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All Journal Jurnal Ilmu Komputer dan Informasi JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JPF (Jurnal Pendidikan Fisika) Universitas Islam Negeri Alauddin Makassar JTERA (Jurnal Teknologi Rekayasa) The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Edsence: Jurnal Pendidikan Multimedia Building of Informatics, Technology and Science Jurnal ABDINUS : Jurnal Pengabdian Nusantara JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Media Jurnal Informatika Bulletin of Computer Science Research Journal of Software Engineering, Information and Communication Technology Industrial Research Workshop and National Seminar Journal Serambi Engineering (JSE)
Muhammad Rizqi Sholahuddin
Politeknik Negeri Bandung
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Physics Social Sciences Other

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Optimizing YOLOv8 for Real-Time CCTV Surveillance: A Trade-off Between Speed and Accuracy Sholahuddin, Muhammad Rizqi; Harika, Maisevli; Awaludin, Iwan; Dewi, Yunita Citra; Dhia Fauzan, Fachri; Sudimulya, Bima Putra; Widarta, Vandha Pradiyasma
JOIN (Jurnal Online Informatika) Vol 8 No 2 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i2.1196

Abstract

Real-time video surveillance, especially CCTV systems, requires fast and accurate face detection. Object detection models with slow inference times are ineffective in real-time. This study addresses this challenge by improving the inference speed of the YOLOv8 model, a leading object detection framework known for its accuracy and speed. We focus on pruning the model's architecture, particularly the P5 head section, which detects larger objects. According to Bochkovskiy's 2020 research, this modification enhances the model's performance specifically for medium and small objects in CCTV footage. The standard YOLOv8 model and its modified version were compared for inference time, mean Average Precision (mAP), and model weight. The pruned YOLOv8 model cuts inference time by 15.56%, from 4.5 ms to 3.8 ms, and reduces model weight. The advantages mentioned above are offset by a 1.6% decrease in mean average precision. This research advances object detection technology by demonstrating architectural modifications' efficacy. These changes make the model faster and lighter, making it suitable for real-time surveillance. The accuracy trade-off is slight. The implications of these findings are crucial for implementing efficient object detection systems in CCTV surveillance. These findings also lay the groundwork for future research to improve such systems' speed-accuracy trade-off.
Uji Fungsionalitas Dan Kebermanfaatan Aplikasi Random Angka Soal Vektor Berbasis Web Sebagai Media Latihan Soal Yunita Citra Dewi; Muhammad Rizqi Sholahuddin; Topan Trianto
JPF (Jurnal Pendidikan Fisika) Universitas Islam Negeri Alauddin Makassar Vol 13 No 1 (2025)
Publisher : Pendidikan Fisika UIN Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/jpf.v13i1.51091

Abstract

The fundamental role of vector concepts in engineering education underscores the development of a web-based application designed to facilitate vector problem-solving practice. To enhance students’ comprehension of vector material, repeated exposure to varied problem sets is essential. This study aims to design and evaluate a practice application that integrates randomized number generation, enabling users to encounter dynamically changing numerical values with each use. Employing a Research and Development (R&D) approach with experimental methods, the application was developed using the Laravel Model-View-Controller (MVC) framework. The database structure, built on MariaDB, was optimized for efficient storage of questions, solutions, and user responses. The application was implemented and tested by first-year students in the Diploma 3 Mechanical Engineering Program at Politeknik Negeri Bandung. Functional testing revealed that 92.89% of the features operated effectively. Furthermore, based on the usefulness assessment, the application received a total score of 254, classifying it as “highly useful.”
Pelatihan Peningkatan Kompetensi Guru melalui Media Board Game sebagai Inovasi Pembelajaran Computational Thinking di Pondok Pesantren Darul Fithrah Kabupaten Bandung Fitriani, Sofy; Rachmat, Setiadi; Sari, Aprianti Nanda; Syakrani, Nurjannah; Hidayatullah, Priyanto; Soewono, Eddy Bambang; Widhiyasana, Yudi; Abdillah, Trisna Gelar; Setiarini, Siti Dwi; Sholahuddin, Muhammad Rizqi
Jurnal ABDINUS : Jurnal Pengabdian Nusantara Vol 10 No 1 (2026): Volume 10 Nomor 1 Tahun 2026
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/ja.v10i1.27180

