Category: Project

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ChatCPT Using GPU To Do Processing

A GPU, Graphics Processing Unit is a specialized type of processor that is designed to handle the complex mathematical operations required to render images and video. However, in recent years, GPUs have also been used for other types of workloads, such as machine learning and deep learning, because they are well-suited for performing large numbers of calculations in parallel.

A GPU is composed of many small, powerful cores, which allows them to perform a large number of calculations simultaneously. This is in contrast to a CPU (Central Processing Unit) which is optimized for sequential processing and not as well-suited for parallel processing tasks.

The specific characteristics of a GPU, such as the number of cores, the clock speed, and the memory capacity can vary depending on the manufacturer and the model. High-end GPUs, such as those used in data centers, can have thousands of cores and several terabytes of memory, allowing them to perform large-scale machine learning workloads in a relatively short amount of time.

When it comes to training a language model like ChatGPT, the computational power of the GPU is a key factor in determining how quickly the model can be trained. The more powerful the GPU, the faster the model can be trained, which is important when dealing with large datasets and complex models.

The specific GPU requirements for training a language model like ChatGPT can vary depending on the size and complexity of the model, as well as the size of the dataset used to train the model. However, in general, training a large language model like GPT-3 requires powerful GPUs with high memory capacity and a large number of CUDA cores.

For example, the original GPT-3 model was trained on several powerful GPUs, including the NVIDIA A100, which has 80 CUDA cores, 40 GB of GPU memory, and a memory bandwidth of 1,555 GB/s. This allows the model to handle large amounts of data and perform complex calculations quickly.

It’s important to note that, the larger the model and the dataset, the more computational power and memory is required to train it. Additionally, if you plan to fine-tune the model on a specific task or domain, which requires less data, you may not need as powerful a GPU as when training the model from scratch.

It’s also worth mentioning that you can leverage cloud-based computing resources to train your model, which will allow you to access more powerful GPUs and a larger amount of memory, without having to invest in the hardware yourself.

The cost of training a language model like ChatGPT can vary depending on a number of factors, including the size and complexity of the model, the size of the dataset used to train the model, and the resources used to train the model.

If you choose to train the model on your own hardware, the cost will depend on the specific GPU you use and the amount of computational resources required to train the model. High-end GPUs, such as those used in data centers, can be quite expensive, with costs ranging from several thousand to tens of thousands of dollars. Additionally, you will also have to factor in the cost of electricity and cooling, as well as the cost of maintaining the hardware.

Alternatively, you can leverage cloud-based computing resources to train your model, which can be more cost-effective. Cloud providers such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure offer GPU-enabled instances that can be used to train the model. These instances are charged by the hour and the cost will depend on the specific instance type and the number of hours used.

In summary, the GPU requirements for training a language model like ChatGPT can vary depending on the size and complexity of the model and the size of the dataset used to train the model. However, in general, training a large language model like GPT-3 requires powerful GPUs with high memory capacity and a large number of CUDA cores. The cost of training a large language model like GPT-3 can be quite high, and it could be in the range of tens of thousands to hundreds of thousands of dollars, depending on the specific implementation and the resources used. However, it’s worth noting that the cost of cloud-based computing resources has been decreasing in recent years, making it more affordable for researchers and developers. It’s important to note that the cost of training a model is not only limited to the computational resources, but also includes the cost of data annotation, data preprocessing, and the expertise of the team working on the project.

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Other Tools to Develop ChatGPT

When creating a language model like ChatGPT, the programming language used, such as Python, is only one aspect of the process. In addition to the programming language, there are several other tools and technologies that are commonly used to create a language model. Here is a more detailed explanation of some of the key tools and technologies used in the creation of a language model like ChatGPT:

  1. TensorFlow and PyTorch: TensorFlow and PyTorch are open-source libraries for machine learning that are used to train and deploy neural networks. They provide a wide range of tools for building and training neural networks, including support for distributed computing and GPU acceleration. These libraries are widely used in deep learning and machine learning and have a rich community and ecosystem, so it’s easy to find tutorials and pre-trained models.

  2. Pre-trained models: Pre-trained models are pre-trained neural networks that have already been trained on a large dataset. They can be fine-tuned on a smaller dataset to adjust them to specific tasks or domains. This can save a lot of time and computational resources. Pre-trained models like GPT-3, BERT, RoBERTa, etc are widely used and available to use.

  3. Natural Language Processing libraries: NLTK, spaCy, and other natural language processing libraries provide tools for tokenization, stemming, and lemmatization. These tools are used to preprocess the text data and make it suitable for training.

  4. Data visualization tools: Matplotlib, seaborn, and other data visualization tools are used to visualize the data and the results of the model. This can help to gain insights into the performance of the model and identify any areas for improvement.

  5. Cloud-based computing resources: Cloud-based resources like AWS, GCP and Azure can be used to train the model, as it requires a lot of computational resources. These cloud providers offer GPU-enabled instances that can be used to train the model quickly and efficiently.

It’s important to note that creating a language model like ChatGPT requires a lot of expertise in machine learning and natural language processing, as well as a good understanding of the tools and technologies used in the process.

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Tools To Develop ChatGPT

Language models like ChatGPT are typically implemented using programming languages such as Python. Python is a popular choice for natural language processing tasks due to its rich ecosystem of libraries and frameworks. Some of the most commonly used libraries and frameworks for creating a language model like ChatGPT include:

  1. TensorFlow: TensorFlow is an open-source library for machine learning that is used to train and deploy neural networks. It provides a wide range of tools for building and training neural networks, including support for distributed computing and GPU acceleration.

  2. PyTorch: PyTorch is an open-source machine learning library that is similar to TensorFlow. It is popular among researchers and developers for its flexibility and ease of use.

  3. Hugging Face’s transformers: This is a library that provides pre-trained models and tools for natural language processing tasks, such as text generation, text classification and more.

  4. NLTK (Natural Language Toolkit): NLTK is a Python library that provides tools for natural language processing, including tokenization, stemming, and lemmatization.

  5. spaCy: spaCy is a library for natural language processing that provides tools for tokenization, text processing, and other common NLP tasks.

  6. Other libraries: Other libraries like pandas, numpy, matplotlib are also commonly used for data preprocessing and visualization.

It’s worth noting that ChatGPT is a multi-language model and can be fine-tuned on different languages, however it’s important to have a large dataset of the target language and fine-tune the model accordingly.

