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All Journal Indonesian Journal of Science and Technology Jurnal CoreIT Buletin Ilmiah Sarjana Teknik Elektro IJIIS: International Journal of Informatics and Information Systems Journal of Applied Data Sciences International Journal of Robotics and Control Systems Munaddhomah: Jurnal Manajemen Pendidikan Islam International Journal for Applied Information Management Indonesian Journal of Teaching in Science Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse
Sangsawang, Thosporn
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Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Mathematics Social Sciences Other

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Blended Teaching Model Optimization for Innovation and Entrepreneurship Courses through Data Analytics in Higher Education Yang, Liu; Sangsawang, Thosporn; Thepnuan, Naruemon; Chankham, Nawaphas; Kulnattarawong, Thidarat
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.734

Abstract

This study aimed to (1) develop a blended teaching model for Innovation and Entrepreneurship courses in Chinese higher education, and (2) assess the effectiveness of the proposed model. The sample consisted of 17 Chinese experts selected through purposive sampling and 30 higher education students from China. The research employed statistical analysis techniques including mean, standard deviation, coefficient of variation, and t-test to analyze the data. Results demonstrated significant improvements in students' entrepreneurship skills. In the experimental group, the pre-test mean score increased from 2.21 to 3.78 post-intervention, while the control group showed a slight improvement from 2.32 to 2.84. The standard deviation of learning outcomes decreased from 0.884 to 0.564, indicating a more consistent student performance. A statistically significant difference was observed (p = 0.003), confirming the effectiveness of the blended teaching model. These findings highlight the potential of blended learning in enhancing the quality of innovation and entrepreneurship education.
Development of a Self-Identity Construction Model for Private Vocational College Students Using Data Science Techniques Chen, Mei; Sangsawang, Thosporn
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.731

Abstract

This study aimed to synthesize theories of self-identity learning to develop a self-identity development model for private vocational college students in Yunnan Province, China, identify key influencing factors, and evaluate the model's effectiveness. Using purposive sampling, the study involved 17 experts and 1,004 first-year students. Data were collected through a semi-structured questionnaire via Delphi Technique, supported by consultations via email, WeChat video, and in-person interviews. The model’s validity was assessed based on satisfaction levels from students, teachers, and stakeholders. Statistical analyses included weight calculations, means, standard deviations, coefficients of variation, and path analysis. The results showed strong expert consensus, with an average score of M = 4.5008 and CV = 0.1181, forming a model of 27 first-level and 21 second-level indicators. The "career development expectation evaluation" held the highest weight at 26.86% in the initial assessment, while "dynamic feedback loop development" recorded the highest importance at 0.442 in the practical development phase. Practical testing demonstrated significant effectiveness, with satisfaction means ranging from M = 4.059 to 4.341. Regression analysis confirmed significant mutual influences among the model's five modules. Overall, the model effectively addresses the urgent need for personalized development strategies for private vocational college students in Yunnan Province.
Predicting AI Service Focus in Companies Using Machine Learning: A Data Mining Approach with Random Forest and Support Vector Machine Sangsawang, Thosporn; Tang, Lin; Pasawano, Tiamyod
International Journal for Applied Information Management Vol. 4 No. 2 (2024): Regular Issue: July 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i2.83

Abstract

This study investigates the prediction of AI service focus in companies using machine learning models. The primary objective is to predict the percentage of AI service focus based on company characteristics such as project size, hourly rate, number of employees, and geographical location. Two machine learning models, Random Forest Regressor and Support Vector Regressor (SVR), were trained and evaluated to determine their effectiveness in predicting AI adoption. The dataset consists of 3099 companies, with key features cleaned and preprocessed, including the transformation of categorical variables into numerical ones using one-hot encoding and imputation techniques applied to handle missing values. The Random Forest model demonstrated better performance, with an R² value of 0.12, indicating a modest ability to explain the variance in AI service focus. In contrast, the SVR model had a negative R² value of -0.03, suggesting that it struggled to capture the underlying relationships in the data. The analysis identified project size and hourly rate as the most significant predictors of AI service focus, with larger projects and higher hourly rates correlating with a greater emphasis on AI services. Despite the relatively low performance of both models, this research provides valuable insights into the factors that influence AI adoption. The findings emphasize the importance of project-related characteristics in determining a company's AI service focus. However, the study is limited by missing data and the absence of additional features that could further improve prediction accuracy. Future research could benefit from incorporating more business-specific features and advanced modeling techniques to enhance the predictive power and generalizability of the model.
Lecturers’ Digital Readiness in the Context of Digital Scholarchy Suwendi, Suwendi; Mesraini, Mesraini; Bakti Gama, Cipta; Rahman, Hadi; Luhuringbudi, Teguh; Sangsawang, Thosporn
Munaddhomah: Jurnal Manajemen Pendidikan Islam Vol. 6 No. 2 (2025): Progressive Management of Islamic Education
Publisher : Prodi Manajemen Pendidikan Islam Pascasarjana Institut Pesantren KH. Abdul Chalim Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31538/munaddhomah.v6i2.1674

