan Pergerakan (PKP) telah memberi kesan mendalam kepada sikap pelajar universiti terhadap pencapaian akademik mereka. Fenomena di mana pelajar kurang berusaha dalam melaksanakan tugasan, kurang hadir ke kuliah, dan suka menangguhkan kerja mengakibatkan kemerosotan pencapaian akademik yang mencemaskan. Terdapat beberapa faktor yang dapat dihubungkan dengan perkembangan ini. Artikel ini membincangkan fenomena kemerosotan dalam pencapaian akademik dan sikap yang kurang berusaha pelajar selepas era COVID-19 dan PKP.

Faktor-Faktor yang Mempengaruhi Sikap Pelajar

1. Masalah Kesejahteraan Emosi

Pandemik dan PKP telah mencipta gelombang tekanan emosi di kalangan pelajar, memberikan impak yang signifikan terhadap sikap dan pencapaian akademik mereka. Isolasi sosial, ketidakpastian mengenai kesihatan, dan kebimbangan tentang keselamatan diri dan keluarga telah mencipta perasaan keterasingan dan kebimbangan yang mendalam. Kehilangan interaksi langsung dengan rakan sebaya dan suasana pembelajaran universiti boleh merosakkan aspek sosial dan emosi pelajar, mengurangkan motivasi mereka untuk terlibat sepenuhnya dalam proses pembelajaran.

Keadaan emosi yang tidak stabil ini tidak hanya mempengaruhi kesejahteraan pelajar, tetapi juga kognitif dan pencapaian akademik mereka. Kehilangan tumpuan, kelesuan mental, dan kekurangan motivasi adalah beberapa hasil daripada ketidakseimbangan emosi. Inilah yang mengganggu proses pengumpulan maklumat, pemahaman konsep, dan kemampuan untuk menyelesaikan tugasan dengan efektif. Pelajar yang terlibat dalam peperiksaan dan tugasan tanpa perasaan yang seimbang boleh menghasilkan hasil yang lebih lemah dan tidak memuaskan.

2. Kurangnya Struktur dan Disiplin

Pembelajaran dalam talian, sementara menawarkan kelebihan seperti fleksibiliti, juga membawa cabaran dalam hal pengurusan diri dan pengurusan masa. Pelajar sekarang perlu mengambil alih tanggungjawab yang lebih besar dalam membangunkan jadual pembelajaran mereka, membuat keputusan mengenai waktu kerja dan rehat, serta menguruskan pengumpulan tugasan. Bagi mereka yang kurang terlatih dalam kemahiran pengurusan diri ini, pembelajaran dalam talian boleh menjadi cabaran yang besar.

Tanpa struktur dan disiplin yang kukuh, pelajar mungkin mengalami kesukaran dalam menjaga rutin pembelajaran yang teratur. Mereka cenderung menangguhkan tugasan atau mengambil sikap santai dalam hal-hal akademik. Kurangnya jadual yang teratur boleh mengakibatkan terabai dalam memenuhi tenggat masa penting, dan ini berpotensi merosakkan kualiti hasil kerja mereka.

3. Gangguan Maklumat Digital

Dalam era digital, akses kepada hiburan dan maklumat adalah lebih mudah daripada sebelumnya. Namun, ini juga membawa risiko gangguan yang besar dalam konteks pembelajaran. Pelajar yang terlibat dalam pembelajaran dalam talian boleh terdedah kepada pelbagai gangguan seperti media sosial, permainan dalam talian, dan hiburan digital lain. Kecenderungan untuk menangguhkan kerja dan menumpukan perhatian kepada aktiviti yang tidak berkaitan dengan pembelajaran boleh menjejaskan produktiviti dan fokus mereka.

Gangguan maklumat digital ini juga boleh mempengaruhi kualiti pemahaman dan penyerapan bahan pembelajaran. Pelajar mungkin cenderung untuk membaca dengan lewat atau tergesa-gesa melalui bahan pembelajaran, yang berpotensi merosakkan kefahaman mereka terhadap konsep-konsep kritikal.

