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Journal of Applied Computer Science and Technology (JACOST)
Published by Indonesian Society of Applied Science
ISSN : -     EISSN : 27231453     DOI : https://doi.org/10.52158/jacost
Core Subject : Science,
Computer Science & IT
Fokus dan Ruang Lingkup Journal of Applied Computer Science and Technology (JACOST) dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian bidang ilmu komputer dan teknologi. Sebagai bagian dari semangat menyebarluaskan ilmu pengetahuan hasil dari penelitian dan pemikiran untuk pengabdian pada Masyarakat luas dan sebagai sumber referensi akademisi di bidang Ilmu Komputer dan Teknologi. Journal of Applied Computer Science and Technology (JACOST) menerima artikel ilmiah dengan lingkup penelitian pada: Rekayasa Perangkat Lunak Rekayasa Perangkat Keras Keamanan Informasi Rekayasa Sistem Sistem Pakar Sistem Penunjang Keputusan Data Mining Sistem Kecerdasan Buatan Jaringan Komputer Teknik Komputer Pengolahan Citra Algoritma Genetik Sistem Informasi Business Intelligence and Knowledge Management Database System Big Data Internet of Things Enterprise Computing Machine Learning Topik kajian lainnya yang relevan
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 91 Documents
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Rancang Bangun dan Evaluasi Sistem Sortir Otomatis Barang dengan Metode Deteksi Objek YOLO v5 dan Kendali PLC Outseal Pamungkas, Daniel Sutopo; Shendy Saputra; Anastasya Andaresta Pelmelay
Journal of Applied Computer Science and Technology Vol 6 No 1 (2025): Juni 2025
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v6i1.1165

Abstract

Manual sorting in manufacturing is time-consuming, labor-intensive, and prone to errors, especially when items have similar colors and shapes. This study aims to design and implement an automatic sorting system for goods based on color and shape to enhance production efficiency. The system integrates a webcam for image acquisition, the YOLO v5 object detection algorithm for real-time classification, and the Outseal PLC to control actuators via a Ladder Diagram. An experimental method was used, with a dataset of 18 object types tested under three lighting conditions (daylight, low light, and ring light). Performance was evaluated using a confusion matrix, achieving an average accuracy of 88.26% and precision of 70.38%, with the best results under ring light illumination. These findings demonstrate that the proposed system can reduce operational costs and improve productivity for small- to medium-scale industries. Future work should include extended field testing and adaptive algorithms for varying lighting environments.

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