Abstract

This study focuses on the digital readiness of lecturers at Islamic Religious Colleges (PTKI) in Jakarta and Banten in the context of Digital Scholarchy. The purpose of the study is twofold: firstly, to explore the attitudes and knowledge of lecturers regarding the use of digital technology, and secondly, to identify the digital literacy gaps among them. The data collection method uses questionnaires, interviews, and documentation to provide a more rigid and focused picture. The data analysis utilizes a range of approaches, including the Digital Readiness and Attitude Framework, the Technology Acceptance Model, and the Digital Literacy Framework, to uncover factors that influence the digital readiness of lecturers. The study's findings indicate that although there are positive attitudes towards technology, its understanding and practical use are still limited, resulting in a digital divide within the academic environment. The study's conclusions provide scientific contributions in the form of new understandings of the interplay between technology, culture, and psychology in the context of higher education by suggesting further research to explore the impact of socialization programs, training, and mentoring in the future. This study is the basis for formulating policies that are more adaptive and responsive to the needs of lecturers at PTKI Jakarta and Banten in this digital and disruptive era. The implications of this research highlight the need to develop training and mentoring programs to improve the digital skills of lecturers at PTKI Jakarta and Banten. These findings can also help build more adaptive policies and support changes in higher education in the digital era.
Factor Analysis on Teaching Quality Management for Art Design Students Using Data Driven Approach Junru, Chen; Sangsawang, Thosporn; Pigultong, Metee; Watkraw, Wasan
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.746

Abstract

This study aimed to improve teaching quality management for Art Design students using a data-driven approach through three objectives: (1) synthesizing key factors influencing instructional quality, (2) analyzing those factors using expert consensus, and (3) evaluating student satisfaction after applying the data-driven methodology. The Delphi Method was used to gather insights from 17 education experts, while 30 purposively selected Art Design students participated in satisfaction assessments. Data collection involved questionnaires and interviews, with analysis techniques including mean, standard deviation, Coefficient of Variation (CV), and t-tests. Cronbach’s α was 0.98, indicating high internal reliability. Results showed expert consensus on relevant teaching quality factors (M = 3.92, SD = 0.33, CV = 19.96, p = .002). Key aspects identified included instructional design, digital integration, feedback mechanisms, and curriculum alignment. Post-intervention analysis revealed significant student improvement, with average skill levels increasing from 16.12 (SD = 0.89) to 20.34 (SD = 0.566, p = .002). Student satisfaction reached 78.59%, with a mean of 3.90 (SD = 0.72, CV = 18.78). All statistical terms were properly defined and contextualized. The findings underscore the role of structured data analysis and expert-informed models in enhancing instructional strategies, aligning teaching with professional expectations, and promoting continuous improvement in Art and Design education.
Reviewing Factors of Audience Engagement in Live Streaming Subiyakto, Aang; Yudhanta, Satya; Nurmiati, Evy; Utami, Meinarini Catur; Fetrina, Elvy; Sugiarti, Yuni; Hakiem, Nashrul; Huda, Muhammad Qomarul; Sangsawang, Thosporn
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 11, No 1 (2025): June 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v11i1.36039

