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Contact Name
Andi Baso Kaswar
Contact Email
a.baso.kaswar@gmail.com
Phone
+6285656227888
Journal Mail Official
fakhri@diginus.id
Editorial Address
Antang, Makassar, South Sulawesi, Indonesia
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Kota makassar,
Sulawesi selatan
INDONESIA
Journal of Deep Learning, Computer Vision and Digital Image Processing
ISSN : 29868920     EISSN : 29868939     DOI : https://doi.org/10.61255/decoding
Core Subject : Science,
The Journal of Deep Learning, Computer Vision and Digital Image Processing (DECODING), covers all topics of artificial intelligence and soft computing and their applications, including but not limited to: • Neural networks • Reasoning and evolution • Intelligent search • Intelligent planning • Intelligence applications • Computer vision and speech understanding • Multimedia and cognitive informatics • Data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning • Technology and computing (like particle swarm optimization); intelligent system architectures • Knowledge representation • Bioinformatics • Natural language processing • Automated reasoning • Logic programming • Machine learning • Visual/linguistic perception • Evolutionary and swarm algorithms • Derivative-free optimisation algorithms • Fuzzy sets and logic • Rough sets • Simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc) • Multi-agent systems • Data and web mining • Emotional intelligence • Hybridisation of intelligent models/algorithms • Parallel and distributed realisation ofintelligent algorithms/systems • Application in pattern recognition, image understanding, control, robotics and bioinformatics • Application in system design, system identification, prediction, scheduling and game playing • Application in VLSI algorithms and mobile communication/computing systems
Articles 1 Documents
Search results for , issue "volume 4 issue 2 june 2026" : 1 Documents clear
Development of Android-Based Smart Learning Media for the Operating Systems Course Using the ADDIE Model Kurnia Wahyu Prima; Hariyadi; Ayu Hasnining
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 4 Issue 2 June 2026
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v4i2.1279

Abstract

Purpose – The rapid advancement of digital technology has encouraged higher education institutions to integrate innovative learning media to enhance the quality of the teaching and learning process. However, learning activities in Operating Systems courses are still predominantly conducted using conventional methods, causing students to experience difficulties in understanding abstract concepts such as process management, memory management, CPU scheduling, and file systems. This study aims to develop Android-Based Smart Learning media for the Operating Systems course and to determine the feasibility level of the developed media as an interactive learning tool.Method – This study employed a Research and Development (R&D) approach using the ADDIE model, which consists of five stages: Analysis, Design, Development, Implementation, and Evaluation. The developed product was validated by subject-matter experts and media experts before being implemented with students of the Informatics and Computer Engineering Education Program who were enrolled in the Operating Systems course. Data were collected through validation sheets and student response questionnaires using a five-point Likert scale and were analyzed using descriptive quantitative methods.Results – The findings indicate that the Android-Based Smart Learning media was successfully developed by integrating learning materials, instructional videos, interactive quizzes, and automated feedback features into a single Android application. The material expert validation yielded a score of 89.00%, while the media expert validation achieved a score of 90.00%, both categorized as highly feasible. Furthermore, student responses obtained an average percentage of 90.27%, classified as very good. Therefore, the developed media was considered suitable for supporting the learning process in the Operating Systems course.Research Implications – This study was limited to a single study program and Android devices; therefore, the generalizability of the findings remains limited.Originality – This research integrates the concepts of mobile learning and smart learning into a single interactive learning medium specifically designed to support Operating Systems education in higher education institutions.

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