Charoenporn, Pattama

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Charoenporn, Pattama
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pattama.ch@kmitl.ac.th

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    Knowledge Discovery and Dataset for the Improvement of Digital Literacy Skills in Undergraduate Students
    (2023-07-01)
    Nilaphruek, Pongpon
    ;
    Charoenporn, Pattama
    For over two decades, scholars and practitioners have emphasized the importance of digital literacy, yet the existing datasets are insufficient for establishing learning analytics in Thailand. Learning analytics focuses on gathering and analyzing student data to optimize learning tools and activities to improve students’ learning experiences. The main problem is that the ICT skill levels of the youth are rather low in Thailand. To facilitate research in this field, this study has compiled a dataset containing information from the IC3 digital literacy certification delivered at the Rajamangala University of Technology Thanyaburi (RMUTT) in Thailand between 2016 and 2023. This dataset is unique since it includes demographic and academic records about undergraduate students. The dataset was collected and underwent a preparation process, including data cleansing, anonymization, and release. This data enables the examination of student learning outcomes, represented by a dataset containing information about 45,603 records with students’ certification assessment scores. This compiled dataset provides a rich resource for researchers studying digital literacy and learning analytics. It offers researchers the opportunity to gain valuable insights, inform evidence-based educational practices, and contribute to the ongoing efforts to improve digital literacy education in Thailand and beyond. Dataset: https://dx.doi.org/10.21227/370s-1s37 Dataset License: CC-BY 4.0
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    Performance Evaluation of Imputation Techniques for Telecommunications Customer Clustering
    (2026-01-01)
    Sukthong, Patthama
    ;
    Charoenporn, Pattama
    Missing data significantly degrades machine learning model performance in telecommunications customer analytics, leading to unreliable customer segmentation and suboptimal business decision-making. This research systematically compares seven imputation techniques across three missing mechanisms (MCAR, MAR, MNAR) and four missing rates (5%, 10%, 20%, 30%) using the Telco Customer Churn Dataset (7,043 records). Methods evaluated include traditional approaches (mean/mode, forward fill, regression), machine learning techniques (KNN, Random Forest, MICE), and deep learning (Autoencoder). We assessed model performance using normalized MAE and RMSE, and evaluated downstream effects through clustering algorithms. Results demonstrate Random Forest imputation's superior performance with MAE of 0.1568 and RMSE of 0.2123, achieving 53.7% lower error rates compared to mean/mode imputation. Statistical analysis confirmed significant performance differences (Friedman test: χ<sup>2</sup> = 55.85, p < 0.001). Interestingly, clustering performance did not directly correlate with imputation accuracy; the Autoencoder achieved the highest silhouette score (0.1510) despite moderate reconstruction accuracy. Machine learning approaches maintained robust performance across all missing data mechanisms, whereas traditional methods degraded under MNAR conditions. These findings provide evidence-based guidelines for selecting appropriate imputation techniques in telecommunications analytics, enabling improved customer segmentation and business outcomes.
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    Early Diagnosis of Knee Osteoarthritis With a Natural Language Processing–Driven Approach Based on Clinician Notes: Development and Validation Study
    (2025-01-01)
    Thanyakunsajja, Narathip
    ;
    Jitkajornwanich, Kulsawasd
    ;
    Xu, Shan
    ;
    Shin, Donghee
    ;
    Charoenporn, Pattama
    Background: Knee osteoarthritis (OA) is a common form of knee arthritis that can cause significant disability and affect a patient’s quality of life. Although this disease is chronic and irreversible, the patient’s condition can be improved and the progression of the disease can be prevented if the disease is diagnosed early and the patient receives appropriate treatment immediately. Therefore, the prediction of knee OA is considered one of the essential steps to effectively diagnose and prevent further severe OA conditions. Knee OA is commonly diagnosed by medical experts or physicians, and the diagnosis of OA is mainly based on patients’ laboratory results and medical images, including x-ray and magnetic resonance images. However, diagnosis through such data is often time-consuming. Moreover, the diagnosis results can vary among physicians depending on their expertise. Previous studies mostly focused on using approaches, such as those involving artificial intelligence, to automatically detect knee OA through such data. However, these studies did not incorporate clinicians’ or doctors’ notes (text data) into the analysis, although these data involving reported symptoms and behaviors are already available and easier to collect and access than laboratory data and image data. Objective: We propose a novel natural language processing–driven approach based on clinicians’ or doctors’ notes of patient-reported symptoms (text data only) for diagnosing knee OA. Methods: The textual information from clinicians’ or doctors’ notes was first preprocessed using text analysis algorithms with respect to natural language processing. We then incorporated deep learning models, including convolutional neural networks, bidirectional long short-term memory (BiLSTM), and gated recurrent units. Lastly, a disease-specific standard questionnaire called WOMAC (Western Ontario and McMaster Universities Arthritis Index) was taken into account to improve the overall performance of the models. Results: Our experiment included 5849 records (OA: 3455; non-OA: 2394). Before applying our WOMAC-based processing approach, the best-performing model was BiLSTM (area under the curve, 0.85; accuracy, 0.87; precision, 0.85; sensitivity, 0.95; specificity, 0.76; F<inf>1</inf>-score, 0.90), and there was an improvement in the results with BiLSTM after applying our approach (area under the curve, 0.91; accuracy, 0.91; precision, 0.91; sensitivity, 0.94; specificity, 0.87; F<inf>1</inf>-score, 0.93). Conclusions: Our proposed method for predicting the occurrence of knee OA showed better performance than other conventional methods that use image data and statistical laboratory data. The findings indicate the feasibility of using text data (symptom descriptions reported by patients and recorded by doctors) to predict knee OA. Medical notes of symptom reports can be considered a valuable data source for predicting whether a particular knee is likely to experience OA progression.