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50 potential GIS projects focusing on the creation of a GIS application for the Universiti Teknologi Malaysia campus

  1. Developing a GIS-based Campus Safety System
  2. Creating a GIS-based Energy Management System
  3. Developing a GIS-based Campus Maintenance System
  4. Building a GIS-based Transportation Management System
  5. Developing a GIS-based Campus Amenities System
  6. Creating a GIS-based Campus Event Management System
  7. Developing a GIS-based Campus Waste Management System
  8. Building a GIS-based Campus Water Management System
  9. Developing a GIS-based Campus Green Space Management System
  10. Creating a GIS-based Campus Food Services System
  11. Developing a GIS-based Campus Building Management System
  12. Building a GIS-based Campus Security System
  13. Developing a GIS-based Campus Disaster Management System
  14. Developing a GIS-based Campus Library Management System
  15. Building a GIS-based Campus Sports Facility Management System
  16. Developing a GIS-based Campus Health and Safety Management System
  17. Creating a GIS-based Campus Outdoor Space Management System
  18. Building a GIS-based Campus Emergency Management System
  19. Building a GIS-based Campus Parking Management System
  20. Developing a GIS-based Campus Bicycle Rental System
  21. Creating a GIS-based Campus Accessibility System
  22. Developing a GIS-based Campus Retail Management System
  23. Building a GIS-based Campus Environmental Monitoring System
  24. Developing a GIS-based Campus Emergency Services System
  25. Creating a GIS-based Campus Safety Audit System
  26. Building a GIS-based Campus Landscape Management System
  27. Developing a GIS-based Campus Recycling Management System
  28. Creating a GIS-based Campus Energy Conservation System
  29. Developing a GIS-based Campus Weather Monitoring System
  30. Building a GIS-based Campus Noise Pollution Management System
  31. Developing a GIS-based Campus Air Quality Monitoring System
  32. Creating a GIS-based Campus Water Conservation System
  33. Developing a GIS-based Campus Sustainable Transportation System
  34. Building a GIS-based Campus Fire Safety Management System
  35. Developing a GIS-based Campus Sewage and Waste Water Management System
  36. Creating a GIS-based Campus Wildlife Monitoring System
  37. Developing a GIS-based Campus Stormwater Management System
  38. Building a GIS-based Campus Asset Management System
  39. Developing a GIS-based Campus Building and Room Scheduling System
  40. Creating a GIS-based Campus Emergency Contact System
  41. Developing a GIS-based Campus Facilities Request System
  42. Building a GIS-based Campus Space Utilization System
  43. Developing a GIS-based Campus Event Planning and Coordination System
  44. Creating a GIS-based Campus Wayfinding and Signage System
  45. Developing a GIS-based Campus Safety Training and Education System
  46. Building a GIS-based Campus Emergency Evacuation System
  47. Developing a GIS-based Campus Resource Conservation System
  48. Creating a GIS-based Campus Land Use Planning and Zoning System
  49. Developing a GIS-based Campus Safety Inspection System
  50. Creating a GIS-based Campus Public Transit System
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UTM and Community Kg. Sg. Timun’s CSR Programme Makes Global Impact

UTM Johor Bahru, Malaysia, June 7, 2022.

The collaboration between UTM and the Kg. Sg. Timun community has attracted attention on a global scale thanks to Dr. Shahabuddin Amerudin, a renowned expert from the Geoinformation Programme at the Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM). This remarkable programme, held on June 4, 2022, united diverse stakeholders committed to preserving the environment and harnessing cutting-edge technology for sustainable outcomes.

Central to this programme were 21 students from the year 3 Bachelor of Science Geoinformatics programme who were enrolled in the SBEG3583 GIS Software System course. Drawing from their coursework and practical experience gained during this activity, these talented students played a pivotal role in developing an advanced geotagging system. Their efforts epitomise the fusion of academic learning and real-world application, contributing to the programme’s success and leaving a lasting impact on environmental conservation.

The collaboration also saw the active participation of esteemed individuals such as Prof. Ir. Dr. Mohd Fadhil Md Din, a director from Campus Sustainability UTM, and Assoc. Prof. Dr. Zulhilmi Ismail, a director from the Centre for River and Coastal Engineering UTM. Their expertise and guidance added invaluable depth to the programme, fostering a holistic approach to conservation and sustainable development.

Additionally, ARC Club UTM, an influential organisation dedicated to environmental causes, made significant contributions to the programme’s execution and success. Their unwavering commitment to environmental preservation and advocacy further amplified the impact of this collective effort.

Mr. Habibun Najar, a fervent supporter of nature and biodiversity conservation, also represented the Malaysian Nature Society (MNS). Mr. Najar’s involvement enriched the programme, bringing a wealth of knowledge and experience from MNS, a leading organisation renowned for its efforts in protecting Malaysia’s natural heritage.

Together, under the leadership of Dr. Shahabuddin Amerudin and the collaborative spirit of UTM, the Kg. Sg. Timun community, and these esteemed participants, the CSR programme has set a new benchmark in environmental conservation. By leveraging technology, academic expertise, and community engagement, the programme has created a blueprint for sustainable initiatives that transcend borders and inspire others to take action.

This extraordinary collaboration extends an invitation to individuals worldwide to join this global movement and contribute to the preservation of the mangrove forest ecosystem. By embracing this shared vision, participants can actively engage in environmental conservation, foster cross-disciplinary learning, and make a lasting impact on a global scale.

For the latest updates on this remarkable CSR programme and upcoming events, please visit our official website at https://www.kstutm.com and follow our social media channels. Together, let us forge a path towards a sustainable future and leave an indelible mark on our environment.

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Preserving Nature’s Legacy: UTM and Kg. Sg. Timun Community Unite for a Successful CSR Programme

Dr. Shahabuddin Amerudin, a distinguished lecturer from the Geoinformation Programme at the Faculty of Built Environment and Surveying, spearheaded the highly successful Corporate Social Responsibility (CSR) Programme. This collaborative initiative, in partnership with Universiti Teknologi Malaysia (UTM) and the Kg. Sg. Timun, Linggi, Negeri Sembilan community, saw active participation from 21 talented students pursuing their Bachelor Degree of Science in Geoinformatics. These students, currently enrolled in the SBEG3583 GIS Software System course, utilised their expertise to develop an innovative geotagging system based on the valuable experience gained from this programme.

