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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam
Ryan Satria Wijaya
Politeknik Negeri Batam
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mechanical Engineering

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The Robotics Training and RoboRoarZ Competition as a STEM-Based Community Service Program for Enhancing STEM Competencies of Senior High School Students in Kepulauan Riau Emelia Rosari Siregar; Anugerah Wibisana; Ahmad Riyad Firdaus; Hendawan Soebhakti; Eko Rudiawan Jamzuri; Senanjung Prayoga; Rifky Afriza; Naurah Nazhifah; Ryan Satria Wijaya; Rifqi Amalya Fatekha
Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Vol. 8 No. 1 (2026): Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/abdimaspolibatam.v8i1.13481

Abstract

The low adoption of STEM (Science, Technology, Engineering, and Mathematics) education in senior high schools (SMA/SMK) across Kepulauan Riau Province, Indonesia, has been identified as a critical gap in preparing students for Industry 4.0 and Society 5.0. To address this challenge, Politeknik Negeri Batam conducted a community service program entitled Robotics Training and RoboRoarZ Competition for Enhancing STEM Competencies of Senior High School Students in Kepulauan Riau. The program involved 200 students from 22 schools organized into 25 competing teams, and was implemented over two days on 12–13 August 2025. Participants received hands-on training using the SMORPHI modular reconfigurable robot, covering hardware assembly, programming, and autonomous navigation. A robotics competition, RoboRoarZ Indonesia 2025, was subsequently held featuring two categories: Autonomous Challenge (line-follower) and Imaginary Challenge (simulation). Pre-test and post-test assessments revealed a significant improvement in participants’ robotics knowledge, with average scores increasing from 45.2 to 72.8 (a 61.1% gain), and the pass rate rising from 28% to 84%. Questionnaire data further confirmed high positive response rates across indicators of interest, motivation, and career aspiration in robotics, consistently exceeding 88%. The findings demonstrate that competition-based STEM learning combining workshop instruction with project-based and problem-solving activities is an effective and scalable model for improving STEM competencies among secondary school students in underserved regions.
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.
Co-Authors Ahmad Mufid Panisti Ahmad Riyad Firdaus ANUGERAH WIBISANA Eko Rudiawan Jamzuri Emelia Rosari Siregar Hendawan Soebhakti Naurah Nazhifah Rifky Afriza Rifqi Amalya Fatekha Senanjung Prayoga