Abstract

Live streaming has become one of the most prominent digital phenomena in recent years, changing the way individuals interact, consume content and shop online. This study aims to identify and analyze the factors that influence audience engagement in live streaming, including audience motivation, the role of social interaction, technological features, platform distribution, and popular genres. Through a systematic literature review approach, 27 articles from relevant ScienceDirect and DOAJ databases were selected for analysis. The study results show that audience engagement is mainly influenced by parasocial interaction (PSI), entertainment-based motivation (hedonic value), and practical benefits (utilitarian value). Technologies such as live chat, gifting, and data-driven recommendation systems strengthen viewing duration and audience loyalty. In addition, platforms such as Twitch dominate the gaming and esports genres, while TikTok Live thrives in the e-commerce space. New genres such as VTubers and travel offer opportunities to attract a wider audience with avatar-based interaction approaches and virtual experiences. The research also identified gaps in the literature, including a lack of studies on local platforms, new genres, and the impact of innovative technologies such as AR/VR. This study makes an academic contribution by summarizing key findings and providing strategic guidance for platform developers, streamers and businesses in creating more engaging and effective live streaming experiences.
Designing a Data-Driven, Innovative Practical Model for Minority Dance Courses in Higher Education Institutions Zhou, Dan; Sangsawang, Thosporn; Vipahasna, Kitipoom; Prammanee, Noppadol; Watkraw, Wasan
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.768

Abstract

This study aimed to design and evaluate a data-driven, innovative practical teaching model for minority dance courses in higher education by integrating constructivist learning theory, multicultural education, and experiential learning. The objectives were threefold: (1) to develop a systematic instructional design framework, (2) to measure students' knowledge improvement before and after applying the model, and (3) to assess student satisfaction with the model, particularly regarding cultural identity, learning experience, and engagement. A total of 17 expert instructors from Chinese universities and Kunming University were selected through purposive sampling to contribute to the design process using the Delphi Method. Additionally, 402 first-year dance students participated in evaluating the model’s effectiveness. Quantitative analysis was conducted using means, standard deviations, coefficients of variation, and t-tests. The experts' evaluation of the teaching model yielded a mean of 4.63 (SD = 0.31, CV = 17.84, p = .002), indicating moderate agreement. Student performance significantly improved after intervention, with average skill scores rising from 16.11 (SD = 0.884) to 20.33 (SD = 0.564), p = .002. Student satisfaction reached 78.58% (mean = 3.90, SD = 0.72, CV = 18.78). The hybrid teaching model—blending traditional methods with interactive digital tools and interdisciplinary content (effectively enhanced students' dance proficiency, cultural awareness, and engagement). These findings support the use of blended learning and data-informed instructional strategies to drive innovation and improve outcomes in minority dance education.
Transforming EEG into Scalable Neurotechnology: Advances, Frontiers, and Future Directions Pamungkas, Yuri; Triandini, Evi; Forca, Adrian Jaleco; Sangsawang, Thosporn; Karim, Abdul
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13824

Abstract

Electroencephalography (EEG) is a key neurotechnology that enables non-invasive, high-temporal resolution monitoring of brain activity. This review examines recent advancements in EEG-based neuroscience from 2021 to 2025, with a focus on applications in neurodegenerative disease diagnosis, cognitive assessment, emotion recognition, and brain-computer interface (BCI) development. Twenty peer-reviewed studies were selected using predefined inclusion criteria, emphasizing the use of machine learning on EEG data. Each study was assessed based on EEG settings, feature extraction, classification models, and outcomes. Emerging trends show increased adoption of advanced computational techniques such as deep learning, capsule networks, and explainable AI for tasks like seizure prediction and psychiatric classification. Applications have expanded to real-world domains including neuromarketing, emotion-aware architecture, and driver alertness systems. However, methodological inconsistencies (ranging from varied preprocessing protocols to inconsistent performance metrics) pose significant challenges to reproducibility and real-world deployment. Technical limitations such as inter-subject variability, low spatial resolution, and artifact contamination were found to negatively impact model accuracy and generalizability. Moreover, most studies lacked transparency regarding bias mitigation, dataset diversity, and ethical safeguards such as data privacy and model interpretability. Future EEG research must integrate multimodal data (e.g., EEG-fNIRS), embrace real-time edge processing, adopt federated learning frameworks, and prioritize personalized, explainable models. Greater emphasis on reproducibility and ethical standards is essential for the clinical translation of EEG-based technologies. This review highlights EEG’s expanding role in neuroscience and emphasizes the need for rigorous, ethically grounded innovation.
Recent Advances in Artificial Intelligence for Dyslexia Detection: A Systematic Review Pamungkas, Yuri; Rangkuti, Rahmah Yasinta; Karim, Abdul; Sangsawang, Thosporn
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.2057

