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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Edsence: Jurnal Pendidikan Multimedia Building of Informatics, Technology and Science Jurnal Mnemonic Jurnal ABDINUS : Jurnal Pengabdian Nusantara JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Pengabdian UNDIKMA Jurnal Difusi Journal of Software Engineering, Information and Communication Technology Jurnal Pengabdian Masyarakat IPTEK Green Intelligent Systems and Applications SisInfo : Jurnal Sistem Informasi dan Informatika Jurnal Informatika Polinema (JIP) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Gelar, Trisna
Politeknik Negeri Bandung
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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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.
An Adaptive Swarm Clustering Algorithm for Game AI Based on Reinforcement Learning Godot and Particle Swarm Optimization (RLGPSO) Trisna Gelar; Iwan Awaludin; Raditya Pasya; Raihan Fuad; Muhammad Rizqi Solahudin
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2480

Abstract

The management of extensive agent swarms presents significant challenges in dynamic, real-time environments, particularly within the context of game artificial intelligence, such as real-time strategy games. Traditional Particle Swarm Optimization (PSO) techniques demonstrate effectiveness in optimization tasks; however, they frequently exhibit suboptimal convergence and insufficient flexibility in complex and challenging scenarios. This study presents a hybrid methodology that combines Reinforcement Learning (RL) and Particle Swarm Optimization (PSO) to develop an adaptive swarm clustering system. This method utilizes a Deep Deterministic Policy Gradient (DDPG) agent operating externally through an API to dynamically adjust Particle Swarm Optimization (PSO) parameters, thereby maintaining a separation between adaptive intelligence and the simulation engine. This allows the swarm to effectively navigate and group within a procedurally generated 2D simulation environment with physical obstacles, unlike previous studies that rely on static mathematical benchmarks. A quantitative analysis employing Mixed Linear Model Regression (MLMR) indicates that this hybrid method significantly outperforms traditional, manually tuned PSO in terms of convergence time and diversity value. The RLGPSO model showed an 11.46% decrease in convergence time on highly complex maps. This result was statistically significant, with a p-value of 0.002 from the MLMR analysis.  This research presents a framework for the deployment of intelligent, self-organizing agent swarms, enhancing the realism and efficacy of contemporary game artificial intelligence.
Co-Authors Abdurrahman, Muhammad Syahid Ade Hodijah Athiyya Rahmawati Auliarahmi, Rizka Bakhrun, Akhmad Budiman Cholid Fauzi Didik Suwito Pribadi Djoko Cahyo Utomo Lieharyani Eddy Bambang Soewono Fabillah, Dzakira Firdaus, Lukmannul Hakim Gema Rullyana Ghifari Munawar Hamdani, Nisrina Wafa Zakiya Hayati, Hashri Hilmy Oktoharitsa Itikap, Sabar Muhamad Iwan Awaludin Jonner Hutahaean Kadhafi, Irvan Lukmanul Hakim Firdaus Melinia, Gina Mugi Pangestu Muhammad Fikri Muhammad Riza Alifi Muhammad Rizqi Sholahuddin Muhammad Rizqi Solahudin Nandhifa Azzahra Naufal Taufik Naylassana, Fauza Permana, Heru Pratidina, Caturiani Priyanto Hidayatullah, Priyanto Rachmat, Setiadi Raditya Pasya Raihan Fuad Ratna Wulan, Sri Ridho Wiratama, Anshar Safira, Decia Sari, Aprianti Nanda Sitepu, Rezky Wahyuda Siti Dwi Setiarini Sofy Fitriani Suprihanto Suprihanto Syakrani, Nurjannah Tubagus, Refdinal Urip Teguh Setijohatmo Widhiyasana, Yudi Wisnuadhi, Bambang