Strategi untuk Mengatasi Kemerosotan Pencapaian Akademik dan Sikap Negatif

Kemerosotan dalam pencapaian akademik dan sikap negatif pelajar pasca era COVID-19 dan PKP adalah isu yang memerlukan tindakan bersepadu dan berkesan dari pihak universiti dan pensyarah. Melalui pelaksanaan strategi-strategi yang sesuai, masalah ini dapat diatasi dengan lebih berjaya:

1. Sokongan Emosi

Universiti perlu menyedarkan pentingnya kesejahteraan mental dalam pencapaian akademik. Menyediakan akses kepada perkhidmatan kaunseling yang profesional dan sumber sokongan emosi boleh membantu pelajar mengatasi tekanan emosi. Kaunseling individu atau sesi kumpulan boleh memberi peluang kepada pelajar untuk membincangkan kebimbangan dan tekanan yang mereka alami, serta mendapatkan nasihat tentang cara menguruskan emosi dalam suasana pembelajaran yang mencabar.

2. Pembelajaran Berinteraksi

Penggunaan kaedah pembelajaran dalam talian yang berinteraksi dapat mengekalkan minat dan motivasi pelajar. Pensyarah perlu memanfaatkan platform pembelajaran dalam talian yang membolehkan interaksi langsung antara pelajar dan pensyarah, seperti perbincangan dalam talian, kajian kes, dan sesi soal jawab. Ini dapat membantu mengatasi perasaan kesunyian dan isolasi yang mungkin dialami oleh pelajar dan memberikan rasa keterlibatan yang lebih mendalam dalam pembelajaran.

3. Menggalakkan Interaksi Sosial

Walaupun dalam talian, peluang untuk interaksi sosial harus dipelihara. Mencipta platform untuk perbincangan berkelompok dan projek kolaboratif dalam talian membolehkan pelajar bekerjasama dengan rakan sebaya dan mengatasi rasa kesunyian. Inisiatif ini mendorong perkongsian idea dan pembelajaran kolektif, yang dapat memperkukuhkan pemahaman konsep dan memberi pelajar pengalaman interaksi yang bermanfaat.

4. Pemberian Sokongan Pelajaran

Pensyarah boleh menyediakan sokongan tambahan dalam bentuk sesi tutorial dan bahan pembelajaran tambahan. Ini membantu pelajar yang menghadapi kesukaran dalam memahami bahan pembelajaran dan memberikan platform bagi mereka untuk bertanya soalan dan mendapatkan penjelasan yang lebih terperinci. Sesi tutorial juga membangunkan hubungan lebih dekat antara pensyarah dan pelajar, mendorong pelajar untuk terlibat dan mengambil tanggungjawab dalam pembelajaran.

5. Mengkomunikasikan Nilai Pendidikan

Universiti perlu mengkomunikasikan nilai pendidikan kepada pelajar dan mengaitkannya dengan peluang pekerjaan di masa depan. Pengenalan kepada peranan pendidikan dalam pembentukan minda dan pemahaman dunia dapat memberikan dorongan kepada pelajar untuk menghargai proses pembelajaran dan mengaitkannya dengan matlamat jangka panjang.

6. Mengajar Pengurusan Masa

Universiti dapat menyediakan panduan mengenai pengurusan masa yang baik dan efektif. Ini termasuk amalan-amalan terbaik dalam merancang jadual pembelajaran, menguruskan tenggat masa, dan memberi keutamaan kepada tugas-tugas penting. Pelatihan ini membantu pelajar membangunkan kemahiran penting yang akan bermanfaat tidak hanya dalam konteks akademik, tetapi juga dalam kehidupan masa depan.

Kesimpulan

Pandemik COVID-19 dan PKP telah memberi impak yang signifikan kepada sikap dan pencapaian akademik pelajar universiti. Kemerosotan dalam pencapaian akademik dan sikap negatif yang diperhatikan memerlukan tindakan proaktif dari universiti, pensyarah, dan pelajar sendiri. Dengan mengambil kira faktor-faktor yang mempengaruhi dan strategi untuk mengatasinya, kita dapat memastikan bahawa kualiti pendidikan tinggi tetap terjamin dalam era pasca pandemik ini.

