Atchariyachanvanich, Kanokwan

Preferred name

Main Affiliation

School of Information Technology

kanokwan.at@kmitl.ac.th

Now showing 1 - 5 of 5

Some of the metrics are blocked by your
consent settings
Innovative Mobile Application for Measuring Big Data Maturity: Case of SMEs in Thailand
(2020-01-01)
Limpeeticharoenchot, Santisook
;
Cooharojananone, Nagul
;
Chavarnakul, Thira
;
Tuaycharoen, Nuengwong
;
Atchariyachanvanich, Kanokwan
A Big Data maturity model (BDMM) is one of the key tools for Big Data assessment and monitoring, and a guideline for maximizing the usage and opportunity of Big Data in organizations. The development of a BDMM for SMEs is a new concept and is challenging in terms of development, application, and adoption. This article aims to create the novel online adaptive BDMM via responsive web application for SMEs. We develop the BDMM API and a responsive web application for easy access via mobile phone. We developed a model by analyzing the factors impacting the success of implementing Big Data Analytics (BDA) in SMEs based on literature reviews. The model was verified by conducting a survey of 180 SMEs in Thailand, interviewed against four extracted domains. Then, the scoring and classified levels for the model was developed through Latent Class Analysis (LCA) to depict four levels of each domain and four final maturity levels to create an adaptive model. As the experimental results with 33 users including executive officers, managers, IT, and data analytic officers. The user acceptance for our mobile application using TAM indicates that executive officer's group and non-executive group satisfied perceived usefulness, perceived ease of use, and intention to use factor. Use cases of the application include SMEs monitoring for their Big Data Analytics capability for improvement, and the Government Agency providing proper support on SMEs’ level of competency.
Some of the metrics are blocked by your
consent settings
Determinants of Personal Health Information Disclosure: A Case of Mobile Application
(2018-01-01)
Atchariyachanvanich, Kanokwan
;
Mitinunwong, Nichaporn
;
Tamthong, Butsaraporn
;
Sonehara, Noboru
This study explored the factors that affect personal health information (PHI) disclosure via a mobile application (app) in Thailand. Since mobile apps are increasingly popular, as is the Thai people's concern on their health condition, many mobile app service providers want to know which factors would persuade customers to reveal their PHI via mobile apps. This research model was, therefore, developed and included the six factors of: personalized service, self-presentation, mobile app reputation, familiarity, perceived benefits and privacy concerns. The hypotheses were tested by structural equation modeling using the questionnaire responses from 294 valid subjects. Surprisingly, privacy concern was not significantly negatively related to the intention to disclose PHI. However, the significance effect of the perceived benefit, personalized service and self-presentation were consistent with previous studies. In addition, the respondents were willing to reveal different personal information in different situations. The implication of the result will shed light on the development of a healthcare mobile app service provider.
Some of the metrics are blocked by your
consent settings
Improvement of a Machine Learning Model Using a Sentiment Analysis Algorithm to Detect Fake News: A Case Study of Health and Medical Articles on Thai Language Websites
(2024-01-01)
Atchariyachanvanich, Kanokwan
;
Saengkhunthod, Chotipong
;
Kerdnoonwong, Parischaya
;
Chanlekha, Hutchatai
;
Cooharojananone, Nagul
These days, the problem of fake news has grown to be a major social and personal concern. With the amount of information generated through social media, it is very crucial to be able to detect and properly take care of that fake information. Previous studies proposed a machine learning model to detect fake news in online Thai health and medical articles. Still, the problem of detecting fake news with similar content but different objectives exists, and the accuracy of the model needs improvement. Therefore, this study aims to solve these problems by adding 33 new features, including textual features, sentiment-based features, and lexicon features, i.e., herbs, fruits, and vegetables, to identify the objective of an article. We trained and tested the model’s prediction accuracy on a new dataset containing 582 reliable and 435 unreliable (fake news) articles from eight Thai websites. Our improved classification model using XGBoost with Lasso, the best feature selection method, achieved an accuracy of 97.76% without over-fitting, reflecting a 7.16% improvement over our earlier model.
Some of the metrics are blocked by your
consent settings
Reverse SQL question generation algorithm in the dblearn adaptive e-learning system
(2019-01-01)
Atchariyachanvanich, Kanokwan
;
Nalintippayawong, Srinual
;
Julavanich, Thanakrit
Using a traditional e-learning system, when teaching structured query language (SQL) queries in classical classrooms help instructors, to improve the students' SQL skills and learning effectiveness. However several problems in using e-learning as a teaching and learning assistant remain-such as difficulties in differences in learning ability and knowledge level. We solved these problems by applying an adaptation module to our e-learning system. However, we still found it required considerable effort to create enough exercises to make the adaptation effective enough. So, we developed a novel automatic question generating algorithm, named Reverse SQL Question Generation Algorithm (RSQLG), to automatically generate exercises (including both answer and question) from a source database. RSQLG reverses the traditional manual process used previously by instructors. Instead of creating questions and answers for them, RSQLG creates the answers first. The generated exercises are presented to students by applying question adaptation methodology based on student knowledge level in each supported learning objective. We evaluated the learning effectiveness of our approach by using outcome-based learning. After post-Test to pre-Test scores were compared, we found students using our system improved their scores by 26%. Consequently, the adaptive e-learning framework using RSQLG could be applied in any adaptive or traditional e-learning for a database course to benefit the instructors leading to less effort in exercise management and to improve the learning outcome from the students allowing as much practice as they need.
Some of the metrics are blocked by your
consent settings
Adaptive big data maturity model using latent class analysis for small and medium businesses in Thailand
(2022-11-15)
Limpeeticharoenchot, Santisook
;
Cooharojananone, Nagul
;
Chavarnakul, Thira
;
Charoenruk, Nuttirudee
;
Atchariyachanvanich, Kanokwan
Big data analytics (BDA) is widely adopted in large enterprises. However, very few small- and medium-sized enterprises (SMEs) have adopted BDA because they lack the relevant knowledge, which makes BDA development expensive and unsuitable. A big data maturity model (BDMM) is a tool for assessing the stage for using big data in a company, and it acts as a guide for improvement. However, most BDMMs are designed for large enterprises using rule-based scoring, which is static over time. Developing a suitable BDMM for SMEs is a challenging task for professionals in terms of acquiring small-scale expertise owing to the lack of case studies for verifying the maturity level. This study proposes a new BDMM for Thai SMEs and a new methodology for developing a dynamic model using latent class analysis (LCA), which explains the behaviour of each latent class and provides non-rule-based scoring. We define four types of capabilities in SMEs: organizational and attitude factors, information technology, technology, and people readiness. Data are collected from 135 SMEs in Thailand. We introduce a methodology for developing multiple building stages of the BDMM. Further, we experiment with several clusters suitable for SMEs using statistic-based and data visualization approaches. The proposed BDMM is validated via a secondary evaluation of 11 firms, nine months after the initial evaluation. Further, we introduce a web-based application for respondents to obtain their firm's assessment results. The visualization-based result helps the respondents compare their business with other companies at the same or higher maturity level. In summary, SMEs can use the proposed BDMM to plan for continuous self-improvement and thus optimize their business using BDA to maximize its value to the organization.

Atchariyachanvanich, Kanokwan

Filters

Settings

Sort By

Results per page