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    The Development of the Logistics Transportation Management Prototype System: A Case Study of the Economic Development along the East-West Economic Corridorin Thailand
    (2022-10-01)
    Charoenporn, Pattama
    The objective of this research is to design a logistic transportation management system prototype that can manage operations through a one-stop service. The development of the prototype will be utilizing a Saving Algorithm and Google Map to help analyze routes and determine the lowest cost. The design of the various functions will be based on samples of the transportation data of the East-West Economic Corridor. The research’s data collection will be based on data obtained from 400 transport and logistical operator samples, professionals who possess knowledge related to logistic transportation management, or academics who are well- versed in logistic operations. The collection of data was conducted using questionnaires, which were used to conduct interviews before the design of the prototype, and thereafter, to evaluate the completed logistic system design. As a result of the data collection, the researcher was then able to use the information obtained to design the interface of the logistic transportation management prototype system, which was able to comprehensively define the various functions of the prototype system. The results from testing the system based on the ISO 9241-151 principles showed that the detailed layout of the screens was rated to be Very Good by 4 persons, while 2 persons rated it as Fair. The reason that it was rated as Fair was due to the details of the proposed content was not very comprehensive as expected. As for the aspect of searching for information, it was rated as Very Good by 2 points, The display of the contents and details of the system function, such as fonts or colors, were rated to be Very Good by 3 points, and Good by 2 points. Experts commented that the system should be able to provide more details in explaining the functions of the different menus on each page or adding a navigator for other topics, such as the system processing speed. The system’s processing function was rated as Good by 3 points and Fair by 2 points.Finally, the results show that, the prototype has presented guidelines for applying the concepts of both algorithms to work collaboratively with the logistics system.It canhelp transporter to find the route of travel as well.
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    Software testing system development based on ISO 29119
    (2021-05-01)
    Raksawat, Chadatarn
    ;
    Charoenporn, Pattama
    The software testing process is an important method in various fields. Every task in software process must be tested before delivery to the customer. So, in the software field, a testing process is necessary for create application. Today, many researchers find a testing methodology from a software testing standard that will ensure many people around the world. This paper chooses the ISO 29119 standard to create a prototype for the testing process. It is suitable for small business and guides developer to generate their software. The result of paper is shown step to test software and creates document to compare results between an actual result from user and an expected result from ISO 29119 standard. Finally, the system is tested with black box methods and evaluated by the specialist that test user satisfaction survey. The results appear a satisfaction average value at 78.4%.
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    A conceptual modelling of QOS' web service framework
    (2018-01-01)
    Charoenporn, Pattama
    The evolution of web service technologies is well known in many organizations. When many organizations want to develop our business, they will add new services in their businesses policy, these additions may help the organization to increase some policy but it is difficult to implement some requirements. To solve this problem, we present the new paradigm of QOS for develop web service that is the procedure to implement web service and help developer to create service appropriate each business process. This conceptual of modelling based on LSS and BWW framework and under business practice guideline the example is Cobit and ITIL for approximate each of business process. In success, we illustrate our approach by class diagram that design under conceptual of modelling quality of service for web service and implement web service for test this idea. Then we choose 2 scenarios in different combination of QoS requirements for test, the results show that this technique is satisfy for choose and we think this paper can be guideline for researcher in the future.