Joining them were esteemed individuals such as Prof. Ir. Dr. Mohd Fadhil Md Din, a Director for Campus Sustainability at UTM, and Assoc. Prof. Dr. Zulhilmi Ismail, a Director from the Centre for River and Coastal Engineering at UTM. The ARC Club UTM, known for its unwavering commitment to environmental causes, played a vital role in the successful execution of the programme. Additionally, Mr. Habibun Najar from the Malaysian Nature Society (MNS) enriched the project with his extensive knowledge and insights into nature and biodiversity conservation.

On Saturday, June 4, 2022, we proudly celebrated the successful completion of our CSR programme, which focused on the preservation of our environment through the planting of mangrove forest tree seeds. A cornerstone of our initiative was the utilisation of the cutting-edge Mangrove Forest Tree Identification and Geotagging mobile app. This advanced application allowed us to effectively capture and record essential information about the tree seeds, ensuring meticulous management and ongoing monitoring.

Throughout the programme, we actively planted approximately 100 mangrove forest tree seeds, symbolising our unwavering dedication to environmental conservation and the restoration of the local ecosystem. By engaging in this hands-on planting process, we aimed to nurture the growth of a sustainable mangrove forest, leaving a profound legacy for generations to come.

The exceptional Mangrove Forest Tree Identification and Geotagging Mobile App played a pivotal role in our success. This state-of-the-art app facilitated precise documentation and streamlined management by enabling us to meticulously record vital information, including species and location, in an online database. Through geotagging, we ensured continuous monitoring of the trees’ growth and development, empowering us to make well-informed decisions and actively contribute to ongoing conservation efforts.

Through the collective efforts of UTM, the Kg. Sg. Timun community, and our dedicated participants, we successfully planted approximately 100 mangrove forest tree seeds. Additionally, we tagged around 100 existing mangrove trees, significantly bolstering preservation and monitoring efforts within this invaluable ecosystem.

We cordially invite you to join us in our CSR programme and make a meaningful contribution to the preservation of the mangrove forest ecosystem. By becoming part of this transformative initiative, you will actively engage in environmental conservation, gain invaluable knowledge, and make a lasting positive impact on the community.

Stay connected with us for updates on our CSR programme and upcoming events by visiting our official website at https://www.kstutm.com and following our social media channels. Together, let us embark on a journey towards a sustainable future, leaving an indelible imprint on our precious environment.

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GDM2000, GDM2000 (Rev2006), GDM2000 (Rev2009), GDM2000 (Rev2016) and GDM2020

In addressing the effects of plate tectonic motion due to natural disasters such as earthquakes on the coordinates reference system and vertical datum systems for the whole country, JUPEM has successfully established a more accurate, precise and contemporary GDM2020. This newly derived geodetic datum system is fully aligned to ITRF2014, where velocities and PSD are modelled as an intrinsic component of the kinematic/ semi-kinematic concept of the CORS coordinates.

In order to facilitate the conversion of various coordinates system in Malaysia, JUPEM has produced numerous sets of datum transformation and map projection parameters to relate the different types of coordinate system.

The parameter values relating to different coordinate reference systems are derived from standard coordinate conversion formulae, Bursa-Wolf transformation formulae and multiple regression model.

The determination of a position requires the choice of a coordinate reference system. A situation now exists whereby it is common for a user acquiring data in a coordinate system that is completely different to which the data will be ultimately required.

JUPEM2021Download
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Terms – Geographic(al) Information System, GIScience, Geomatics, Geoinformatics, Geoinformation Technology and Geospatial Technology

Common people, often, get confused with the terms Geographic(al) Information System, GIScience, Geomatics, Geoinformatics, Geoinformation Technology and Geospatial Technology. To understand the differences or similarities among them we need to fine-tune our understanding about these frequently used and interchangeable terms.

Geographic Information System (GIS) is a computer-based information system used to digitally represent and analyze the geospatial data or geographic data. The GIS has been called an ‘enabling technology’, because it offers interrelation with the wide variety of disciplines which must deal with geospatial data. Each related field provides some of the techniques which make up a GIS. Many of these related fields emphasize data collection; GIS brings them together by emphasizing integration, modelling, and analysis. GIS has many alternative names used over the years with respect to the range of applications and emphasis; e.g., land information system, AM/FM–automated mapping and facilities management, environmental information system, resources information system, planning information system, spatial data-handling system, soil information system, and so on.

However, GIS may be considered as a type of software in a computer system that allows us to handle information about the location of features or phenomena on the earth’s surface, which has all the functionalities of a conventional DBMS plus much of the functionality of a computer mapping system. But software or an information system cannot be used in a vacuum. We need proper knowledge to develop it, to use it, and to make decisions from it. From this point of view, GIS is not just an advanced type of information systems, but a combination of science and technology, which has several interrelated distinct disciplines. Some of the interrelated important disciplines are geography, cartography, remote sensing, photogrammetry, surveying, geodesy, global navigation satellite system (GNSS), statistics, operations research, computer science, mathematics, and civil engineering.

As the integrating field, GIS often claims to be a science–Geospatial Information Science or Geographic Information Science. In the strictest sense, GIS is a computer system capable of integrating, storing, editing, analyzing, sharing, and displaying geographically referenced information. In a more generic sense, GIS is a tool that allows users to create interactive queries (user defined searches), analyze the geospatial information, and edit geospatial data. Geographical Information Science (often written as GIScience) is the science underlying the applications and systems. It is closely related to GIS but is not application-specific like GIS. For instance, analysis techniques, visualisation techniques, and algorithms/scientific logics for geographical data analysis are all part of GIScience.

GIScience is very much related with the term Geoinformatics that is a shorter name for Geographic Information Technology. Geographic information (also called geoinformation) is created by manipulating geographic (or geospatial) data in a computer system. Geoinformatics is a science and technology, which develops and uses information science infrastructure to address the problems of Geosciences (another name for Earth sciences) and related branches of engineering. Prakash (2006) defined Geoinformatics as “the collection, integration, management, analysis, and presentation of geospatial data, models and knowledge that support disciplinary, multidisciplinary, interdisciplinary and transdisciplinary research and education”. The four main tasks of Geoinformatics are: (1) collection and processing of geodata (geodata is the contraction of geographic data), (2) development and management of databases of geodata, (3) analysis and modelling of geodata, and (4) development and integration of logic and computer tools and software for the first three tasks. Geoinformatics uses GeoComputation (see note below) and it is the development and use of remote sensing, GIS, and GNSS. 