Suggestion for Citation:
Amerudin, S. (2023). Kemerosotan Pencapaian Akademik dan Sikap Pelajar Universiti Pasca Era COVID-19 dan Perintah Kawalan Pergerakan (PKP). [Online] Available at: https://people.utm.my/shahabuddin/?p=6633 (Accessed: 16 August 2023).
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Pengaruh Anugerah Pencapaian Cemerlang Terhadap Kecemerlangan Staf Lain dalam Konteks Organisasi

Oleh Shahabuddin Amerudin

Pemberian anugerah pencapaian cemerlang kepada staf dalam sesebuah organisasi adalah suatu bentuk pengiktirafan terhadap usaha dan pencapaian individu yang luar biasa. Namun, apabila anugerah ini hanya diberikan kepada segolongan tertentu sahaja, terdapat beberapa isu dan implikasi yang perlu dipertimbangkan dalam konteks pengaruhnya terhadap kecemerlangan staf lain di masa hadapan.

Di bawah ini adalah beberapa aspek yang perlu dipertimbangkan dalam menjawab soalan ini:

  1. Motivasi dan Inspirasi: Anugerah pencapaian cemerlang boleh menjadi sumber motivasi dan inspirasi kepada staf lain. Melihat rakan-rakan mereka diberikan pengiktirafan atas usaha yang luar biasa dapat memacu semangat dan tekad untuk meningkatkan prestasi mereka sendiri.
  2. Sikap Positif atau Ketidakpuasan: Pengaruh anugerah terhadap staf lain dapat bergantung kepada bagaimana mereka melihat pemberian anugerah ini. Mereka yang merasa bahawa pemberian anugerah adalah adil dan berdasarkan prestasi yang sebenarnya mungkin akan terus berusaha untuk mencapai kecemerlangan. Namun, jika mereka melihatnya sebagai tidak adil atau subjektif, ini boleh menyebabkan perasaan ketidakpuasan atau pengurangan motivasi.
  3. Persaingan Sihat atau Tidak Sihat: Pemberian anugerah cemerlang kepada segolongan tertentu boleh mencetuskan persaingan di kalangan staf. Persaingan yang sihat dapat memacu inovasi dan peningkatan prestasi, tetapi jika menjadi tidak sihat, ia boleh menyebabkan konflik dan pengurangan kerjasama di dalam organisasi.
  4. Pengiktirafan Alternatif: Jika hanya segelintir staf yang berjaya menerima anugerah cemerlang, organisasi perlu memastikan bahawa terdapat peluang pengiktirafan alternatif untuk staf lain yang turut berusaha dengan gigih. Ini boleh melibatkan pengiktirafan secara formal atau tidak formal, seperti penghargaan atas usaha yang konsisten, kemajuan berterusan, atau penyumbangan dalam projek-projek tertentu.
  5. Perbezaan Prestasi Individu: Setiap individu mempunyai keupayaan dan konteks prestasi yang berbeza. Adalah penting untuk mengambil kira faktor-faktor ini ketika mempertimbangkan pemberian anugerah. Keputusan pemberian anugerah seharusnya berdasarkan keadilan dan objektif, bukan sahaja prestasi mutlak.
  6. Pengurusan Prestasi: Anugerah cemerlang harus disokong oleh sistem pengurusan prestasi yang kukuh. Ini termasuk penilaian prestasi yang telus dan objektif, serta mekanisme untuk memberikan maklum balas dan peluang perkembangan kepada semua staf.
  7. Pengaruh Budaya Organisasi: Budaya organisasi memainkan peranan penting dalam bagaimana anugerah dan pengiktirafan diberikan. Jika budaya organisasi menggalakkan kerjasama, perkongsian pengetahuan, dan pertumbuhan bersama, maka pemberian anugerah akan lebih berkaitan dengan pencapaian kolektif dan tidak hanya individu tertentu.

Pada keseluruhannya, pengaruh anugerah pencapaian cemerlang kepada staf yang hanya diberikan kepada segolongan tertentu dapat mempengaruhi kecemerlangan staf lain di masa hadapan. Penting untuk menjaga keseimbangan antara memberikan pengiktirafan atas prestasi individu dan memastikan bahawa semua staf merasa dihargai dan diberi peluang untuk berkembang. Keadilan, transparensi, dan sikap positif dalam budaya organisasi adalah aspek-aspek penting yang perlu ditekankan untuk menjaga kecemerlangan dan motivasi staf secara keseluruhan.