Abstract

Kegiatan Pengabdian kepada Masyarakat (PKM) ini dilatarbelakangi oleh keterbatasan guru pesantren dalam memahami dan mengimplementasikan keterampilan computational thinking dalam pembelajaran. Di Pondok Pesantren Darul Fithrah, media pembelajaran inovatif berupa board game berbasis Unplugged Computational Thinking dirancang untuk menjembatani kebutuhan tersebut sekaligus memberikan alternatif metode pembelajaran yang lebih interaktif. Metode pelaksanaan meliputi perancangan board game, penyusunan instrumen pelatihan, pelaksanaan pre-test, pemberian materi dan simulasi board game, serta post-test dan observasi implementasi di kelas. Hasil kegiatan menunjukkan adanya peningkatan signifikan pada pemahaman guru mengenai computational thinking, terlihat dari kenaikan skor rata-rata pre-test dan post-test, khususnya pada aspek pengenalan pola, penyusunan langkah pemecahan masalah, dan pemahaman istilah computational thinking. Respon kuesioner dan feedback guru juga menunjukkan bahwa board game dipandang interaktif, mudah digunakan, serta potensial untuk diterapkan dalam pembelajaran di pesantren. Dengan demikian, kegiatan PKM ini berhasil mendorong guru lebih siap mengintegrasikan keterampilan abad 21 ke dalam praktik pendidikan
Analisis Visual Perilaku Agen Q-Learning dan SARSA pada Cliff Walking Problem dengan Explainable Reinforcement Learning Atqiya, Firas; Sholahuddin, Muhammad Rizqi
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.984

Abstract

Reinforcement Learning (RL) has achieved remarkable success in complex sequential decision tasks. However, modern RL models often lack explainability, creating a serious "black box" problem, especially in high-stakes domains. This study proposes a Pygame-based real-time visualization architecture for RL, and demonstrates its benefits in a Cliff Walking case study using Q-Learning and SARSA algorithms. Key contributions include: (1) a real-time visualization architecture that decouples training logic from graphics rendering with support more than 60 FPS, (2) interpretive visualization techniques including diverging heatmaps, dynamic policy arrows, and Ghost Policies, and (3) a comprehensive empirical study clarifying the distinct characteristics of both algorithms. Experimental results clearly show that Q-Learning selects an efficient but risky path aligned with its optimistic off-policy nature, while SARSA converges on a safer path reflecting its on-policy nature that considers exploration safety. Quantitatively, Q-Learning successfully achieved an optimal 13-step path with an accumulation of 10,642 falls, whereas SARSA converged to a safe 23-step path with a significantly higher collision frequency (232,844 times) to avoid extreme penalties from the cliff zone.
YOLOv8 to YOLO11 Performance Benchmark and Comprehensive Architectural Comparative Review Hidayatullah, Priyanto; Syakrani, Nurjannah; Sholahuddin, Muhammad Rizqi; Gelar, Trisna; Tubagus, Refdinal
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.6598

Abstract

In the domain of deep learning-driven computer vision, YOLO is revolutionary. However, not all YOLO models are accompanied by academic articles and architectural diagrams. It complicates the comprehension of the model's operation. Moreover, the existing review papers fail to examine each model comprehensively. This work aims to provide a thorough comparative analysis of the architectures from YOLOv8 to YOLO11, allowing readers to swiftly understand the operational mechanisms and differences among the models. We analyzed the architecture of each YOLO version by reviewing relevant scholarly articles, official documentation, and examining the source code. In particular, we discovered that YOLOv8 through YOLO11 differ in novelty while sharing similarities in the anchor-free and Non-Maximum Suppression (NMS) aspects, except YOLOv10 (NMS-free). Each also has drawbacks, such as differing levels of complexity in the way features are connected (v8), architectural structure and training (v9), training methods or dual assignments (v10), inference, and code implementation (v11). While each version improves architecture, some blocks remain unchanged. This study helps readers understand different YOLO version architectures and inspires how to improve their performance. It also provides readers with a comprehensive architecture diagram and detailed descriptions of each block, serving as a reference for both academic and practical applications. In terms of performance, a benchmark using the Roboflow 100 dataset reveals that YOLOv9 achieves superior accuracy; however, it is eight times slower owing to its NMS mechanism. YOLOv10 is the fastest but least accurate, whereas YOLOv8 and YOLO11 provide a balanced compromise between speed and accuracy.
Evaluating RAG Performance on Small Language Models for Low-Resource Devices through Chunking and Retrieval Methods Agustiani, Amelia Dewi; Putri, Salsabila Maharani; Hutahaean, Jonner; Sholahuddin, Muhammad Rizqi; Alifi, Muhammad Riza; Hodijah, Ade
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1733

