https://ph01.tci-thaijo.org/index.php/IT_Journal/issue/feed Information Technology Journal KMUTNB 2025-07-16T14:40:50+07:00 Asst. Prof. Dr. Sakchai Tangwannawit sakchai.t@itd.kmutnb.ac.th Open Journal Systems <p>IT Journal KMUTNB is a biannual publication (January-June and July-December)</p> https://ph01.tci-thaijo.org/index.php/IT_Journal/article/view/262768 Full Issue 2025-06-26T16:18:00+07:00 Information Technology Journal KMUTNB itjournal@it.kmutnb.ac.th 2025-07-16T00:00:00+07:00 Copyright (c) 2025 Information Technology Journal KMUTNB https://ph01.tci-thaijo.org/index.php/IT_Journal/article/view/262769 Application of Geographic Information System for Mapping Population Exposure to Flood Hazards in Thailand 2025-06-26T16:21:20+07:00 Puvadol Doydee puvadol.d@ku.th <p>The assessment of population exposure to flood hazards in urban riverine areas is crucial to flood risk response and mitigation in Thailand. This study employed a free, open-source QGIS associated with various spatial datasets e.g. administrative boundaries, census data, built-up areas, and flood hazard. The objective was to estimate the population's exposure to flood hazards in Thailand. The analysis focused at the provincial level and estimated the population's exposure to a 25-year flood event. The findings revealed that the percentage of the Thai population exposed to riverine flood hazards ranged from zero to 99.86% and was categorized into five severity levels. Approximately 18.10 million Thai people (25.83%) dwell along rivers that are highly vulnerable to riverine floods. &nbsp;Nakhon Pathom province was the first highest risk of its population being exposed to riverine floods specifically nearby the Tha Cheen River. Concurrently, there were 8 provinces namely; 1) Nonthaburi, 2) Sing Buri, 3) Phra Nakhon Si Ayutthaya, 4) Samut Songkhram, 5) Ang Thong, 6) Pathum Thani, 7) Bangkok and 8) Samut Sakhon were determined as having the highest vulnerabilities to riverine floods, while Phangnga, Krabi and Phuket showed the lowest vulnerabilities. The findings of this study provide valuable insights for policymakers to facilitate preparedness and improve effective strategies to mitigate the flood hazards.</p> 2025-07-16T00:00:00+07:00 Copyright (c) 2025 Information Technology Journal KMUTNB https://ph01.tci-thaijo.org/index.php/IT_Journal/article/view/262770 A Comparison of Classification Methods of Hypothyroid Disease Prediction 2025-06-26T16:57:28+07:00 Kulchaya Pongsawaeng itjournal@it.kmutnb.ac.th Ausron Binmaduereh itjournal@it.kmutnb.ac.th Panuphong Jenrotphondet itjournal@it.kmutnb.ac.th Orasa Patsadu orasa.p@mail.rmutk.ac.th <p>This paper proposes a comparison of classification methods of hypothyroid disease prediction using data mining techniques. A dataset from the UCI repository with the thyroid disease dataset is used to prepare data with missing value handling, imbalance class handling, and suitable attribute selection. Then, the dataset is used to build the model by comparing the performance of classification methods such as Multilayer Perceptron, Support Vector Machine, and Decision Tree. The result shows that the Decision Tree achieves high performance with an accuracy of 99.61%, which is higher than the Multilayer Perceptron and Support Vector Machine with an accuracy of 96.46 % and 92.93%, respectively. In addition, we compared the result with state-of-the-art, which uses a similar technique to our proposed method. The result shows that our proposed method also outperforms previous research. Therefore, we decided to use Decision tree model for the prototype system development in hypothyroid disease prediction to support physicians' decision-making for diagnosis and treatment. Furthermore, this paper proposes data visualization to help users for primary risk assessment of a chance of hypothyroid disease to acknowledge risk before deciding to meet physicians using demographic information. Therefore, it will reduce the cost of medical and death rates.