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Yudhi Darmawan
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Computer Science & IT Control & Systems Engineering Engineering

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RANCANG BANGUN LESAN TEMBAK MENGGUNAKAN LASER SPOT BERBASIS IMAGE PROCESSING: Telekomunikasi Maulana Doni Handoyo; Gatut Yulisusianto; Yudhi Darmawan
Jurnal Telkommil Vol 5 No 1 (2024): Jurnal Telkommil
Publisher : Pustaka Poltekad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54317/kom.v5i1.445

Abstract

Latihan menembak sangat penting bagi aparat penegak hukum maupun tentara. Saat ini, masih banyak latihan menembak menggunakan cara konvensional dalam perhitungan nilainya. Penelitian ini akan merancang dan mengimplementasikan sistem lesan tembak menggunakan sinar laser sebagai objek pengganti peluru. Pada penelitian ini perhitungan skor otomatis juga diterapkan menggunakan image processing. Perancangan tersebut bertujuan agar mempermudah para penembak dalam melakukan latihan menembak. Dengan menerapkan metode HSV (Hue Saturation Value) colour segmentation, penelitian ini telah berhasil mendeteksi sinar laser. Proses deteksi lingkaran penilaian guna melakukan perhitungan skor akhir pada kertas target telah dijelaskan pada penelitian ini. Hasil pengujian menunjukkan nilai yang cukup akurat dalam mendeteksi sinar laser dan menghitung skor.
Intelligent System for Real-Time Weapon and Combat Equipment Detection Based on Computer Vision for Military Base Security: Rekayasa Keamanan Siber Yudhi Darmawan; Ilyas Hasmi; Robianto Herdana Sukirno
Jurnal Telkommil Vol 6 No 2 (2025): Jurnal Telkommil
Publisher : Pustaka Poltekad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54317/kom.v6i2.599

Abstract

This research proposes an intelligent security system based on computer vision for real-time detection of weapons and military equipment in guard posts and military bases. The primary objective is to strengthen early warning capabilities by automatically identifying objects resembling firearms, knives, or combat gear through surveillance cameras. The system employs convolutional neural networks (CNN) for object classification and detection, integrated with a real-time alert mechanism to notify security personnel when suspicious items are detected. The method includes dataset collection of various weapon and combat equipment images, preprocessing, model training using YOLOv8, and evaluation with precision, recall, and F1-score metrics. Experimental results demonstrate that the system can accurately recognize specific military-related objects with high detection speed, ensuring reliable performance in real-time monitoring scenarios. The findings highlight the potential application of artificial intelligence in enhancing situational awareness and proactive security measures in military environments. This study concludes that the implementation of computer vision-based intelligent detection systems can significantly improve the effectiveness of base and post security operations, providing timely alerts to prevent potential threats
Co-Authors Asep Suryanta dpurwanto Gatut Yulisusianto Ilyas Hasmi jepripanjaitan Maulana Doni Handoyo Robianto Herdana Sukirno Transisma Budiarta