According to Virrantaus and Haggrén (2000) geoinformatics is a combination of remote sensing and GIS (they used the term Geoinformation Technique (GIT) instead of GIS technology). For example spatial analysis is a field in which image processing and GIS software tools are mixed and used together. It is very good experience to realize how same functionality can be achieved by using either image processing software tool or traditional GIS analysis tool within the embrace of Geoinformatics. 

Geoinformatics is not only for the people from surveying or geography but recently more and more people from other disciplines like Computer Science, Civil Engineering, Architecture, Geology etc. want to study Geoinformatics as their minor or even as their major subject (Virrantaus and Haggrén 2000). For that reason it has been most important to develop the contents of Geoinformatics curriculum towards more scientific subject and less being related with traditional surveying and mapping. People who wish to apply RS and GIS in their own problems among landscape design, geology or software development do not want to get profound knowledge on field measurements or printing technology. Geoinformatics as a mathematically and computationally oriented subject concentrates on data modeling and management, analysis and visualization processes and algorithms, GeoComputation, spatial statistics and operations research applications, development of GIS, image interpretation and satellite mapping technology (Virrantaus and Haggrén 2000).

Geoinformatics is a subset of Geomatics (also called Geomatics Engineering). In addition to topics within the confines of Geoinformatics, Geomatics emphasizes traditional surveying and mapping. The term ‘Geomatics’ relates both to science and technology, and integrates the following more specific disciplines and technologies: geodesy, traditional surveying, GNSS and their augmentations, cartography, remote sensing, photogrammetry, and GIS. An alternative view is that geomatics is the measurement and survey component of the broader field of GISscience. Geomatics is the discipline of gathering, storing, processing, and delivering of geoinformation or spatially referenced information.

The term Geomatics is fairly young, apparently being coined by B. Dubuisson in 1969. Originally used in Canada, because it is similar in French and English, the term geomatics has been adopted by the International Organization for Standardization, the Royal Institution of Chartered Surveyors, and many other international authorities, although some (especially in the United States) have shown a preference for the term ‘Geospatial Technology’.

Geomatics (or Geospatial Technology) is all about geospatial data. Although, precise definition of geomatics is still in flux; a good definition can be given from the University of Calgary’s web page: “Geomatics Engineering is a modern discipline, which integrates acquisition, modelling, analysis, and management of spatially referenced data, i.e. data identified according to their locations. Based on the scientific framework of geodesy, it uses terrestrial, marine, airborne, and satellite-based sensors to acquire spatial and other data. It includes the process of transforming spatially referenced data from different sources into common information systems with well-defined accuracy characteristics”. Konecny (2002) said “Geomatics, composed of the disciplines of geopositioning, mapping and the management of spatially oriented data by means of computers, has recently evolved as a new discipline from the integration of surveys and mapping (geodetic engineering) curricula, merged with the subjects of remote sensing and GIS”. Geopositioning refers to identifying the real-world geographic position by means of GNSS or any other surveying technique.

A number of University Departments which were once titled Surveying, Survey Engineering or Topographic Science, have re-titled themselves as Geomatics or Geomatics Engineering. According to Konecny (2002), geomatics has originated from surveying, mapping, and geodesy. Earlier, in higher education, the specialization was possible in one field such as geodesy or photogrammetry, but a comprehensive orientation toward surveying and mapping was lacking. Since about 1960 a technological revolution has taken place in surveying and mapping technology: angular surveys have been augmented by electronic distance measurement, and more recently by GNSS. Digital computers were able to statistically analyze huge measurement sets. Photogrammetry has become an analytical discipline, competing in accuracy with ground surveys. Earth observation by satellites has made remote sensing an indispensable tool. Cartography relying on tedious graphic work has made way to computer graphics. GIS has permitted to organize spatially oriented data in databases for the management of global, regional and local problems. The need for sustainable development has recently made obvious, that spatially referenced data constitute a needed infrastructure (spatial data infrastructure), to which all governments subscribe. Surveying and mapping curricula have traditionally provided the vision for the provision, updating, management and dissemination of spatially referenced data. However, there was a need to upgrade the curriculum orientation to modern tools and to society’s requirements. This is the reason why many programs have changed their name to ‘Geomatics’.

NOTE
GeoComputation is an emergent paradigm (class of elements with similarities) for multidisciplinary/interdisciplinary research that enables the exploration of previously insolvable, extraordinarily intricate problems in geographic context. Some people see GeoComputation as an incremental development rather than something entirely new. Several doubt that GeoComputation will make any real contribution to the sciences. Others view GeoComputation as a follow-on revolution to GIS. Openshaw (2000) argues GeoComputation is not just using computational techniques to solve spatial problems, but rather a completely new way of doing science in a geographical context.


References
Konecny, G. (2002). Recent global changes in geomatics education. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXIV, Part 6, pp. 9-14.
Openshaw, S. (2000). GeoComputation. In: S. Openshaw and R.J. Abrahart (eds.), GeoComputation, Taylor & Francis, New York, pp. 1-31.
Prakash, A. (2006). Introducing Geoinformatics for Earth System Science Education. Journal of Geoscience Education. URL: http://findarticles.com/p/articles/mi_q … _n17190422
University of Calgary’s web page: http://www.geomatics.ucalgary.ca/about/whatis
Virrantaus, K. and Haggrén, H. (2000). Curriculum of Geoinformatics — Integration of Remote Sensing and Geographical Information Technology. International Archives of Photogrammetry and Remote Sensing, Vol. XXXIII, Part B6, pp. 288-294.

Source: http://basudebbhatta.blogspot.com/2010/02/geographical-information-system.html

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Integrasi Data Geospatial Johor dengan Infrastruktur MyGDI

MyGDI

Oleh Shahabuddin Amerudin

Pengenalan

Pembangunan geospatial di Malaysia kini semakin pesat, seiring dengan perkembangan teknologi dan keperluan untuk pengurusan maklumat yang lebih cekap di pelbagai peringkat. Negeri Johor, sebagai salah satu negeri utama di Malaysia, perlu mengambil langkah proaktif dalam mengintegrasikan data geospatial yang dikumpulkan dengan sistem yang telah ditetapkan oleh Pusat Geospatial Negara (PGN). Ini bukan sahaja memastikan data yang dikumpulkan adalah seragam dan boleh diakses oleh pelbagai agensi, tetapi juga membolehkan Johor untuk memanfaatkan infrastruktur yang telah sedia ada, seperti MyGDI (Infrastruktur Data Geospatial Negara), yang dibangunkan oleh PGN. Artikel ini membincangkan bagaimana pelan induk geospatial negeri Johor boleh diselaraskan dengan standard dan infrastruktur MyGDI, serta kepentingan pematuhan kepada garis panduan yang telah ditetapkan.

