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All Journal Mattawang: Jurnal Pengabdian Masyarakat Journal of Economic Education and Entrepreneurship Studies Information Technology Education Journal Journal of Embedded Systems, Security and Intelligent Systems Jurnal Pengabdian Masyarakat dan Riset Pendidikan
Awalia, Andi Dio Nurul
Unknown Affiliation
Humanities Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Pemberdayaan Masyarakat Sekolah melalui Penguatan Literasi Digital Berbasis AI dan AR dalam Eksplorasi Sains di SMAN 4 Barru : Penelitian Surianto, Dewi Fatmarani; Zulfikar, Muh Ihsan; Hasnining, Ayu; Agusyana, Nurrahmah; Nirmala, Putri; Awalia, Andi Dio Nurul
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 2 (2025): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 2 (October 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i2.2795

Abstract

The development of digital technology requires teachers to have adequate digital literacy in order to be able to design innovative, relevant, and adaptive learning that meets the needs of students. This activity aims to improve the competence of teachers at SMAN 4 Barru in utilizing AI Chatbot (LioraTa'), ChatGPT, and Augmented Reality (AR) for biology learning. The implementation method was carried out through Participatory Action Research (PAR) involving 26 teachers and students from SMAN 4 Barru. Evaluation was conducted through pre-tests and post-tests as well as direct observation of practice. The first result showed an increase in the average score from 8.896 (74%) on the pre-test to 10.733 (89%) on the post-test, indicating a significant improvement in teachers' understanding of digital literacy. The second result was that teachers acquired practical skills in designing chatbot conversation flows, compiling ChatGPT-based teaching materials, and utilizing AR for biology experiments, making learning more interactive and contextual. The third result was that teachers reported an increase in confidence in using digital technology and awareness of the importance of AI and AR-based learning innovations to support the quality of education. In conclusion, this training successfully had a positive impact on teachers' mastery of concepts, practical skills, and readiness to integrate digital technology into biology learning, while also strengthening their position.
Empowering South Sulawesi MGMP Economics Teachers through Digital Economic Literacy and Artificial Intelligence: Pemberdayaan Guru Ekonomi MGMP Sulawesi Selatan melalui Literasi Ekonomi Digital dan Artificial Intelligence Nurhayani, Nurhayani; Ryketeng, Masdar; Akbar, Muh.; Fakhri, M. Miftach; Rauf, Annajmi; Awalia, Andi Dio Nurul; Isma, Andika
Mattawang: Jurnal Pengabdian Masyarakat Vol. 6 No. 3 (2025)
Publisher : Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.mattawang4335

Abstract

The transformation of education in the digital era requires teachers to have adequate technological literacy, especially in economic learning that is relevant to the dynamics of everyday life. This service program aims to improve teachers' digital economic literacy through a Kahoot-based game-based learning approach. Four main materials were developed, namely Shares, Online Gambling, Life Trail, and MagicSchool AI. The activity was carried out with the South Sulawesi Economics Teacher MGMP Forum using a Participatory Action Research (PAR) approach through the stages of socialization, training, implementation, mentoring, and evaluation. The methods used were interactive quizzes, simulations, discussions, and hands-on practice. The evaluation results showed a significant increase in teachers' understanding, with the highest achievement in the material of Shares and MagicSchool AI. This training not only strengthens teachers' digital skills, but also fosters their reflective, collaborative attitudes and readiness as agents of change in integrating digital technology in economic learning.
Analysis of Naive Bayes and Support Vector Machine Algorithms in Classification of Diabetes Cases Based on Lifestyle Factors Awalia, Andi Dio Nurul; Muhammad Fadhil Hani; Dewi Fatmarani Surianto
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9783

