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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JURNAL INTEGRASI JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Applied Electrical Engineering Journal of Community Service and Empowerment Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Innovative: Journal Of Social Science Research Engagement: Jurnal Pengabdian Kepada Masyarakat The Indonesian Journal of Computer Science
ANUGERAH WIBISANA
Politeknik Negeri Batam
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Journal : journal of applied informatics and computing

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Sensor Fusion – Based Localization for ASV with Linear Regression Optimization Wijaya, Ryan Satria; Jamzuri, Eko Rudiawan; Wibisana, Anugerah; Sinaga, Jepelin Amstrong; Julanba, Vafin
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10048

Abstract

ASV (Autonomous Surface Vehicle) is one of popular innovations in the maritime field that is widely used for various missions on the water surface. The ASV itself has the ability to operate automatically without human intervention. Therefore, ASV requires an accurate and reliable localization system. This research focuses on developing an ASV localization system using waterflow sensors optimized through linear regression and integrated with orientation data from an IMU sensor through sensor fusion to obtain global coordinate position estimation. The experiments conducted showed a significant improvement in accuracy after optimization, with the Root Mean Square Error (RMSE) of the waterflow sensor data decreasing from 161.65 meters to 0.28 meters. Moreover, the yaw data reading by IMU achieved accuracy with RMSE 1.54 degrees. The localization system in the final test achieved RMSE values of 0.07 meters for the X-axis, 0.14 meters for the Y-axis, and 1.9 degrees for yaw during the ASV global positioning experiment. In addition, a GUI (Graphical User Interface) was developed for visualization with average communication latency of 113.6 milliseconds. This localization system is a promising solution in stable water condition.
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 Abda Abda Adlian Jefiza Adriansyah, Yoga Ahmad Mufid Panisti Ahmad Riyad Firdaus Ahmad Riyad Firdaus B. Budiana Bima Sena Bayu Dewantara Dadet Pramadihanto Eka Mutia Lubis Eko Rudiawan Jamzuri Emelia Rosari Siregar Fitriyanti Nakul Hasnira Hasnira Hendawan Soebhakti Ika Karlina Laila Nur Suciningtyas Ikhwan Heprilnez Pratama Julanba, Vafin Kaputra, Aldi Maulidiah, Hana Mutialif MOCHAMAD ARI BAGUS NUGROHO Muhammad Imron Shodiq Muhammad Naufal Airlangga Diputra Nanta Fakih Prebianto Naufal Abdurrahman Prasetyo Naurah Nazhifah Naurah Nazhifah Nur Sakinah Asaad Rifky Afriza Rifqi Amalya Fatekha Riska Analia Ryan Satria Wijaya Ryan Satria Wijaya S. Susanto Santonius, Santonius Senanjung Prayoga Sinaga, Jepelin Amstrong Susanto Susanto Widya Rika Puspita