Pematuhan kepada Standard MyGDI

Pematuhan kepada standard MyGDI merupakan elemen asas dalam memastikan data geospatial yang dikumpulkan adalah berkualiti tinggi dan sesuai untuk digunakan dalam pelbagai aplikasi. MyGDI menetapkan pelbagai standard dan garis panduan teknikal yang perlu diikuti oleh agensi-agensi yang terlibat dalam pengumpulan, penyimpanan, dan perkongsian data geospatial. Salah satu aspek penting dalam pematuhan ini adalah pengurusan metadata. Metadata berfungsi sebagai deskripsi data geospatial, yang merangkumi maklumat seperti tarikh pengumpulan, sumber data, skala, dan ketepatan. Dalam konteks Pelan Induk Geospatial Negeri Johor, setiap set data yang dikumpulkan mesti disertakan dengan metadata yang lengkap dan mengikut format yang ditetapkan, seperti ISO 19115. Ini akan memudahkan pengguna lain untuk memahami konteks data dan menggunakannya dengan betul.

Selain daripada metadata, format data juga memainkan peranan penting dalam pematuhan kepada standard MyGDI. Data geospatial perlu disimpan dalam format yang serasi dengan sistem yang digunakan oleh MyGDI, seperti Shapefile, GeoJSON, atau GML. Format-format ini dipilih kerana mereka menawarkan fleksibiliti tinggi dan keserasian yang luas dengan pelbagai perisian GIS yang digunakan di peringkat global. Dengan menggunakan format yang standard, negeri Johor dapat memastikan data yang dikumpulkan dapat diintegrasikan dengan mudah ke dalam pangkalan data MyGDI tanpa mengorbankan kualiti atau struktur data asal.

Selain itu, aspek privasi dan keselamatan data juga tidak boleh diabaikan. Data geospatial mungkin mengandungi maklumat yang sensitif atau peribadi, dan oleh itu, ia perlu dilindungi mengikut peraturan yang ditetapkan oleh MyGDI dan undang-undang seperti Akta Perlindungan Data Peribadi 2010 (PDPA). Ini termasuk penyulitan data, kawalan akses yang ketat, dan langkah-langkah keselamatan lain yang dapat memastikan data tersebut tidak terdedah kepada pihak yang tidak bertanggungjawab.

Penggunaan Infrastruktur MyGDI

Selain pematuhan kepada standard MyGDI, satu lagi komponen penting dalam pelaksanaan Pelan Induk Geospatial Negeri Johor adalah penggunaan infrastruktur MyGDI. Infrastruktur ini, yang dibangunkan oleh Pusat Geospatial Negara, menyediakan platform untuk integrasi dan perkongsian data geospatial di seluruh negara. Penggunaan infrastruktur ini akan membolehkan Johor untuk menggabungkan data geospatial yang dikumpulkan dengan pangkalan data nasional, sekali gus memudahkan perkongsian data antara agensi negeri dan nasional.

Langkah pertama dalam penggunaan infrastruktur MyGDI adalah penghantaran dan penyelarasan data. Data geospatial yang dikumpulkan di peringkat negeri Johor perlu dihantar ke MyGDI untuk diselaraskan dengan pangkalan data nasional. Ini termasuk pelbagai jenis data seperti peta topografi, data tanah, data kemudahan awam, dan lain-lain yang relevan dengan perancangan dan pembangunan negeri. Proses ini memerlukan penyelarasan yang rapi antara agensi negeri dan PGN untuk memastikan bahawa data yang dihantar adalah tepat, terkini, dan memenuhi standard yang ditetapkan.

Setelah data dihantar dan diselaraskan, agensi-agensi di negeri Johor akan mendapat akses kepada data geospatial nasional melalui portal MyGeoportal. Akses ini adalah kritikal untuk membuat keputusan yang lebih informatif, terutamanya dalam perancangan pembangunan yang melibatkan analisis merentas sempadan atau memerlukan maklumat dari negeri lain. Sebagai contoh, dalam merancang jaringan pengangkutan yang menghubungkan Johor dengan negeri-negeri bersebelahan, akses kepada data geospatial dari negeri-negeri lain akan membolehkan perancangan yang lebih berkesan dan menyeluruh.

Selain itu, kemas kini dan penyelenggaraan data adalah aspek penting dalam penggunaan infrastruktur MyGDI. Data yang disimpan dalam pangkalan data MyGDI perlu dikemas kini secara berkala untuk memastikan bahawa ia sentiasa relevan dan boleh digunakan untuk analisis semasa. Ini memerlukan kerjasama berterusan antara agensi di negeri Johor dan PGN dalam memastikan bahawa data yang dikumpulkan dan disimpan adalah sentiasa terkini, tepat, dan mematuhi piawaian yang ditetapkan. Penyelenggaraan data ini adalah kritikal untuk mengelakkan penggunaan data usang yang boleh mengakibatkan kesilapan dalam perancangan dan keputusan.

Kesimpulan

Dalam usaha membangunkan Pelan Induk Geospatial Negeri Johor, pematuhan kepada standard MyGDI dan penggunaan infrastruktur MyGDI adalah elemen-elemen penting yang perlu diberi perhatian serius. Dengan mematuhi garis panduan yang ditetapkan oleh Pusat Geospatial Negara, negeri Johor dapat memastikan bahawa data geospatial yang dikumpulkan adalah berkualiti tinggi, seragam, dan boleh diintegrasikan dengan mudah ke dalam pangkalan data nasional. Penggunaan infrastruktur MyGDI pula akan membolehkan Johor untuk memanfaatkan data geospatial yang sedia ada di peringkat kebangsaan, serta memudahkan perkongsian data antara agensi negeri dan nasional. Kerjasama erat antara agensi-agensi di negeri Johor dan PGN juga adalah kunci kepada kejayaan pelaksanaan Pelan Induk Geospatial Negeri Johor. Dengan langkah-langkah yang betul, pelan ini bukan sahaja akan menyokong pembangunan negeri Johor, tetapi juga akan menyumbang kepada pembangunan geospatial yang lebih luas di peringkat kebangsaan.

