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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Informatics (IJEEI) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JURNAL NASIONAL TEKNIK ELEKTRO Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL INTEGRASI JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah Journal of Applied Electrical Engineering Journal of Community Service and Empowerment Journal of Applied Computer Science and Technology (JACOST) Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Engagement: Jurnal Pengabdian Kepada Masyarakat
Eko Rudiawan Jamzuri
Department Of Electrical Engineering, Politeknik Negeri Batam, Indonesia
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Mechanical Engineering Social Sciences Other

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Comparison of the Application of YOLOv5, YOLOv8, and YOLOv11 for Training Chinese Chess Objects Ahmad Mufid Panisti; Ryan Satria Wijaya; Eko Rudiawan Jamzuri; Anugerah Wibisana
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12252

Abstract

The use of YOLO (You Only Look Once)-based object detection algorithms has become one of the main approaches in visual object recognition and training. This study aims to compare the performance of three versions of YOLO, namely YOLOv5, YOLOv8, and YOLOv11, in training models to detect objects in Chinese chess images. The dataset used consists of images of Chinese chess boards and pieces in various positions and lighting variations. The training process was carried out using uniform parameters to ensure fair evaluation, including batch size, number of epochs, and image resolution. The performance of each model was evaluated based on detection accuracy, inference speed, and computational efficiency metrics. The results of the study show that each version of YOLO has specific advantages in certain aspects, such as training speed or detection precision. From the 7224 images used as the dataset, several results were obtained that were necessary in helping to compile this journal. These included Precision (YOLOv5: 0.94, YOLOv8: 0.96, YOLOv11: 0.98), Recall (YOLOv5: 0.93, YOLOv8: 0.98, YOLOv11: 0.96), and mAP (YOLOv5: 0.96, YOLOv8: 0.98, YOLOv11: 0.99). This study provides important insights into the advantages and disadvantages of each version of YOLO in the specific application of Chinese chess object recognition, as well as providing guidance for developers in choosing the model that suits their project needs. This study provides insights into the strengths and limitations of each YOLO version, offering guidance for selecting appropriate models in real-time Chinese chess object detection applications.
Robotic Bin-Picking Object Detection Using YOLOv11-OBB with SAM2 Auto-Annotation Very Very; Eko Rudiawan Jamzuri
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12806

Abstract

Robotic bin-picking requires accurate detection of randomly oriented objects under cluttered conditions. Conventional axis-aligned bounding boxes struggle to distinguish adjacent objects, motivating the use of oriented bounding boxes (OBB). This paper proposes a complete pipeline for bin-picking object detection that combines the Segment Anything Model 2 (SAM2) with YOLOv11-OBB. A three-stage auto-annotation pipeline first applies a YOLOv11s horizontal bounding-box detector to localize each object and assign its class label. SAM2 then performs automatic instance segmentation within each detected bounding-box region without requiring manual point prompts. Last, the resulting masks are converted to OBB annotations via minimum-area rectangle fitting, reducing annotation time by approximately 877× compared with manual labeling. YOLOv11-OBB featuring C2PSA attention, C3k2 convolution blocks, and an anchor-free rotated detection head is trained for 300 epochs on a purpose-built dataset of three cylindrical object classes (white, black, and red) captured in a UR3 collaborative-robot workspace. Experiments demonstrate an overall mAP@0.5 of 0.995 and mAP@0.5:0.95 of 0.949, with an inference time of 138 ms per frame on a consumer CPU. The results indicate that the proposed pipeline is well-suited for oriented object detection in industrial bin-picking applications.
Co-Authors Adlian Jefiza Ahmad Mufid Panisti Ahmad Riyad Firdaus Ahmad Riyad Firdaus Alvin Anisatul Munawaroh ANUGERAH WIBISANA Anugerah Wibisana B. Budiana Budi Sugandi Daniel Sutopo Pamungkas, Daniel Sutopo Eka Mutia Lubis Emelia Rosari Siregar Fitriyanti Nakul Geminiesty Lathifasari Djavendra Hanjaya Mandala Hasnira Hasnira Hendawan Soebhakti Heru Wijanarko Ika Karlina Laila Nur Suciningtyas Ikhsan, Habyb Nur Islami, Megan Shahra Jacky Baltes Julanba, Vafin Kaputra, Aldi Lindawani Siregar Mayang Sari MOCHAMAD ARI BAGUS NUGROHO Muhamad Ilham Mulyadi, Indra Hardian Naufal Abdurrahman Prasetyo Naurah Nazhifah Ricardo Silitonga, Jony Arif Ridwan Ridwan Rifky Afriza Rifqi Amalya Fatekha Riska Analia Ryan Satria Wijaya Ryan Satria Wijaya Ryan Satria Wijaya S. Susanto Santonius, Santonius Senanjung Prayoga Senanjung Prayoga Sinaga, Jepelin Amstrong Siti Aisyah Sumantri Kurniawan Risandriya Susanto Susanto Susanto Susanto Utama, Eldio Very Very Vivin Oktowinandi Wahyu Wibowo Widya Rika Puspita