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Developing Web Map-Based Applications

By Shahabuddin Amerudin

Introduction

Web map-based applications have transformed how we interact with geographic information, enabling us to explore, analyze, and visualize data on interactive maps. The development of such applications involves a unique set of challenges and considerations, ranging from selecting mapping libraries to optimizing performance for diverse devices. This article delves into the technical intricacies of creating web map-based applications, discussing mapping libraries, geospatial data integration, user experience, and optimization techniques.

Choosing Between the Libraries

Selecting the right mapping library is crucial for building effective web map-based applications. Two of the most prominent options are Leaflet and Google Maps API.

1. Leaflet

Leaflet is a popular open-source JavaScript library for building interactive maps. Its simplicity and flexibility have made it a go-to choice for developers working on web map-based applications. Here’s a closer look at its features and advantages:

  • Lightweight and Fast: Leaflet is designed to be lightweight, making it ideal for projects where performance is crucial. Its modular nature allows developers to include only the components they need, optimizing load times.
  • Customizable Map Styles: Leaflet provides various map tile providers that offer different map styles, such as street maps, satellite imagery, and topographic maps. Developers can easily switch between these styles or even use their custom map tiles.
  • Markers and Popups: Adding markers and popups to the map is straightforward with Leaflet. Markers can be used to indicate specific locations on the map, while popups can display additional information when users interact with these markers.
  • Third-Party Plugins: Leaflet has a vibrant ecosystem of third-party plugins that extend its functionality. These plugins cover a wide range of features, such as heatmaps, clustering, routing, and more. This allows developers to enhance their maps with advanced capabilities without reinventing the wheel.
  • Integration with Data Sources: Leaflet can integrate with various data sources, including GeoJSON files, web services, and APIs. This enables developers to overlay geographic data onto their maps and create compelling visualizations.

2. Google Maps API

Google Maps API is a comprehensive set of tools and services provided by Google for integrating maps and geospatial data into web applications. While powerful, it does come with some complexities:

  • Geospatial Capabilities: Google Maps API offers robust geospatial capabilities, including street view, geocoding (converting addresses to geographic coordinates), and routing. It’s particularly useful for applications that require accurate geolocation services.
  • Extensive Documentation: Google provides thorough documentation, guides, and tutorials for developers working with their API. This resource-rich environment can be extremely helpful for those new to geospatial development.
  • Embedding Maps: With Google Maps API, developers can embed interactive maps into their applications, allowing users to explore locations, zoom in and out, and even switch between map styles like terrain, satellite, and street view.
  • Custom Layers: Developers can create custom map layers using Google Maps API. This enables the overlay of additional information on top of the base map, such as weather data or traffic conditions.
  • API Key Requirement: To use Google Maps API, developers need to obtain an API key, which adds a layer of security and allows Google to track usage. While not overly complex, this additional step can be a consideration during the development process.

Choosing between Leaflet and Google Maps API depends on your project’s requirements, your team’s familiarity with each library, and your desired level of customization. If you’re looking for a lightweight and easily customizable solution, Leaflet might be the better option. On the other hand, if you need powerful geospatial capabilities, extensive documentation, and seamless integration with Google’s services, Google Maps API could be the way to go.

Both libraries have thriving communities, so finding support, tutorials, and plugins won’t be an issue. Evaluate your project’s specific needs and your team’s expertise to make an informed decision that aligns with your application’s goals and technical requirements.

Geospatial Data Integration

Geospatial data integration is a cornerstone of web map-based applications, allowing developers to visualize and interact with location-based information. GeoJSON, a widely used format for encoding geographical data structures, plays a pivotal role in this process.

GeoJSON Overview: GeoJSON is a lightweight and human-readable format that represents geographic data in JavaScript Object Notation (JSON) format. It supports various geometry types, including Point, LineString, Polygon, MultiPoint, MultiLineString, and MultiPolygon. Each geometry type corresponds to specific geographical features, such as individual points, lines, or complex polygons.

