Penerapan Algoritma YOLOv8 Nano Untuk Vehicle Tracking Pada KM Tiga Putra

Fery, Santria (2026) Penerapan Algoritma YOLOv8 Nano Untuk Vehicle Tracking Pada KM Tiga Putra. S1 Teknik Informatika thesis, STMIK WIDYA CIPTA DHARMA.

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Abstract

Fery Santria, 2026, Penerapan Algoritma YOLOv8 Nano untuk Vehicle Tracking Pada KM Tiga Putra, Skripsi, Program Studi Teknik Informatika, STMIK Widya Cipta Dharma Samarinda. Pembimbing (I) Vilianty Rafida., S.T, M.Kom. dan Pembimbing (II) Ulfah Nurfadhila, S.Pd., M,Pd. Kata Kunci : Computer Vision, Pelacakan Kendaraan, YOLOv8 Nano, ByteTrack, KM Tiga Putra. Manajemen kapasitas muatan pada geladak kapal feri KM Tiga Putra saat ini masih mengandalkan pencatatan manual yang rentan terhadap inefisiensi dan berisiko menyebabkan kelebihan muatan. Penelitian ini bertujuan untuk mengotomatisasi pengawasan tersebut dengan merancang sistem Computer Vision menggunakan bahasa pemrograman Python (versi 3.10+) dan pustaka Ultralytics di dalam lingkungan kerja Visual Studio Code (VS Code). Sistem yang dibangun mengintegrasikan arsitektur YOLOv8 Nano dan algoritma ByteTrack untuk mendeteksi, melacak, serta menghitung kendaraan secara presisi berdasarkan rekaman Dataset dari Webcam Logitech C270 (720 piksel, 30 FPS) yang dipra-pemrosesan melalui platform Roboflow. ============================================================ Fery Santria, 2026, Application of the YOLOv8 Nano Algorithm for Vehicle Tracking on KM Tiga Putra, Thesis, Informatics Engineering Study Program, STMIK Widya Cipta Dharma Samarinda. Advisor (I) Vilianty Rafida, S.T., M.Kom. and Advisor (II) Ulfah Nurfadhila, S.Pd., M.Pd. Keywords: Computer Vision, Vehicle Tracking, YOLOv8 Nano, ByteTrack, KM Tiga Putra. The load capacity management on the deck of the KM Tiga Putra ferry currently relies on manual recording, which is prone to inefficiency and the risk of overloading. This research aims to automate the monitoring by designing a Computer Vision system using the Python programming language (version 3.10+) and the Ultralytics library within the Visual Studio Code (VS Code) environment. The developed system integrates the YOLOv8 Nano architecture and the ByteTrack algorithm to precisely detect, track, and count vehicles based on a Dataset recorded by a Logitech C270 Webcam (720 pixel, 30 FPS) and pre-processed through the Roboflow platform. Considering the implementation is conducted on a low-specification perangkat keras environment a laptop running the Windows 11 operating system, an Intel Celeron processor, 8GB of RAM, and Solid State Drive (SSD) storage the computational load is optimized by implementing a frame skipping technique to prevent memory overload.

Item Type: Thesis (S1 Teknik Informatika)
Additional Information: Pembimbing 1 : Vilianty Rafida, S.T., M.Kom Pembimbing 2 : Ulfah Nurfadhila, S.Pd., M.Pd
Uncontrolled Keywords: Computer Vision, Pelacakan Kendaraan, YOLOv8 Nano, ByteTrack, KM Tiga Putra.
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Teknik Informatika
Depositing User: Mr Fery Santria
Date Deposited: 10 Aug 2026 01:16
Last Modified: 10 Aug 2026 01:16
URI: http://repository.wicida.ac.id/id/eprint/6464

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