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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) JISKa (Jurnal Informatika Sunan Kalijaga)
Michael Sidharta Dharma
Universitas Buddhi Dharma
Computer Science & IT Electrical & Electronics Engineering Library & Information Science

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An AI-Based Mobile Application for Personalized Learning in Secondary Education Ellysha Dwiyanthi Kusuma; Lianny Wydiastuty Kusuma; Hartana Wijaya; Arya Bodhi Yuardi; Michael Sidharta Dharma
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 18, No 1 (2026): MATICS
Publisher : The Informatics Engineering Study Program

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v18i1.39498

Abstract

The development of Artificial Intelligence (AI) provides opportunities to support personalized learning, particularly at the secondary school level, where students have diverse learning needs. This study aims to develop an AI-assisted mobile application prototype to support personalized learning and assist teachers in monitoring student progress. The research adopts a Research and Development (RD) approach, which includes stages of user needs analysis, system design, prototype development, and user evaluation. The application was developed in two versions: a student application and a teacher application, both implemented on Android devices. The student application provides features such as a learning dashboard, adaptive quizzes, learning analytics, feedback, and rule-based learning recommendations derived from student performance, while the teacher application offers class monitoring, grade input, and learning material upload functionalities. The evaluation involved 43 students and 3 teachers using usability questionnaires. Results indicate that more than 85% of students found the application easy to use and beneficial in supporting their learning, while teachers reported that the system supports monitoring and instructional activities. These findings suggest that the proposed application is feasible and well-accepted as a personalized learning support tool. However, further studies are required to evaluate its impact on learning outcomes using more rigorous experimental methods.
Tinjauan Sistematis terhadap Arsitektur Convolutional Neural Network untuk Deteksi Kantuk pada Pengemudi Berbasis Visual Michael Sidharta Dharma; Aditiya Hermawan
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 3 (2026): September 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.5914

Abstract

This study presents a systematic literature review (SLR) on the application of Convolutional Neural Networks (CNNs) for vision-based driver drowsiness detection. A total of 23 peer-reviewed studies published between 2021 and 2025 were analyzed using the PICOC framework and PRISMA 2020 guidelines. The review aims to identify trends in model architectures, datasets, preprocessing strategies, and performance outcomes. The findings show that transfer learning models, such as MobileNetV2, ResNet, and InceptionV3, along with hybrid architectures like CNN–LSTM, dominate recent research, achieving accuracies predominantly above 90%. However, performance varies significantly depending on dataset characteristics and experimental conditions. Key challenges include sensitivity to low-light environments, occlusion (e.g., glasses), and head pose variations. In addition, most studies rely on controlled datasets, limiting real-world applicability and generalization. Emerging trends emphasize lightweight architectures, temporal modeling, and efficiency-oriented optimization for real-time deployment. This review highlights the need for cross-dataset evaluation, more diverse and representative data, and robust model design to ensure reliable performance in real-world driving scenarios.
Co-Authors Aditiya Hermawan Arya Bodhi Yuardi Ellysha Dwiyanthi Kusuma Hartana Wijaya Lianny Wydiastuty Kusuma