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All Journal OKTAL : Jurnal Ilmu Komputer dan Sains Buletin Ilmiah Ilmu Komputer dan Multimedia (BIIKMA)
Andang Ramadhan
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Astronomy Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Social Sciences

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Kasus Implementasi pada PT Nusantara Manufaktur Samsoni; Andang Ramadhan; Faried Brian Prawira; Firda Auliatunnajah; Rauzan Habas; Slamet Supriyadi
Buletin Ilmiah Ilmu Komputer dan Multimedia Vol 1 No 6 (2024): Buletin Ilmiah Ilmu Komputer dan Multimedia (BIIKMA)
Publisher : Shofanah Media Berkah

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Abstract

Sistem Informasi Manajemen (SIM) adalah teknologi yang digunakan untuk mendukung proses manajerial dalam organisasi. Penelitian ini membahas implementasi SIM di PT Nusantara Manufaktur, sebuah perusahaan manufaktur menengah yang mengalami peningkatan efisiensi operasional dan pengambilan keputusan yang lebih baik setelah mengadopsi SIM. Studi kasus ini memberikan wawasan mendalam tentang proses implementasi, tantangan yang dihadapi, dan manfaat yang diperoleh.
Perancangan Sistem Sederhana Deteksi Helm Sepeda Motor dengan Metode Convolutional Neural Network Dan Algoritma YOLO v3 Ibnu Hajar; Ahmad Rifa`i; Ilham Fauzi Alam; Andang Ramadhan; Perani Rosyani
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 07 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

Traffic accidents are one of the most common causes of death in the world, and helmet use has been effective proved in reducing the risk of head injuries for motorcyclists. Therefore, it is crucial to ensure that motorcyclists always wear helmets while riding. One method to detect helmet use is by utilizing object recognition technology based on Convolutional Neural Networks (CNN). This study focus on design and implement a simple helmet detection system using CNN methods and the YOLO v3 model for real-time detection. The system is expected to accurately detect helmet use by riders. In this research, the YOLO v3 model is trained using the COCO dataset, which includes various images with diverse contexts. The results of this implementation show that the system can detect helmet use effectively under various lighting conditions and environments. This demonstrates the potential use of a helmet detection system based on CNN and YOLO in enhancing riding safety.
Co-Authors Ahmad Rifa`i Faried Brian Prawira Firda Auliatunnajah Ibnu Hajar Ilham Fauzi Alam Perani Rosyani Rauzan Habas Samsoni Slamet Supriyadi