Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
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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)
Siriwan Kajornkasirat
Prince of Songkla University
Computer Science & IT Electrical & Electronics Engineering Engineering

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A sentiment analysis model of Agritech startup on Facebook comments using naive Bayes classifier Nawapon Kewsuwun; Siriwan Kajornkasirat
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2829-2838

Abstract

Facebook page is a tool able to generate perceptions and acceptance, and support people and investors in making business decisions. Moreover, Facebook page plays a part in engaging people in the form of a community. People share experiences and opinions toward products, services, and trends in particular periods on the Facebook page community. Regarding sentiment analysis on Facebook pages, most education and other general topics in English have only been analyzed in English. However, sentiment analysis regarding Agritech startups topics in Thai language has not been done yet. This study analyzes opinions and categorizes positive and negative comments by using naive Bayes classifier to examine the sentiments and attitudes of people and investors. The results could possibly reflect the perception rate of Agritech startups in Thailand and could be applied to explain attentiveness and assess people’s engagement opinions. Furthermore, it could be applied in studying consumer behavior, marketing analysis, spread of information, and attitudes. The study's model is generic and could be applied in other contexts to provide insightful suggestions.
Information system supporting research on rubber in Thailand Siriwan Kajornkasirat; Jirapond Muangprathub; Nathaphon Boonnam; Teerawad Sriklin
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1424-1430

Abstract

his research aimed to develop an information system supporting research on rubber (ISSR) in Thailand. This system was designed as a web application with responsive web design. The rubber database was developed with MySQL, used Apache Server as a Web Server and programed with PHP-script. High Chart, Google Chart API and Java Technology were used to represent an online information with graphical format. The system was tested with the actual data on rubber research in Southern Thailand. The system has been available online at URL http://www.s-cm.site/issr. There are three type of users: administrator, researcher (member) and generic user. The researcher performed data entry about research with log-in to the system using username and password provided by the automatic system via online registration form. The administrator can manage the research information. The researchers can manage their research information, use searching tool and leave comments on other member’s research. The generic users can access the system without username and password to view the research and general information on rubber. Moreover, the system generates a report on rubber research with online graphical format. In conclusion, this information system enhances investigation on rubber research in Thailand and its strategy planning for rubber plantation in the future.
Classification technique for real-time emotion detection using machine learning models Chanathip Sawangwong; Kritsada Puangsuwan; Nathaphon Boonnam; Siriwan Kajornkasirat; Wacharapong Srisang
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

This study aimed to explore models to identify a human by using face recognition techniques. Data were collected from Cohn-Kanade dataset composed of 398 photos having face emotion labeled with eight emotions (i.e., neutral, angry, disgusted, fearful, happy, sad, and surprised). Multi-layer perceptron (MLP), support vector machine (SVM), and random forest were used in model accuracy comparisons. Model validation and evaluation were performed using Python programming. The results on F1 scores for each class in the dataset revealed that predictive classifiers do not perform well for some classes. The support vector machine (RBF kernel) and random forest showed the highest accuracies in both datasets. The results could be used to extract and identify emotional expressions from the Cohn-Kanade dataset. Furthermore, the approach could be applied in other contexts to enhance monitoring activities or facial assessments. 
Co-Authors Chanathip Sawangwong Jirapond Muangprathub Kritsada Puangsuwan Nathaphon Boonnam Nawapon Kewsuwun Teerawad Sriklin Wacharapong Srisang