Rujukan:

  • Pusat Geospatial Negara. (n.d.). MyGDI: Infrastruktur Data Geospatial Negara. Diakses dari https://www.mygeoportal.gov.my
  • Jabatan Perancangan Bandar dan Desa Semenanjung Malaysia. (2019). Manual Penyediaan Data Geospatial Bersepadu.
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Inisiatif Penubuhan Pelan Induk Geospatial Negeri Johor

Pelan Induk Geospatial Negeri Johor

Oleh Shahabuddin Amerudin

Pengenalan

Dalam era digitalisasi dan globalisasi yang pesat, teknologi geospatial telah menjadi satu keperluan asas dalam perancangan dan pembangunan negeri. Negeri Johor, yang terkenal dengan pembangunan pesat dalam sektor ekonomi, infrastruktur, dan sosial, perlu memperkenalkan sebuah Pelan Induk Geospatial yang komprehensif bagi menguruskan data geospatial dengan lebih berkesan. Pelan ini bertujuan untuk memastikan Johor mampu memanfaatkan sepenuhnya teknologi geospatial dalam menyokong pembangunan mampan, mempertingkatkan pengurusan sumber, serta meningkatkan kualiti hidup rakyatnya. Di samping itu, pelan ini juga akan memastikan data geospatial Johor diselaraskan dengan keperluan dan piawaian nasional, khususnya melalui pematuhan kepada standard MyGDI yang ditetapkan oleh Pusat Geospatial Negara (PGN).

Objektif dan Kepentingan Pelan Induk Geospatial

Objektif utama pelan ini adalah untuk memperkukuhkan infrastruktur teknologi geospatial di Johor, serta memudahkan pengurusan dan perkongsian data antara agensi kerajaan, sektor swasta, dan masyarakat umum. Pembangunan ini penting bagi memastikan segala perancangan, sama ada dalam sektor perumahan, perindustrian, atau pertanian, dapat dijalankan berdasarkan data geospatial yang tepat dan terkini. Selain itu, pelan ini juga bertujuan untuk memaksimumkan penggunaan teknologi geospatial dalam pengurusan sumber alam seperti tanah, air, dan hutan. Sebagai contoh, dengan data geospatial yang terperinci, kerajaan negeri boleh merancang penggunaan tanah dengan lebih efisien, mengelakkan pembaziran sumber, dan memastikan kelestarian alam sekitar.

Kepentingan pelan ini juga terletak pada kemampuan untuk meningkatkan kecekapan perkhidmatan awam seperti pengurusan bencana, perancangan bandar, dan penyampaian perkhidmatan kesihatan. Sebagai contoh, dalam pengurusan bencana seperti banjir, data geospatial yang tepat dapat membantu pihak berkuasa mengenal pasti kawasan yang berisiko tinggi dan merangka pelan mitigasi yang berkesan. Di samping itu, perkhidmatan pengangkutan awam juga dapat dipertingkatkan dengan menggunakan data geospatial untuk merancang laluan bas atau tren yang lebih efisien, dengan mengambil kira kepadatan penduduk dan pola perjalanan harian.

Pematuhan kepada Standard MyGDI dan Penggunaan Infrastruktur MyGDI

Salah satu elemen penting dalam pelan ini adalah pematuhan kepada standard MyGDI yang ditetapkan oleh PGN. Standard ini penting untuk memastikan data geospatial yang dikumpul oleh kerajaan negeri adalah seragam dan boleh digunakan oleh pelbagai agensi, termasuk di peringkat persekutuan. Sebagai contoh, data penggunaan tanah yang dikumpul oleh negeri Johor perlu mematuhi standard MyGDI bagi memastikan ia boleh disepadukan dengan data lain seperti data geologi atau data alam sekitar yang disimpan dalam pangkalan data MyGDI. Ini akan memudahkan perkongsian data antara agensi, serta memastikan perancangan yang lebih menyeluruh dan bersepadu dapat dilakukan.

Penggunaan Infrastruktur MyGDI juga adalah satu langkah penting dalam memastikan data geospatial Johor boleh diakses dan digunakan oleh pelbagai pihak. Infrastruktur MyGDI menyediakan platform yang membolehkan data geospatial dikongsi dan digunakan dengan lebih meluas, sama ada oleh agensi kerajaan, sektor swasta, atau masyarakat umum. Sebagai contoh, data geospatial mengenai lokasi kemudahan awam seperti hospital, sekolah, atau balai polis boleh diakses oleh pihak berkuasa tempatan atau syarikat swasta bagi tujuan perancangan atau pelaksanaan projek. Ini akan memastikan segala keputusan yang dibuat adalah berdasarkan data yang tepat dan terkini, serta mengelakkan berlakunya pertindihan atau konflik dalam penggunaan sumber.

Strategi Pelaksanaan

Pelaksanaan Pelan Induk Geospatial Negeri Johor akan dijalankan dalam beberapa fasa yang dirancang secara teliti untuk memastikan keberkesanan dan kelestarian pelan ini. Pada fasa pertama (tahun 1-2), penilaian terhadap keperluan dan kapasiti sedia ada akan dijalankan, diikuti dengan penyediaan kerangka kerja dasar yang mantap. Pada fasa ini, kerajaan negeri akan melakukan penilaian terhadap infrastruktur teknologi yang ada, serta mengenal pasti kekurangan atau jurang yang perlu ditangani. Pada masa yang sama, kerangka kerja dasar akan disediakan untuk memastikan segala aktiviti yang dijalankan adalah selaras dengan visi dan misi pelan ini.

Fasa kedua (tahun 3-5) melibatkan pengumpulan dan pemprosesan data geospatial yang lebih terperinci. Data ini akan meliputi pelbagai aspek seperti penggunaan tanah, perancangan bandar, infrastruktur, dan sumber alam. Data yang dikumpul juga akan diselaraskan dengan standard MyGDI dan diintegrasikan ke dalam pangkalan data MyGDI untuk memastikan ia boleh dikongsi dan digunakan oleh pelbagai pihak. Selain itu, pada fasa ini juga, kerajaan negeri akan membangunkan infrastruktur teknologi seperti pusat data geospatial yang canggih, serta menyediakan latihan kepada tenaga kerja yang terlibat.

