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Journal of Deep Learning, Computer Vision and Digital Image Processing
Published by Sakura Digital Nusantara
ISSN : 29868920     EISSN : 29868939     DOI : https://doi.org/10.61255/decoding
Core Subject : Science,
Computer Science & IT
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
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 33 Documents
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Analisis Sentimen Aplikasi Gojek Driver Menggunakan Support Vector Machines dan Naïve Bayes dengan Framework Optuna Sebagai Hyperparameter Tuning Nadilla Madjid; Rudi Setiawan
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 4 Issue 1 March 2026
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v4i1.948

Abstract

Purpose – This study aims to analyze user review sentiment toward the Gojek Driver application and compare the performance of two classification algorithms, Support Vector Machine (SVM) and Naïve Bayes, using Optuna as a framework for hyperparameter tuning.Methods – The study collected and labeled user review data into positive and negative sentiment categories. Text preprocessing involved cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Features were represented using TF-IDF. The dataset was then divided into training and testing sets, and SVM and Naïve Bayes models were trained using automated hyperparameter optimization with Optuna. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix.Findings – The application of SMOTE to the Optuna-tuned SVM model produced better performance than the other models tested in this study. The best model achieved an accuracy of 0.868, a highest cross-validation accuracy of 92.72%, and a weighted average F1-score of 0.87. These results indicate that SVM was more effective in handling high-dimensional TF-IDF features and complex decision boundaries.Research implications – The findings support the use of automated sentiment analysis to assist operational decision-making and improve the quality of Gojek Driver services. The proposed approach can accelerate the identification of service-related issues and provide a basis for proactive responses to user feedback.Originality – This study offers an original contribution by directly comparing SVM and Naïve Bayes on a Gojek Driver review dataset while applying Optuna-based hyperparameter tuning. It highlights the effect of automated tuning on both algorithms within a TF-IDF representation framework for ride-hailing service data, a topic that remains underexplored in the specific context of Gojek Driver within the local literature.
The Utilization of Technology-Based Media in Physical Education Learning Strategies at Madrasah Ibtidaiyah Fitriyani; Friska Aulia; Annisa Nur Cahyani; Bardan Hanafi Alfian Luthfi; Meity Suryandari
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 4 Issue 1 March 2026
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v4i1.1091

Abstract

Purpose – This study examines the use of technology-based media in Physical Education learning at Madrasah Ibtidaiyah (MI) and Elementary School (SD) levels. It addresses the limitations of conventional instruction in explaining complex movement concepts and the need for more interactive, visual, and student-centered learning strategies in the digital era.Methods – A qualitative literature review was conducted using a systematic review procedure adapted from the PRISMA 2020 framework. Searches were carried out in Google Scholar and ERIC for publications from 2015 to 2025. From 145 initial records, 18 studies met the inclusion criteria and were synthesized qualitatively.Findings – The review shows that instructional videos, animations, simulations, interactive multimedia, mobile applications, and exergames can support students’ movement understanding, motivation, engagement, and participation in Physical Education learning. These media help present movement concepts visually and flexibly. However, their implementation is constrained by limited technological infrastructure, unequal access to digital resources, and insufficient teacher competence in integrating technology into movement-based instruction.Research implications – The findings suggest that technology integration should be supported by adequate infrastructure, teacher training, and pedagogical strategies that maintain active physical participation. Since this study is literature-based, its findings should be interpreted as synthesized evidence rather than direct empirical proof.Originality – This study provides a focused synthesis of recent literature on technology-based media in MI/SD Physical Education and identifies its pedagogical potential, implementation challenges, and future research directions.
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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