BELL, NABILA ABIGAIL DE (2023) Perancangan Sistem Keamanan Untuk Restricted Area dengan Image Detection Menggunakan Algoritma YOLOv8. S2 thesis, Universitas Mercu Buana - Menteng.
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Abstract
Dalam dunia modern yang penuh dinamika teknologi, keamanan di lokasi konstruksi menjadi esensial. Sistem pemantauan otomatis dengan kecerdasan buatan dan computer vision telah berkembang pesat. Namun, banyak perusahaan masih mengandalkan sistem manual yang kurang efisien. Solusi terkini adalah deteksi objek menggunakan kamera pengawas video, dengan YOLOv8 sebagai pilihan utama. Penelitian ini memperkenalkan penyempurnaan dengan mengadopsi YOLOv8 untuk mendeteksi pekerja di area terlarang. Hasilnya menunjukkan peningkatan presisi dan sensitivitas. Implementasi ini diharapkan memberikan kontribusi positif dalam meningkatkan keamanan di lokasi konstruksi. Penelitian ini berhasil menghasilkan dataset pekerja di lokasi konstruksi. Pengujian menggunakan YOLOv8 menghasilkan akurasi deteksi pekerja mencapai 99%. Sistem ini juga berhasil dalam pengiriman data ke MQTT, memungkinkan implementasi dengan teknologi lain. Rekomendasi penelitian ini adalah menerapkan sistem deteksi objek pekerja di area terlarang menggunakan YOLOv8 untuk meningkatkan pengawasan dan evaluasi bulanan pekerjaan konstruksi. In the modern world filled with technological dynamism, safety at construction sites is paramount. Automated monitoring systems utilizing artificial intelligence and computer vision have seen rapid advancements. However, many companies still rely on manual systems that are less efficient. The current solution involves object detection using surveillance cameras, with YOLOv8 as the preferred choice. This study introduces refinements by adopting YOLOv8 for detecting workers in restricted areas. The results show improved precision and sensitivity. This implementation is expected to make a positive contribution to enhancing safety at construction sites. This study successfully generated a dataset of construction site workers. Testing using YOLOv8 yielded a worker detection accuracy of up to 99%. The system also succeeded in sending data via MQTT, allowing implementation with other technologies. The recommendation from this study is to implement a worker object detection system in restricted areas using YOLOv8 to enhance monitoring and monthly evaluation of construction work.
| Item Type: | Thesis (S2) |
|---|---|
| NIM/NIDN Creators: | 55420110017 |
| Uncontrolled Keywords: | YOLOv8, Image Detection, Internet of Things, MQTT. YOLOv8, Image Detection, Internet of Things, MQTT. |
| Subjects: | 600 Technology/Teknologi > 620 Engineering and Applied Operations/Ilmu Teknik dan operasi Terapan > 621 Applied Physics/Fisika terapan |
| Divisions: | Pascasarjana > Magister Teknik Elektro |
| Depositing User: | SILMI KAFFA MARISKA |
| Date Deposited: | 24 Feb 2024 02:42 |
| Last Modified: | 24 Feb 2024 02:42 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/86491 |
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