Integration with Mapping Libraries: Mapping libraries like Leaflet and Google Maps API allow developers to integrate GeoJSON data seamlessly. By creating GeoJSON-encoded data objects and feeding them into the libraries, developers can render geographic features on the map. For instance, to display a set of points representing cities on a map, developers can provide a GeoJSON structure containing these points’ coordinates and associated data.

Custom Styling and Interactivity: One of the benefits of GeoJSON integration is the ability to apply custom styling and interactivity to the map features. Developers can define different marker symbols, colors, and popups for each data point, enhancing the user experience and conveying information effectively.

Dynamic Data Sources: In addition to static GeoJSON files, web map-based applications can also integrate dynamic data sources through APIs. For instance, a real estate application could retrieve property listings in real-time from an API and display them on the map as clickable markers, linking to detailed property information.

Real-Time Data Integration: Integrating real-time data adds a layer of dynamic information to web map-based applications, enhancing their relevance and usefulness. Here are a couple of examples:

  1. Weather Data Integration: Real-time weather data can be integrated to provide users with current conditions, forecasts, and other meteorological information. OpenWeatherMap’s API, for instance, allows developers to fetch weather data for specific locations and display it on the map. This is particularly useful for travel applications, outdoor event planning, or any scenario where weather conditions impact user decisions.
  2. Traffic Data Integration: Real-time traffic data can enhance applications that involve route planning, navigation, or urban mobility. Services like HERE Traffic offer APIs that provide traffic congestion information, incidents, and suggested alternate routes. Developers can overlay this data on the map, helping users make informed decisions about their routes.

Enhancing User Experience: Integrating real-time data not only provides valuable information to users but also enriches the interactive experience. For instance, showing live traffic conditions on a map allows users to avoid congestion and find the fastest route. Similarly, displaying real-time weather information helps users plan their activities and journeys accordingly.

Considerations: When integrating real-time data, consider factors such as API availability, data freshness, and potential usage limits. Make sure to choose reputable sources that provide reliable and up-to-date data for a seamless user experience.

Geospatial data integration, particularly through formats like GeoJSON, and the incorporation of real-time data significantly enhance the value and functionality of web map-based applications. Whether you’re displaying static geographical features or dynamically updating information like weather or traffic conditions, careful integration and thoughtful presentation of data can create engaging and informative user experiences.

User Experience and Interactivity

User experience is paramount in web map-based applications. Interactivity plays a crucial role in engaging users and conveying information effectively. Here are some considerations:

1. User-Friendly Interface

An intuitive and user-friendly interface is essential for keeping users engaged with your web map-based application. Here’s how to design an interface that enhances user experience:

  • Clear Navigation: Ensure that users can easily navigate the map and access different features. Use familiar icons for zooming, panning, and toggling map layers.
  • Consistent Design: Maintain a consistent design language throughout the application. Use colors, typography, and layout that align with your brand and offer a cohesive visual experience.
  • Responsive Design: Ensure that the application is responsive and works well on various devices, including smartphones, tablets, and desktops. A responsive design adapts the layout and elements to different screen sizes, providing a seamless experience for users.

2. Markers and Popups

Markers and popups are essential tools for conveying information and enhancing interactivity in web map-based applications:

  • Markers: Use markers to pinpoint specific locations, points of interest, or important areas on the map. For example, in a tourism application, markers can indicate tourist attractions, hotels, and restaurants.
  • Popups: When users click on a marker, display a popup that provides additional information. This information could include details about the location, images, descriptions, and links. For instance, clicking on a restaurant marker could open a popup with the restaurant’s name, cuisine type, and a link to its website.

3. User Input and Customisation

Empowering users to customize their map experience enhances engagement and makes the application more user-centric:

  • Search Bars and Filters: Incorporate search bars or filters that allow users to refine the displayed data based on their preferences. For example, in a real estate application, users could use filters to narrow down properties by price range, number of bedrooms, or location.
  • Geocoding Services: Integrate geocoding services to convert user-provided addresses or location names into geographic coordinates. This feature helps users quickly find and visualize specific locations on the map.
  • Customization Options: Provide users with options to customize map elements such as map styles, colors, and overlays. This customization allows users to tailor the map to their preferences and needs.