</p> 2025-07-16T00:00:00+07:00 Copyright (c) 2025 Information Technology Journal KMUTNB https://ph01.tci-thaijo.org/index.php/IT_Journal/article/view/262772 Chatbot Application for Learning Computer Laws Using Artificial Intelligence 2025-06-26T17:12:04+07:00 Sukuma Uamcharoen sukuma.uam@mail.pbru.ac.th <p>This research aimed to develop and evaluate a "Chatbot Application for Learning Computer Laws using Artificial Intelligence." The study involved assessing user attitudes towards this application, utilizing questions and answers derived from pertinent laws in Thailand, including the "Computer-Related Crime Act," the "Copyright Act," "Thailand's Personal Data Protection Act," the "Cybersecurity Act," and the "Patent Act." In an evaluation of the Chatbot AI performance with a sample group of 100 evaluators, the following metrics were observed: Accuracy = 0.98, Precision = 1.00, Recall = 0.97, and F1-Score = 0.98. The research outcomes encompassed the successful development of the chatbot application and the summary results of the performance from a sample data set of the Chatbot Application for Learning Computer Law using Artificial Intelligence. The sample size, comprising 244 undergraduate students from Western Rajabhat Universities, was determined using the Taro Yamane table. The universities included Kanchanaburi Rajabhat University, Nakhon Pathom Rajabhat University, Phetchaburi Rajabhat University, and Muban ChomBueng Rajabhat University. The assessment revealed that the overall of performance from a sample data set was at the highest level (X̅ = 4.59, SD. = 0.09).</p> 2025-07-16T00:00:00+07:00 Copyright (c) 2025 Information Technology Journal KMUTNB https://ph01.tci-thaijo.org/index.php/IT_Journal/article/view/262773 Utilize Novel Algorithms to Acquire, Analyze, and Extract Data from TikTok Discover Page and Education-Related Topics 2025-06-26T17:33:00+07:00 Jincheng Zhang zjc1639834588@gmail.com Thada Jantakoon thada.phd@gmail.com <p>Due to the swift advancement of research and technology, particularly in the fields of computer science and data science, individuals are progressively employing these technologies, along with others, in the realm of education. This study encompasses the development, creation, and utilization of a comprehensive range of techniques, spanning from data collecting to data analysis and mining. It introduces a novel algorithm and methodology for acquiring and refining data, as well as three innovative algorithms for data analysis and exploration. This project collects data on the topics featured on the TikTok Discover page for the purpose of doing data analysis and data mining. The research methodologies employed in this work encompass empirical research, experimental verification, algorithm design and optimization, system design, and implementation. Our study examined and extracted educational content from TikTok Discover pages. We studied the popularity of this data from various perspectives and levels. This allows users to easily and efficiently locate the specific information they are interested in for further investigation. Analysis, sentiment analysis, and potential anomalous data were discovered. The analysis and extraction of this data offer educational practitioners’ significant insights that can be utilized to inform and direct educational practice.</p> 2025-07-16T00:00:00+07:00 Copyright (c) 2025 Information Technology Journal KMUTNB https://ph01.tci-thaijo.org/index.php/IT_Journal/article/view/262774 Safeguarding Skies: Airport Cybersecurity in the Digital Age 2025-06-26T18:02:52+07:00 Suphannee Sivakorn suphannee_si@rmutto.ac.th Nuttaya Rujiratanapat itjournal@it.kmutnb.ac.th Yotsapat Ruangpaisarn itjournal@it.kmutnb.ac.th Chanond Duangpayap itjournal@it.kmutnb.ac.th Sakulchai Saramat itjournal@it.kmutnb.ac.th <p>The aviation industry faces significant vulnerabilities from both physical and cybersecurity threats, highlighting the urgent need for enhanced cybersecurity measures amid increasingly sophisticated attacks. This paper systematically reviews emerging threats at airports, analyzing real-world incidents and relevant literature while mapping risks to the MITRE ATT&amp;CK Matrix, a widely recognized knowledge base for categorizing cyberattack tactics, techniques, and procedures. This is the <em>first</em> to apply the MITRE Matrix to airport security risks, offering a novel approach to understanding and mitigating these challenges. Building on this analysis, the paper advocates for modern cybersecurity defense models, emphasizing Cybersecurity Frameworks and Zero Trust Architecture, as well as critical measures for supply chain risk management and strategies to mitigate ransomware and DoS attacks. Our analysis provides insights into vulnerabilities and actionable recommendations, serving as a comprehensive guide for aviation stakeholders to strengthen defenses against evolving cybersecurity threats.</p> 2025-07-16T00:00:00+07:00 Copyright (c) 2025 Information Technology Journal KMUTNB