Abstract

The prevalence of dyslexia, a common neurodevelopmental learning disorder, poses ongoing challenges for early detection and intervention. With the advancement of artificial intelligence (AI) technologies in the fields of healthcare and education, AI has emerged as a promising tool for supporting dyslexia screening and diagnosis. This systematic review aimed to identify recent developments in AI applications for dyslexia detection, focusing on the methods used, types of algorithms, datasets, and their performance outcomes. A comprehensive literature search was conducted in 2025 across databases including ScienceDirect, IEEE Xplore, and PubMed using a combination of relevant MeSH terms. The article selection process followed the PRISMA guidelines, resulting in the inclusion of 31 eligible studies. Data were extracted on AI approaches, algorithm types, dataset characteristics, and key performance metrics. The results revealed that machine learning (ML) was the most widely applied method (58.06%), followed by multi-method (22.58%), deep learning (16.13%), and large language models (3.23%). Among the ML algorithms, Random Forest and Decision Tree were the most commonly used due to their robustness and performance on structured datasets. In the deep learning category, CNN were the most frequently used models, especially for image-based and sequential input data. The datasets varied widely, including digital cognitive tasks, EEG, MRI, handwriting, and eye-tracking data, with several studies employing multimodal combinations. Ensemble and hybrid models demonstrated superior performance, with some achieving accuracy rates exceeding 98%. This review highlights that AI, particularly ML and multimodal ensemble methods, holds strong potential for improving the accuracy, scalability, and accessibility of dyslexia detection. Future research should prioritize large-scale, multimodal datasets, interpretable models, and adaptive learning systems to enhance real-world implementation.
Data-Driven SEO Strategy Optimization to Enhance MSME Sales Performance on Indonesian E-Commerce Platforms Sangsawang, Thosporn; Li, Shuang
International Journal of Informatics and Information Systems Vol 8, No 3: September 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i3.262

Abstract

The rapid growth of digital commerce in Indonesia has created both opportunities and challenges for Micro, Small, and Medium Enterprises (MSMEs) seeking to increase their online visibility and sales. This study presents a data-driven approach to Search Engine Optimization (SEO) strategy optimization aimed at enhancing MSME sales performance on leading Indonesian e-commerce platforms, including Tokopedia and Shopee. Using a quantitative design, the research integrates Microsoft Excel for preliminary data exploration and Google Colab (Python) for advanced analysis and predictive modeling. The dataset, comprising over 1,000 transaction entries, includes key SEO-related indicators such as keyword rank, website traffic, backlinks, social media engagement score, advertising spend, and monthly sales. Ensemble regression models—Random Forest and Gradient Boosting—were employed to evaluate the predictive relationship between SEO factors and sales outcomes, validated through RMSE and R² metrics. The findings indicate that advertising expenditure (r = +0.83), backlinks (+0.29), and social media engagement (+0.25) are the most influential predictors of sales performance, while website traffic shows a weaker positive correlation (+0.13). These results highlight the critical role of integrated SEO and digital advertising strategies in improving MSME competitiveness. The study demonstrates that accessible analytical tools can empower MSMEs to make data-driven marketing decisions. Future research should expand model generalization across industries and explore additional digital variables to improve predictive accuracy.
Co-Authors Aang Subiyakto Abdul Karim Bakti Gama, Cipta Boonsong, Sutthiporn Chankham, Nawaphas Chen, Mei Chen, Rui Cheng, Fan Darboth, Kanokwan Dulklang, Thanyathorn Evi Triandini Evy Nurmiati Fetrina, Elvy Forca, Adrian Jaleco Fu, Ying Huang, Lihua Huda, Muhammad Qomarul Intarapreecha, Navinee Isman, Aytekin Junru, Chen Kim, Sangbum Kulnattarawong, Thidarat Li Guo Li, Shu Liu Yang Luaensutthi, Aammuay Luhuringbudi, Teguh Meinarini Catur Utami Mesraini Mesraini Nashrul Hakiem Pasawano, Tiamyod Pigultong, Matee Pigultong, Metee Prammanee, Noppadol Punyayodhin, Sulaganya Rahman, Hadi Rangkuti, Rahmah Yasinta Sawetmethikul, Pakornkiat Shuang Li Siramaneerat, Issara Tang, Lin Thepnuan, Naruemon Thepnuan, Narumom Vipahasna, Kitipoom Vipahasna, Piyanan Pannim Wang, Ning Watkraw, Wasan Wen, Tingbin Yang Wang Yongmei Li Yudhanta, Satya Yuni Sugiarti, Yuni Yuri Pamungkas Zhang, Yongfeng Zhang, Zhang Zhong, Junhua Zhou, Dan Zhou, Lin