Examples:

  • Travel Planner Application: Imagine a travel planner application that enables users to explore different travel destinations. The interface offers intuitive zoom and pan controls, making it easy for users to navigate the map. When users click on markers representing landmarks, popups display detailed information about each landmark, including historical facts, images, and opening hours.
  • Real Estate Finder: In a real estate application, users can search for properties by entering an address or a city. Geocoding services convert their input into geographic coordinates, placing a marker on the map at the specified location. Users can then apply filters to narrow down properties by price, number of bedrooms, and property type. Clicking on a property marker opens a popup with property details, photos, and contact information.

User experience and interactivity are pivotal aspects of web map-based applications. A user-friendly interface, markers, popups, user input elements, and customization options collectively enhance the application’s usability and engagement. By designing an intuitive interface, providing informative markers and popups, and enabling users to interact with and personalize the map, you create a compelling experience that keeps users engaged and empowers them to explore geographic data with ease.

Performance Optimization

Optimizing performance is crucial to ensure that your web map-based application runs smoothly across various devices and network conditions.

1. Data Caching

Caching is a strategy that involves storing frequently accessed data in a temporary storage location to reduce the need to fetch it from external sources repeatedly. In web map-based applications, caching map tiles and geospatial data is crucial for enhancing performance:

How It Works

  • When a user accesses the application, the map tiles and geospatial data are initially fetched from the server.
  • These fetched resources are then stored in the user’s browser cache.
  • If the user revisits the application or explores different areas of the map, the cached resources can be loaded directly from the browser cache, reducing load times.

Benefits

  • Caching minimizes the number of requests to external servers, reducing latency and improving responsiveness.
  • It ensures a smoother user experience, especially in scenarios where users navigate the map frequently.

2. Minification and Compression

Minification involves removing unnecessary characters and white spaces from code files (such as JavaScript and CSS), while compression reduces file sizes by encoding them in a more efficient manner. Both techniques contribute to faster loading times:

How It Works

  • Minification removes comments, white spaces, and unused code from files, reducing their size without affecting functionality.
  • Compression uses algorithms to encode files in a way that requires fewer bytes to transmit and store.

Benefits

  • Minification and compression significantly reduce the amount of data that needs to be downloaded by users.
  • Smaller file sizes lead to faster loading times, particularly on networks with limited bandwidth.

3. Responsive Design

Responsive design is the practice of designing web applications to adapt seamlessly to different screen sizes and devices, ensuring a consistent experience for users regardless of how they access the application:

How It Works

  • The layout, fonts, images, and other elements of the application are designed to respond and adjust based on the screen size.
  • Media queries are used in CSS to apply specific styles for different screen widths, ensuring that the application remains usable and visually appealing on various devices.

Benefits

  • A responsive design eliminates the need for users to zoom in or scroll horizontally, improving the overall usability of the application.
  • It ensures that the application functions well on smartphones, tablets, laptops, and desktops, enhancing accessibility and user satisfaction.

4. Lazy Loading

Lazy loading is a technique that delays the loading of certain resources until they are actually needed, improving initial loading times and conserving bandwidth:

How It Works

  • In web map-based applications, layers and assets that are not immediately visible when the application loads can be loaded lazily.
  • As the user interacts with the map and navigates to different areas, additional layers and assets are loaded on demand.

Benefits

  • Lazy loading reduces the initial load time of the application, allowing users to access the basic functionality quickly.
  • It optimizes resource usage, as only the resources required for the current view are fetched, conserving bandwidth.

Examples

  • Travel Guide Application: A responsive travel guide application displays an interactive map of a city’s landmarks. The application’s layout adapts based on the user’s device, ensuring a seamless experience on smartphones, tablets, and desktops. The map layers and assets are loaded lazily, ensuring that the application loads quickly, even on slower connections. Additionally, the map tiles and geospatial data are cached in the user’s browser, enhancing performance when the user explores different parts of the city.
  • Real-Time Traffic Application: In a real-time traffic application, markers indicate traffic incidents on the map. The application uses minification and compression techniques to reduce the size of JavaScript and CSS files, resulting in faster loading times. As users navigate the map to find alternative routes, the application dynamically fetches and displays additional traffic data while optimising performance through lazy loading.