Fasa ketiga (tahun 6-10) akan memberi tumpuan kepada peningkatan dan inovasi dalam penggunaan teknologi geospatial. Pada fasa ini, pelbagai aplikasi geospatial akan dibangunkan untuk menyokong pelbagai sektor seperti pertanian, perindustrian, dan pelancongan. Sebagai contoh, aplikasi geospatial boleh digunakan untuk mengenal pasti kawasan pertanian yang berpotensi tinggi atau untuk merancang pembangunan industri yang lebih mampan. Selain itu, data geospatial juga akan sentiasa dikemas kini dan relevan untuk analisis semasa, serta inovasi dalam penggunaan data akan disokong bagi meningkatkan kualiti hidup rakyat Johor.

Cabaran dan Penyelesaian

Pelaksanaan Pelan Induk Geospatial Negeri Johor tidak akan terlepas daripada pelbagai cabaran yang perlu ditangani dengan bijak. Salah satu cabaran utama adalah kekangan kewangan, di mana kos pembangunan infrastruktur dan pengumpulan data geospatial adalah tinggi. Oleh itu, kerajaan negeri perlu mencari sumber pembiayaan alternatif seperti melalui kerjasama dengan sektor swasta atau memohon bantuan pembiayaan daripada kerajaan persekutuan. Sebagai contoh, kerajaan negeri boleh menjalin kerjasama dengan syarikat swasta melalui inisiatif Public-Private Partnership (PPP) bagi membiayai projek-projek tertentu.

Selain itu, cabaran dari segi keselamatan dan privasi data juga perlu diberi perhatian serius. Data geospatial sering kali mengandungi maklumat yang sensitif, terutamanya apabila melibatkan maklumat mengenai infrastruktur kritikal atau maklumat peribadi. Oleh itu, kerajaan negeri perlu memastikan bahawa sistem keselamatan yang komprehensif dilaksanakan untuk melindungi data tersebut. Ini termasuk pematuhan kepada undang-undang seperti Akta Perlindungan Data Peribadi 2010 (PDPA), serta pelaksanaan sistem kawalan akses yang ketat bagi memastikan hanya pihak yang diberi kebenaran sahaja boleh mengakses data tersebut.

Cabaran dari segi penyelarasan antara agensi kerajaan negeri, persekutuan, serta sektor swasta juga tidak boleh diabaikan. Penyelarasan ini penting bagi memastikan segala aktiviti yang dijalankan adalah selaras dan tidak berlaku pertindihan atau konflik. Bagi menangani cabaran ini, penubuhan satu badan penyelaras yang kukuh seperti Jawatankuasa Pengurusan Geospatial Negeri adalah penting. Jawatankuasa ini akan bertanggungjawab untuk memastikan kelancaran operasi dan komunikasi antara semua pihak yang terlibat, serta memastikan segala aktiviti yang dijalankan adalah selaras dengan visi dan misi pelan ini.

Anggaran Kos dan Sumber Pembiayaan

Pelaksanaan Pelan Induk Geospatial Negeri Johor memerlukan peruntukan kewangan yang besar, dan anggaran kos keseluruhan projek adalah sekitar RM100 juta hingga RM150 juta. Kos ini merangkumi pelbagai komponen seperti pengumpulan data geospatial, pembangunan dan penyelenggaraan infrastruktur teknologi, latihan dan pembangunan kapasiti, serta kemas kini data dan inovasi. Bagi komponen pengumpulan data, anggaran kos adalah antara RM15 juta hingga RM25 juta, manakala bagi komponen pembangunan dan penyelenggaraan infrastruktur teknologi, anggaran kos adalah antara RM30 juta hingga RM50 juta. Komponen latihan dan pembangunan kapasiti memerlukan anggaran kos antara RM10 juta hingga RM15 juta, manakala bagi komponen kemas kini data dan inovasi, anggaran kos adalah sekitar RM10 juta setahun.

Bagi memastikan kelestarian projek ini, kerajaan negeri perlu mencari sumber pembiayaan yang mencukupi, sama ada melalui peruntukan bajet negeri, kerjasama dengan sektor swasta, atau bantuan pembiayaan daripada kerajaan persekutuan. Kerjasama dengan sektor swasta melalui inisiatif PPP adalah salah satu pendekatan yang boleh dipertimbangkan, di mana syarikat swasta boleh menyumbang dalam bentuk kewangan atau kepakaran teknikal, manakala kerajaan negeri menyediakan sokongan dalam bentuk dasar atau insentif. Selain itu, kerajaan negeri juga boleh memohon bantuan pembiayaan daripada kerajaan persekutuan melalui pelbagai program atau inisiatif yang disediakan oleh agensi persekutuan seperti Kementerian Kewangan atau Kementerian Sains, Teknologi, dan Inovasi.

Kesimpulan

Penubuhan Pelan Induk Geospatial Negeri Johor merupakan satu inisiatif penting yang perlu dilaksanakan dengan teliti. Pelan ini akan memastikan Johor dapat memanfaatkan teknologi geospatial secara penuh untuk memperkukuh pembangunan negeri, meningkatkan kecekapan pengurusan sumber, serta menyokong pelbagai sektor ekonomi dan perkhidmatan awam. Pematuhan kepada standard MyGDI dan penyelarasan dengan Infrastruktur Data Geospatial Negara adalah kunci kepada kejayaan pelan ini. Dengan perancangan yang teliti, pelaksanaan yang berfasa, dan kerjasama erat antara agensi-agensi yang terlibat, Johor akan dapat mencapai matlamat pembangunan yang mampan dan berdaya saing dalam jangka masa 5 hingga 10 tahun akan datang.

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Thesis, Dissertation and Project

Terminologies like thesis, dissertation and project are common in academic and research practice. Although, these terms are used synonymously by students and some faculty they have different implications. 

The word ‘dissertation’ is derived from the Latin word “dissertare” which means ‘to discuss’. Oxford Dictionary defines dissertation as ‘a long essay on a particular subject or topic especially written for a university degree or diploma’. In Merriam Webster dictionary, it is defined as “an extended usually written treatment of a subject; specifically: one submitted for a doctorate”. The Cambridge dictionary defines dissertation as “a long piece of writing on a particular subject, especially one that is done to receive a degree at college or university”. It is clear from these definitions that the emphasis in a dissertation is on a review and write up on a subject rather than the novelty of the research.