Abstract

Retrieval-Augmented Generation (RAG) combines generative capabilities of language models with external document retrieval to answer questions grounded in reference texts. However, deploying RAG on low-resource devices like Android smartphones is challenging because SLMs have limited computational capacity and depend heavily on efficient chunking and retrieval. Although interest in on-device processing is growing, research on RAG configurations for SLMs under strict resource constraints especially for domain-specific tasks remains limited. This study therefore investigates which combinations of chunking technique, chunk size, overlap, and retrieval strategy best balance accuracy and speed on low-resource devices. The evaluation uses 148 Indonesian questions sourced from an official Hajj guidebook. The study consists of two phases retrieval and generation. Retrieval is evaluated using BLEU, ROUGE-L, MRR, MAP, and Hit@k, while answer quality is measured with BERTScore. The experiments compare different chunking methods (fixed-size or semantic), chunk sizes (128 or 256 tokens), overlaps (25, 50 and 100 tokens), and retrieval methods (dense, sparse, or hybrid). Results show that sparse retrieval with 256-token chunks and 100-token overlap yields the best answer quality (F1 = 0.726). However, 128-token chunks with the same overlap provide the fastest generation time (69.737 seconds). The main contribution of this study is a systematic evaluation of RAG configurations for fully on-device SLMs using a domain-specific Hajj and Umrah dataset not explored in prior research. The findings provide practical guidance for designing efficient and accurate RAG-based question-answering systems on low-resource devices.
Co-Authors Ade Hodijah Agustiani, Amelia Dewi Asri Maspupah Bakhrun, Akhmad Bima Putra Sudimulya Carolina Magdalena Lasambouw Dewi, Yunita Citra Dhia Fauzan, Fachri Dimas Kurniawan Djoni Djatnika Eddy Bambang Soewono Elfada, Berliana Fachri Dhia Fauzan Farida Agoes FENNY MARTHA DWIVANY Firas Atqiya Firdaus, Lukmannul Hakim Gantini, Annisa Dinda Gelar, Trisna Hayati, Hashri Husna Faridah Ihsani, Nisa Iwan Awaludin Jonner Hutahaean Kadhafi, Irvan Lukmannul Hakim Firdaus Maisevli Harika Maisevli Harika Maisevli Harika Malika, Adinda Faayza Meilinda, Lina Melinia, Gina Muhammad Riza Alifi Mulia, Syelvie Ira Ratna Neneng Nuryati Nugraha, Wili Akbar Nuri Nurianti Priyanto Hidayatullah, Priyanto Putri, Salsabila Maharani Putriadhinia, Salma Syawalan Rachmat, Setiadi Santosa, Yoseph Sari, Aprianti Nanda Setiadi Rachmat Sitepu, Rezky Wahyuda Siti Dwi Setiarini Sofy Fitriani Sofy Fitriani SONY SUHANDONO Sudimulya, Bima Putra Suharsih, Ririn Syakrani, Nurjannah Topan Trianto Tubagus, Refdinal Vandha Pradiyasma Widarta Widarta, Vandha Pradiyasma Widhiyasana, Yudi Wisnuadhi, Bambang Wulan, Sri Ratna Yudi Widhiyasana Yunita Citra Dewi Yusuf Sofyan Yusuf Sofyan, Jurusan Teknik Elektro Politeknik Negeri Bandung, Parno Raharjo, Tria Maariz, Jurusan