Performance optimization is vital for delivering a smooth and responsive experience in web map-based applications. By employing techniques such as data caching, minification, compression, responsive design, and lazy loading, developers can create applications that load quickly, work well across different devices, and offer an enjoyable user experience, even in varying network conditions. These optimization techniques contribute to higher user engagement and satisfaction, ensuring that users can interact with and explore geographic data seamlessly.

Conclusion

Developing web map-based applications requires a deep understanding of mapping libraries, geospatial data integration, user experience design, and performance optimization. By selecting the appropriate mapping library, integrating geospatial data effectively, prioritizing user experience, and optimizing performance, developers can create captivating and efficient applications that empower users to explore the world through interactive maps. The world of web map-based applications is expanding rapidly, offering developers new opportunities to innovate and provide valuable spatial insights to users across various domains.

Suggestion for Citation:
Amerudin, S. (2023). Developing Web Map-Based Applications. [Online] Available at: https://people.utm.my/shahabuddin/?p=6629 (Accessed: 15 August 2023).
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Exploring Firewall Bypass Techniques: Strategies and Countermeasures

Introduction

In today’s interconnected world, network security is paramount. Firewalls serve as the frontline defense against cyber threats, safeguarding sensitive data and digital assets from unauthorized access and malicious attacks. However, determined hackers employ a variety of sophisticated techniques to bypass firewalls and compromise networks. This article delves into the methods hackers employ to breach firewall defenses, as well as strategies and countermeasures to fortify network security.

Common Firewall Bypass Techniques

  1. Exploiting Vulnerabilities: Hackers actively search for vulnerabilities within the target’s software and systems. By identifying and exploiting these vulnerabilities, they can execute arbitrary code, effectively bypassing firewall protections.
  2. Malware and Malicious Software: The deployment of malware, such as viruses, worms, Trojans, and ransomware, allows hackers to establish a foothold within a network. These malware agents can communicate with external servers, thus sidestepping firewall rules.
  3. Phishing and Social Engineering: Hackers leverage social engineering tactics to manipulate employees into divulging sensitive information. Obtaining legitimate access credentials allows hackers to circumvent firewall alerts.
  4. IP Spoofing: IP spoofing involves manipulating the source IP address of network packets to deceive packet-filtering firewalls. This technique conceals the true origin of the traffic, facilitating unauthorized access.
  5. Application Layer Attacks: Targeting application vulnerabilities that may not be thoroughly inspected by firewalls enables hackers to infiltrate a network. Exploiting these weaknesses grants them unauthorized access.
  6. Firewall Misconfigurations: Weak firewall configurations or mismanagement can expose network gaps, providing hackers an entry point into the system.
  7. Zero-Day Exploits: Hackers exploit vulnerabilities that vendors and the public are unaware of. By targeting these zero-day vulnerabilities, attackers can breach systems before patches are released.
  8. Tunneling and Encryption: Encrypted tunnels and protocols allow hackers to evade firewall analysis, as encrypted traffic may not undergo full inspection. This technique conceals malicious activity.
  9. Backdoors and Remote Access Trojans (RATs): Hackers deploy backdoors and RATs to create covert communication channels. These channels allow hackers to bypass firewalls and remotely control compromised systems.
  10. Brute Force Attacks: Persistent attempts at gaining access through repetitive login attempts with varying credentials can grant hackers unauthorized entry.
  11. Insider Threats: Compromised employee accounts or malicious insiders can exploit their privileged access to bypass firewalls and compromise network security.

Strengthening Firewall Defenses: Strategies and Countermeasures

  1. Multi-Layered Security Approach: Implement a comprehensive security strategy, integrating firewalls with intrusion detection/prevention systems, endpoint protection, and regular security updates.
  2. Regular Vulnerability Assessments: Conduct routine vulnerability assessments and penetration testing to identify and rectify software vulnerabilities that hackers might exploit.
  3. Security Awareness Training: Educate employees about social engineering risks and phishing tactics to minimize the chances of hackers obtaining legitimate credentials.
  4. Strong Authentication and Access Controls: Implement strong authentication methods such as multi-factor authentication (MFA) and enforce strict access controls to limit unauthorized access.
  5. Firewall Configuration Audit: Regularly review and audit firewall configurations to ensure proper rule management and adherence to security best practices.
  6. Intrusion Detection and Prevention Systems (IDS/IPS): Implement IDS and IPS systems to monitor network traffic, detect anomalies, and proactively respond to potential threats.
  7. Application Layer Inspection: Deploy firewalls that offer application layer inspection to identify and block traffic exploiting vulnerabilities in specific applications.
  8. Threat Intelligence Integration: Integrate threat intelligence feeds to keep firewalls updated with the latest threat information, enhancing their ability to detect emerging risks.
  9. Regular Updates and Patch Management: Keep firewalls and all network software up to date to address known vulnerabilities and protect against attacks that exploit them.
  10. Incident Response Plan: Develop a robust incident response plan that outlines steps to be taken in the event of a security breach, ensuring a swift and coordinated response.