The origin of the word “thesis” comes from the Greek word “tithenai” which means “to place or to put forth”. The early Greek word “tithenai” metamorphosed into ‘thesis’ which in Greek refers “to put forth something” like a proposal. The Oxford English dictionary defines a thesis as “a long essay or dissertation involving personal research, written by a candidate for a university degree”. In Merriam Webster dictionary it is defined as a “dissertation embodying results of original research and especially substantiating a specific view”. The Cambridge dictionary defines a thesis as “a long piece of writing on a subject, especially one based on original research and done for a higher college or university degree”. In some countries, a dissertation is also referred to as a thesis. However, in contrast to dissertation thesis is an in-depth study of a topic that contributes novel information in the field of research.

The word project is derived from the Latin word “projectum” from the Latin verb “proicere” (before an action), which in turn comes from “pro” (precedence), and “iacere”(to do). Thus, the original meaning of the word “project” is to plan of something and not to the act of carrying out the plan. The Oxford English dictionary defines project as “a piece of research work undertaken by a school or college student”. In Merriam Webster dictionary, it is defined as a planned undertaking: such as a formulated piece of research. The Cambridge dictionary defines a project as “a study of a particular subject done over a period, especially by students”.

The word project is often used in the engineering field and various government plans. A research project can be a short-term (less than a year) or long-term project. A short-term research project is usually undertaken by the undergraduate students and a long-term project is usually undertaken by faculty working in research institutes. A short-term research project is an abbreviated form of the dissertation where the focus is on research methodology and not the outcome of the research. In the long-term research project, the focus is on the novelty of research in addition to the methodology like a thesis.

Source: Subhash Chandra Parija and Vikram Kate (2018). Thesis, Dissertation and Project in Thesis Writing for Master’s and Ph.D. Program. Springer Nature Singaporer Pte Ltd.

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Kunjungan dari bekas pelajar – Sdri. Azne Hazira bt. Sukor

Hari ini seorang bekas pelajar PSM saya, Sdri. Azne Hazira bt Sukor telah datang ke UTM Johor Bahru di atas urusan pengesahan dokumen dan mengambil kesempatan untuk menziarahi saya. Beliau sekarang bekerja di Perunding Ukur DC di Subang dan sebelum itu berkhidmat di Geoinfo Services, Taman Melawati, Kuala Lumpur selepas sahaja tamat pengajian di dalam program Sarjana Muda Sains (Geoinformatik).

Pada sesi pengajian 2018/2019 beliau telah berjaya menyiapkan sebuah thesis Projek Sarjana Muda bertajuk “Determination of Potential Water Pipeline Bursting using Stochastic Approach in Geographical Information System”. Di dalam projek PSM tersebut beliau telah mendapat kerjasama daripada Pejabat Harta Bina (PHB) bagi membekalkan data awalan dan Sekolah Kejuruteraan Awam, Fakulti Kejuruteraan bagi khidmat nasihat tentang proses pengagihan bekalan paip air di kawasan UTM.

Selamat maju jaya diucapkan kepada beliau.

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Development of Web-Based Application for Shapefile Coordinate System Conversion for Malaysia

By Elysonia Alim
Supervised by Dr. Shahabuddin Amerudin
UTM Undergraduate Thesis Year 2018
It is difficult to process GIS vector data when they are not aligned with one another. The need for different coordinate systems rose from the fact that some coordinate systems are better fitted to describe the phenomenon happening in a specific area. However, even commercial software had been proven to have questionable accuracy in coordinate system conversions. The purpose of this study is to develop a web application capable of converting the coordinate system of a GIS data format such as a shapefile for Peninsular Malaysia. The web application named Coordinate Conversion Application (CCA v1.1) was developed using Django 2.0 with Python 3.6 and is capable of 5 coordinate transformations namely WGS84 to GDM2000 (forward and backward), WGS84 to MRSO (old) (forward only), MRSO (old) to Cassini (old) (forward and backward). Results obtained were compared with existing software such as GDTS v4.01 and ArcGIS 10.3, and analysis shows that CCA v1.1 has achieved satisfactory accuracy

Source

Published in ACRS 2018

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Mesyuarat kerjasama penyelidikan bersama JPS Malaysia

21 Februari 2020. Mewakili UTM di dalam jemputan kerjasama penyelidikan bersama dengan Jabatan Pengairan dan Saliran Malaysia. Pembentangan dan perbincangan adalah dari Unit Korporat dan pelbagai unit lain di dalam JPS yang turut sama. Topik kajian penyelidikan ialah menggunakan Agent-Based Model (ABM) di dalam pengurusan banjir di Malaysia.

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Corporate Social Responsibility (CSR)

In today’s socially conscious environment, employees and customers place a premium on working for and spending their money with businesses that prioritize corporate social responsibility (CSR).

CSR is an evolving business practice that incorporates sustainable development into a company’s business model. It has a positive impact on social, economic and environmental factors.

As the use of corporate responsibility expands, it is becoming extremely important to have a socially conscious image. Consumers, employees and stakeholders are beginning to prioritize CSR when choosing a brand or company. They are holding corporations accountable for effecting social change with their business beliefs, practices and profits.

Recognizing how important socially responsible efforts are to their customers, employees and stakeholders, many companies now focus on a few broad CSR categories:

  1. Environmental efforts: One primary focus of corporate social responsibility is the environment. Businesses, regardless of size, have large carbon footprints. Any steps they can take to reduce those footprints are considered good for both the company and society.
  2. Philanthropy: Businesses can practice social responsibility by donating money, products or services to social causes and nonprofits. Larger companies tend to have a lot of resources that can benefit charities and local community programs. It is best to consult with these organizations about their specific needs before donating.
  3. Ethical labour practices: By treating employees fairly and ethically, companies can demonstrate their social responsibility. This is especially true of businesses that operate in international locations with labour laws that differ from those in the United States.
  4. Volunteering: Attending volunteer events says a lot about a company’s sincerity. By doing good deeds without expecting anything in return, companies can express their concern for specific issues and commitment to certain organizations.

Main Source: https://www.businessnewsdaily.com/4679-corporate-social-responsibility.html