Information Technology Journal KMUTNB

Full Issue

2025-12-30T16:08:45+07:00

A Meal Recommendation System for Major Depressive Disorder Patients

2025-12-30T16:16:21+07:00

Depression requires medications as a primary treatment to help the patient recover from the disease or alleviate it and can lead a normal life. Unfortunately, medications used in the treatment of depression can affect the patient's body. Some patients do not take the medication continuously until their symptoms of the disease relapse, which can lead to suicide. Therefore, to improve treatment outcomes, integrating nutritional principles into the therapeutic approach becomes imperative. The purpose of this research was to design and develop a meal recommendation system for major depressive disorder patients with malnutrition that takes into account the side effects of medications, physical symptoms, and chronic non-communicable diseases. This system ensures that patients receive the proper daily intake of nutrients based on their body mass index. Moreover, the system could help healthcare professionals access specialist meal recommendations that promote effective patient care without losing the opportunity for treatment. The evaluation results indicated that the overall system usability is at the highest level ( = 2.35), while the Efficiency and Helpfulness aspects are at the average level. Considering each aspect of the system usability assessment reveals that the outstanding aspects of the system are the Learnability ( = 2.52) aspect, which achieves the highest level among the five aspects, followed by the Control ( = 2.48) and Affect ( = 2.34) aspects.

Classification Model Comparison for Predicting Professional Fields

2026-01-06T16:19:26+07:00

Currently, peoples who study at the Institute of Digital Arts and Science, Suranaree University of Technology, have to select one of the professional fields between Digital Technology and Digital Communication. The professional field was very important because it will have a direct effect on course modules to be studied and a future career path. The purpose of this research was to construct and compare the classification performance of professional fields prediction model by collecting and analyzing data from the student opinion questionnaire. The classification technique which is part of a data mining approach was applied with 5 algorithms, e.g. Decision Tree, Naïve Bayes, OneR, Support Vector Machines, and K-Nearest Neighbors. The performance of the classification model was evaluated and compared by Precision, Recall, Accuracy, and F-Measure with 10-folds, 20-folds, and 30-folds cross-validation. The evaluation results indicated that (1) the classification model obtained from the Naïve Bayes algorithm achieved the highest Accuracy at 89.6%, using 20-folds cross-validation; (2) the classification model derived from the Support Vector Machines algorithm achieved the highest Precision at 89.6%, using 30-folds cross-validation; and (3) the classification model obtained from the Decision Tree algorithm achieved the highest Recall and F-measure at 83.3% and 82.5%, respectively, using 10-folds cross-validation.

The Success of Teaching Materials for Human Organ Systems Using Virtual Reality Technology

2026-01-06T16:32:17+07:00

This research aimed to 1) design and develop teaching media on the human body organ system using virtual reality technology, 2) evaluate the satisfaction of learning the human body organ system using virtual reality technology, and 3) compare the achievement of teaching with virtual reality technology with conventional teaching. The research method was a randomized controlled trial. The samples consisted of 42 fifth grade students from two schools in Suphan Buri Province and one in Surat Thani Province. They were divided into two groups: the experimental group and the control group. The results of the research found that 1) the design and development of teaching media on the human body organ system using virtual reality technology can make learners interested and learn faster. 2) The overall satisfaction of learning teaching media on the human body organ system using virtual reality technology was at a high level with a mean value of 4.45 and a standard deviation of 0.03. 3) The achievement of teaching media on the human body organ system using virtual reality technology was higher than conventional learning

The Design and Development of a Smart Digital Data Storage and Exchange Platform for the Air Force Command and Control Systems Using Blockchain Technology

2026-01-06T17:01:45+07:00

Research objectives are to design and develop a smart digital data storage and exchange platform for the Air Force’s command and control system using the blockchain. This system focuses on process in managing data and controlling operations or activities. This also includes executing decisions under rapid changing situations or in conditions that are far more complex and uncertain. All of these require a fast and secure exchange of data. Then, the research has created an architectural platform that uses blockchain to store and exchange data. Moreover, to enhance data protection capabilities and strengthen the security of data exchange, large data transmissions were tested via smart contracts. The results showed that the system can handle data of various sizes effectively. However, as the data size increases, the processing time also increases, with the largest data size of 20 MB taking an average of 1.175 seconds to process. The research result provides the Air Force platform for command and control to work with full strength and high efficiency. This will integrate new technology with the military context, transcending into defense technology, based on the concept of Thai invention aiming to be self-sufficient.

Dynamic Thai Sign Language Recognition using Recurrent Neural Network

2026-01-06T17:28:24+07:00

Sign Language is a communication using a hand gesture that can pose on head to waist along with a facial emotion. There are numerous articles attempting to recognize dynamic sign language using machine learning. However, the dynamic sign language is a temporal continuous data. In addition, the positions of the hands and facial emotion are components that contribute to the completeness of sign language communication. Therefore, a sign language recognition methodology development is still challenge. This research aims to develop a Thai sign language recognition approach using recurrence neural network (RNN). The MediaPipe library applies to landmark extraction consisting of the hands, face and posture using by coordinate (x, y, z) totally 1,662 keypoint for RNN input. After that, these keypoints are learned by RNN technique consisting of long short-term Memory (LSTM) 2) gated recurrent unit (GRU) and 3) bi-direction LSTM (BiLSTM). The dataset consists of 10 words of Thai sign Language totally 1,000 videos that are established by volunteers sign language interpreters and hearing impaired. The experiment result demonstrates that an accuracy of the proposed method at 99% by LSTM and GRU.

