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YOLOv8 to YOLO11 Performance Benchmark and Comprehensive Architectural Comparative Review Priyanto Hidayatullah; Nurjannah Syakrani; Muhammad Rizqi Sholahuddin; Trisna Gelar; Refdinal Tubagus
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.
Pelatihan Peningkatan Kompetensi Guru melalui Media Board Game sebagai Inovasi Pembelajaran Computational Thinking di Pondok Pesantren Darul Fithrah Kabupaten Bandung Sofy Fitriani; Setiadi Rachmat; Aprianti Nanda Sari; Nurjannah Syakrani; Priyanto Hidayatullah; Eddy Bambang Soewono; Yudi Widhiyasana; Trisna Gelar Abdillah; Siti Dwi Setiarini; Muhammad Rizqi Sholahuddin
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