Conclusion

Firewalls remain a critical component of network security, but the tactics employed by hackers to bypass these defenses are constantly evolving. By understanding the techniques hackers use and implementing a multi-faceted security strategy, organizations can significantly reduce the risk of breaches and unauthorized access. Regular assessment, training, vigilant monitoring, and proactive response are essential in the ongoing battle to safeguard sensitive data and maintain the integrity of networks in the face of ever-evolving cyber threats.

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GIS Applications That Demand Sub-Meter Accuracy

By Shahabuddin Amerudin

Sub-meter accuracy is essential for a variety of Geographic Information System (GIS) applications that require precise spatial data collection, mapping, and analysis. Here are some GIS applications that benefit from sub-meter accuracy:

  1. Surveying and Mapping: Sub-meter accuracy is crucial for professional land surveying, cadastral mapping, and creating accurate property boundaries. Surveyors rely on sub-meter accuracy to establish accurate control points and measurements.
  2. Utility Infrastructure Management: Managing utility networks, such as water, gas, and electric, requires accurate spatial data. Sub-meter accuracy ensures precise location and mapping of utility assets, helping with maintenance and network planning.
  3. Agriculture and Precision Farming: Sub-meter accuracy is vital for precision agriculture, where farmers use GIS to optimize planting, irrigation, and fertilization. Accurate data collection aids in crop monitoring and yield estimation.
  4. Environmental Monitoring: Sub-meter accuracy is crucial for tracking changes in the environment, such as habitat mapping, erosion monitoring, and vegetation analysis. Precise data collection helps in understanding ecological changes over time.
  5. Natural Resource Management: GIS applications related to forestry, mining, and wildlife management benefit from sub-meter accuracy. Accurate data is essential for sustainable resource utilization and monitoring.
  6. Transportation and Infrastructure Planning: Sub-meter accuracy aids in transportation planning, road design, and traffic management. It ensures accurate representation of road networks and helps in optimizing traffic flow.
  7. Emergency Response and Public Safety: GIS applications for emergency response require precise location data for incident management, evacuation planning, and resource allocation during disasters.
  8. Urban Planning: Urban planners use sub-meter accuracy to create accurate base maps, analyze land use patterns, and design infrastructure projects within urban environments.
  9. Construction and Engineering: Sub-meter accuracy is essential in construction layout and engineering design to ensure that structures are built to accurate specifications.
  10. Archaeology and Cultural Heritage: Sub-meter accuracy helps archaeologists and preservationists document historical sites and cultural heritage with precision, aiding in conservation efforts.
  11. Geological and Geophysical Analysis: Geologists use sub-meter accuracy to map geological formations, study fault lines, and analyze terrain characteristics.
  12. Remote Sensing and Imagery Analysis: Accurate ground control points obtained through sub-meter accuracy enhance the quality of remote sensing data analysis, such as satellite and aerial imagery interpretation.
  13. GIS Data Integration: Sub-meter accuracy ensures seamless integration of GIS data from different sources, enabling accurate overlay and analysis of spatial datasets.

While sub-meter accuracy is critical for these applications, it’s important to choose the appropriate level of accuracy based on the specific requirements of the project. In many cases, sub-meter accuracy provides a balance between precision and cost-effectiveness, making it a valuable asset in various GIS applications.

Suggestion for Citation:
Amerudin, S. (2023). GIS Applications That Demand Sub-Meter Accuracy. [Online] Available at: https://people.utm.my/shahabuddin/?p=6616 (Accessed: 14 August 2023).