Exploratory Factor Analysis of Financial Modeling Chatbot Features Factor

2026-01-07T09:54:31+07:00

Financial models are essential for startups, serving as a guideline for conducting business and fundraising. However, most startups lack the requisite financial knowledge and understanding, leading to operational challenges. Chatbots have expanded their functionality across various domains, yet their application in financial modeling remains unexplored. This study seeks to investigate the elements influencing a chatbot capacity to develop financial models. A mixed research approach was utilized to collect data from a sample group of startups in Thailand. Exploratory Factor Analysis (EFA) revealed several pivotal factors essential for the development of chatbots adept at financial modeling. These include ease of use, the ability to give recommendations, and the presence of advanced features. By understanding these factors, chatbot developers can create more effective financial modeling tools. This, consequently, will benefit startup founders by offering them a variety of chatbot options to select the one that best aligns with their requirements.

Effectively Managing Service Incident Data by Implementing ISO/IEC 29110 Part 4-3: Service Delivery in Industry and Digital Technology Services

2026-01-07T11:14:43+07:00

The survey conducted by ISO/JTC1SC7WG 24 among digital industry entrepreneurs revealed that 49.1% of them face challenges in managing customer requirements [1]. To address this issue, entrepreneurs should consider adapting their customer requirements management processes according to the international software quality standard ISO/IEC 29110 Part 4-3: Service delivery [2], [3]. In this research, the study outlines the steps in managing changes that impact customer usage and evaluates the effectiveness of incident management processes in service delivery. The findings indicate that before implementing the mentioned standard in service delivery and responsiveness, satisfaction was at 40%. However, after the application of the international standard, satisfaction significantly improved to 100%.

A Design and Development of Good and Bad Cocoon Classification Models using Convolutional Neural Network and Transfer Learning

2026-01-07T13:31:06+07:00

This research aims to design and development of good and bad cocoon classification models using convolutional neural network and transfer learning. There are 3 steps: 1) data acquisition, 2) data preparation, and 3) model development. The data used was a WAV audio file, recorded using a microphone and computer program, which had experts shake out 500 good cocoons and 500 bad cocoons, for a total of 1000 files. The data was converted from audio data into images using Mel spectrogram technique, The data was converted from audio data into images using the Mel Spectrum technique into 1000 images and crop the image using 2 methods, including 1) finding the center of the image and 2) resizing the image. Using a convolutional neural network. and transfer learning in modeling. The data is divided into two parts, 800 images of training data and 200 images of test data. The results show that the convolutional neural network model has an accuracy of 100 percent, Since the images obtained after cropping the images from both methods, there are still prominent features in the center of the image and the images used for training are images of the spectrum of good and bad cocoons, reducing noise or bias in classification model. As for the MobileNetV2 transfer learning model has an accuracy of 95 percent, and the NASNetMobile has an accuracy of 94.5 percent.

A Predicting Depression Model from Social Media Images using Machine Learning Technique

2026-01-07T13:51:20+07:00

The rate of depression in Thailand has been steadily increasing, with the majority of cases going untreated. As a result, expressions of depression often appear on social media. This study aims to develop a predicting depression model from social media images using machine learning technique. Data were collected from Twitter users, including images and results from a Patient Health Questionnaire-9 (PHQ-9), totaling 1,131 images. These were categorized into four groups: 423 images of individuals without depression, 525 images of individuals with mild depression, 134 images of individuals with moderate depression, and 69 images of individuals with severe depression. A convolutional neural network was applied in the study. The experimental results showed that the predictive model for depression based on images from social networks using machine learning techniques achieved the accuracy rate is 81.16%, the precision rate is 81.88%, the recall rate is 80.81% and the overall efficiency rate is 81.02%.

Traceability System for Coffee Product for the Ban Tham Singh Coffee Community Enterprise in Chumphon Province

2026-01-08T09:12:45+07:00

This research aims to develop a product traceability system for coffee products of the Ban Tam Sing Coffee Community Enterprise in Chumphon Province, evaluate its efficiency, and assess user satisfaction. The study was conducted following a systematic system development process. The research targeted a group of 300 consumers and individuals interested in coffee products, selected through purposive sampling. The tools used in the study included: 1) a coffee product traceability system developed using PHP with MySQL as the database management system, and 2) a questionnaire to assess user satisfaction with the system. Statistical methods used for data analysis included the mean and standard deviation. The findings revealed that the overall satisfaction with the coffee product traceability system was rated at the highest level (Mean = 4.64, S.D. = 0.03). The developed system enabled consumers to easily access information about the origin and production process of individual coffee products by scanning a QR code on the packaging. The system provided production details, nutritional information, distribution data, and other relevant product information, ensuring accurate and comprehensive information for consumers. This enhanced consumer confidence in the coffee products of the Ban Tam Sing Coffee Community Enterprise in Chumphon Province. Additionally, the system demonstrated potential for adaptation and application to other community products in the future.