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This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Sakchai Tangwannawit
King Mongkut’s University of Technology North Bangkok
Computer Science & IT Electrical & Electronics Engineering Engineering

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The effect of technology-organization-environment on adoption decision of big data technology in Thailand Wanida Saetang; Sakchai Tangwannawit; Tanapon Jensuttiwetchakul
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v10i6.pp6412-6422

Abstract

Big data technology (BDT) is being actively adopted by world-leading organizations due to its expected benefits. However, most of the organizations in Thailand are still in the decision or planning stage to adopt BDT. Many challenges exist in encouraging the BDT diffusion in businesses. Thus, this study develops a research model that investigates the determinants of BDT adoption in the Thai context based on the technology-organization-environment (TOE) framework and diffusion of innovation (DOI) theory. Data were collected through an online questionnaire. Three hundred IT employees in different organizations in Thailand were used as a sample group. Structural equation modeling (SEM) was conducted to test the hypotheses. The result indicated that the research model was fitted with the empirical data with the statistics: Normed Chi-Square=1.651, GFI=0.895, AFGI=0.863, NFI=0.930, TLI=0.964, CFI=0.971, SRMR=0.0392, and RMSEA=0.046. The research model could, at 52%, explain decision to adopt BDT. Relative advantage, top management support, competitive pressure, and trading partner pressure show significant positive relation with BDT adoption, while security negatively influences BDT adoption.
Tangible user interface design for learners with different multiple intelligence Salintip Sudsanguan; Sakchai Tangwannawit; Thippaya Chintakovid
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3381-3392

Abstract

The creation of learning activities responsive to learners with different basic skills has been limited due to a classroom environment and applied technologies. The goals of this research were to develop Tang-MI, a game with a tangible user interface supporting primary school learners’ analytical skills based on the theory of multiple intelligences (MI), and to present design guidelines for a tangible user interface suitable for learners in different MI groups. In this research, the Tangible user interface for multiple intelligence (Tang-MI) was tested with thirty students initially evaluated for their multiple intelligences. The learners’ usage behavior was observed and recorded while the students performed the assigned tasks. The behavioral data were analyzed and grouped into behaviors occurring before performing the tasks, during the tasks, and after completing the tasks. Based on the learners’ usage behavior, the tangible user interface design guidelines for learners in different MI groups were proposed concerning physical equipment design, question design, interactive program design, audio design, and animated visual feedback design. These guidelines would help educators build learning games that respond to the learners’ intelligence styles and enhance students’ motivation to learn.
An optimization clustering and classification based on artificial intelligence approach for internet of things in agriculture Sakchai Tangwannawit; Panana Tangwannawit
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i1.pp201-209

Abstract

This research focused on testing with maize, economical crop grown in Phetchabun province, Thailand, by installing a total of 20 sets of internet of things (IoT) devices which consist of soil moisture sensors and temperature and humidity sensors (DHT11). Data science tools such as rapidminer studio was used for data cleansing, data imputation, clustering, and prediction. Next, these data would undergo data cleansing in order to group them to obtain optimization clustering to identify the optimum condition and amount of water required to grow the maize through k-mean technique. From the analysis, the optimization result showed 3 classes and these data were further analyzed through prediction to identify precision. By comparing several algorithms including artificial neural network (ANN), decision tree, naïve bayes, and deep learning, it was found that deep learning algorithm can provide the most accurate result at 99.6% with root mean square error (RMSE)=0.0039. The algorithm obtained was used to write function to control the automated watering system to make sure that the temperature and humidity for growing maize is at appropriate condition. By using the improved watering system, it improved the efficacy of watering system which saves more water by 13.89%
Co-Authors Panana Tangwannawit Salintip Sudsanguan Tanapon Jensuttiwetchakul Thippaya Chintakovid Wanida Saetang