Abstract

The increase in diabetes mellitus cases globally, including in Indonesia, demands a more adaptive lifestyle-based risk prediction strategy. This study aims to evaluate and compare the efficiency of Support Vector Machine (SVM) and Naive Bayes in the diabetes risk classification process using a Hybrid clustering-classification approach . The data analyzed in this study were obtained from the Kaggle platform , with 8,500 data of diabetes patients analyzed based on the attributes of age, gender, and smoking history. The Clustering process was carried out using K-Means (k=3), resulting in three main groups with different lifestyle characteristics. The classification results showed that Naive Bayes provided stable performance with an F1-score of 0.975. Meanwhile, SVM excelled in terms of F1-score 98.3% and perfect AUC (1,000), and was able to classify all data in cluster C3 without error. However, SVM recorded a higher classification error in the majority cluster . This study concluded that SVM was superior by 0.8% over Naive Bayes . Naive Bayes is more suitable for balanced data, while SVM is effective for detecting patterns in minority groups. These findings support the use of a hybrid approach in lifestyle data-based diabetes early detection systems. Future research directions include integrating additional variables and ensemble techniques to improve model generalization.
A PLS-SEM Analysis of Basic Psychological Needs on Self-Regulation in Digital Learning: Insights from Self-Determination Theory Ahmad Faris Al Faruq; Muhammad Fardan; Awalia, Andi Dio Nurul; Nurrahmah Agusnaya; M.Miftach Fakhri
Information Technology Education Journal Vol. 4, No. 4, November (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i4.10813

Abstract

In the rapidly evolving digital age, technology-based learning has become integral to modern education, offering flexibility and accessibility while introducing challenges in student engagement and motivation. This study explores the relationship between basic psychological needs: autonomy, competence, and relatedness. Outlined in Self-Determination Theory (SDT) and self-regulated learning in digital environments. Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM), data was collected from 737 students to examine how these needs impact self-regulation in digital learning. The findings reveal that fulfilling these psychological needs significantly enhances students' self-regulation, leading to improved learning outcomes. Autonomy, particularly when supported by digital tools, and competence, bolstered by immediate feedback and digital literacy, are crucial for fostering effective self-regulation. Relatedness, although less influential, remains important in maintaining motivation through social connections in online learning. The study contributes to the growing body of literature on SDT by highlighting the importance of creating digital learning environments that cater to students' psychological needs, thereby enhancing motivation and academic success.
Explaining Tax Digitalization Adoption: The Mediating Role of Digital Literacy in the Effects of AI-Driven Automation, Effort Expectancy, and Facilitating Conditions Fadhilatunisa, Della; Fakhri, M. Miftach; Nirmalasari, Aprilianti; Awalia, Andi Dio Nurul; Soeharto, Soeharto
Journal of Economic Education and Entrepreneurship Studies Vol. 6 No. 3 (2025): VOL. 6, NO. 3 (2025): JE3S, SEPTEMBER 2025
Publisher : Department of Economics Education, Faculty of Economics, Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62794/je3s.v6i3.9399

Abstract

The acceleration of tax digitalization through artificial intelligence (AI) has redefined modern taxation systems; however, its success largely depends on users’ digital literacy and readiness to embrace automation. This study investigates the mediating role of digital literacy in the relationship between AI-driven automation, facilitating conditions, and effort expectancy on tax digitalization adoption in Indonesia. Employing a quantitative approach with a cross-sectional survey design, data were collected from 161 individual and professional taxpayers using purposive sampling methods. The analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results demonstrate that digital literacy exerts the strongest direct and significant influence on the adoption of tax digitalization. It also mediates the effects of AI-driven automation and facilitating conditions, whereas effort expectancy shows a positive but statistically insignificant relationship. These findings underscore that digital literacy is not merely a supporting factor but a fundamental determinant of successful digital tax transformations. This study implies that policies aimed at promoting tax digitalization should prioritize digital literacy enhancement through systematic education, technical training, and user-friendly system design. By strengthening digital competence, tax authorities can increase user engagement, improve compliance, and facilitate an equitable digital transformation within tax administration.
Co-Authors Agusyana, Nurrahmah Ahmad Faris Al Faruq Akbar, Muh. Andika Isma Ayu Hasnining Della Fadhilatunisa Dewi Fatmarani Surianto M. Miftach Fakhri M.Miftach Fakhri Muhammad Fadhil Hani Muhammad Fardan NIRMALA, PUTRI Nirmalasari, Aprilianti Nurhayani nurhayani Nurrahmah Agusnaya Rauf, Annajmi Ryketeng, Masdar Soeharto Soeharto Zulfikar, Muh Ihsan