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All Journal Journal of Applied Smart Electrical Network and System (JASENS) Journal of Advanced Vocational Information and Communication Technology (JAVICT)
Dimas Pristovani Riananda
Politeknik Perkapalan Negeri Surabaya
Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Simulasi Perbandingan Training Dataset YOLOv5 Dan YOLOv8 Menggunakan Prototipe AUV Ryan Yudha Adhitya; Erutan Dwi Sajiwo; Zindhu Maulana Ahmad Putra; M. Yusuf Santoso; M. Khoirul Hasin; Adianto; Dimas Pristovani Riananda; Isa Rachman; Agus Khumaidi; Yuning Widiarti
Journal of Applied Smart Electrical Network and Systems Vol. 7 No. 1 (2026): JASENS Vol. 7 No. 1 (2026) : Vol. 07 No. 01, Juni 2026
Publisher : Indonesian Society of Applied Science (ISAS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/pkrjg903

Abstract

Penelitian ini mengevaluasi performa pelatihan model YOLOv5n dan YOLOv8n menggunakan dataset Kompetisi Robot Berbasis Kecerdasan Buatan (KRBAI) 2024 untuk deteksi objek pada prototipe Autonomous Underwater Vehicle (AUV). Dataset yang terdiri atas 4.114 citra dengan tiga kelas objek (gerbang, rintangan, drum) dilatih melalui Google Colab dengan konfigurasi 200 epoch, ukuran batch 16, dan resolusi citra 640x640. YOLOv5n memanfaatkan struktur berbasis anchor, sedangkan YOLOv8n menggunakan pendekatan tanpa anchor dengan modul C2f untuk efisiensi lebih tinggi. Hasil pelatihan menunjukkan YOLOv8n unggul dalam presisi (0,994 vs. 0,990) dan mAP50-95 (0,940 vs. 0,924), sementara YOLOv5n lebih baik pada recall kelas gerbang (0,999). Kedua model memiliki mAP50 serupa (0,993). Waktu pelatihan YOLOv8n sedikit lebih lama (3 jam 17 menit vs. 3 jam 31 menit). YOLOv8n terbukti lebih efektif untuk deteksi objek real-time pada AUV, mendukung navigasi dan pengenalan lingkungan bawah air. Penelitian mendatang perlu menguji dataset yang lebih beragam dan kondisi lingkungan yang lebih kompleks.
Implementation of a Kalman Filter to Improve RSSI-Based Trilateration Accuracy Using the LoRa RFM95W Module Muhammad Rayhan Firmansyah Firmansyah; Afif Zuhri Arfianto; Dimas Pristovani Riananda; Noorman Rinanto; Muhammad Khoirul Hasin; Mustika Kurnia Mayangsari
Journal of Advanced Vocational Information and Communication Technology Vol. 1 No. 3 (2026)
Publisher : ISAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/javict.v1i3.1618

Abstract

Internet of Things (IoT)-based positioning has become increasingly important for monitoring and navigation applications. This study proposes a LoRa-based positioning system using Received Signal Strength Indicator (RSSI) measurements filtered by a Kalman Filter. The filtered RSSI values are converted into distance using the Log-Distance Path Loss model, while the target position is estimated through trilateration. Positioning accuracy is evaluated by comparing the estimated coordinates with GPS coordinates obtained from a u-blox NEO-6M module using the Haversine distance. Experimental results show that the Kalman Filter improves RSSI stability and reduces distance fluctuations, leading to more consistent position estimates. However, the positioning accuracy remains lower than GPS, particularly in dynamic environments. Despite this limitation, the proposed system demonstrates the potential of LoRa as a low-power, long-range positioning solution. Future work will focus on adaptive filtering methods to further improve positioning accuracy.
Co-Authors Adianto Agus Khumaidi Arfianto, Afif Zuhri Erutan Dwi Sajiwo M. Khoirul Hasin M. Yusuf Santoso Muhammad Khoirul Hasin Muhammad Rayhan Firmansyah Firmansyah Mustika Kurnia Mayangsari Rinanto, Noorman Ryan Yudha Adhitya Yuning Widiarti Zindhu Maulana Ahmad Putra