eprintid: 77704
rev_number: 10
eprint_status: archive
userid: 12460
dir: disk0/00/07/77/04
datestamp: 2026-07-29 01:52:51
lastmod: 2026-07-29 01:52:51
status_changed: 2026-07-29 01:52:51
type: thesis
metadata_visibility: show
contact_email: muh.khabib@uin-suka.ac.id
creators_name: Ahmad Bidni Musyafa, NIM.: 22106050063
title: RANCANG BANGUN SISTEM DETEKSI GENDER MENGGUNAKAN MODEL YOLOV8 UNTUK PEMANTAUAN KEAMANAN PADA HUNIAN KHUSUS PUTRI BERBASIS FULL-BODY RECOGNITION
ispublished: pub
subjects: 005.36.
divisions: Informatika(S1)
full_text_status: restricted
keywords: Yolov8n, Deteksi Gender, Full-Body Recognition, Computer Vision, Sistem Real-Time, Early Warning System
note: Nurochman, S.Kom., M.Kom.
abstract: Manual security monitoring in female-only dormitories has limitations and is prone to negligence. To address this issue, this study designs a gender detection system as a proactive early warning system. The system utilizes deep learning-based computer vision technology to perform automatic and Real-time security monitoring.
The system was developed using a full-body recognition approach with the lightweight YOLOv8n architecture, integrated with automatic notifications via Telegram. The dataset consists of simulated CCTV images (semi top-down angles, lighting variations, and mask/helmet occlusion scenarios) as well as secondary data. The entire data was divided into 70% training data, 20% validation data, and 10% testing data using a transfer learning strategy.
Evaluation results show the model achieved an mAP50 of 92.90%, precision of 90.65%, recall of 89.01%, and F1-score of 89.82%. The system proved efficient, operating stably at an average speed of 20 FPS on purely CPU-based devices without GPU acceleration. Field testing demonstrated that the full-body recognition approach remains robust in detecting subjects under occlusion. In conclusion, the YOLOv8n model provides an optimal balance between high accuracy, local computational efficiency, and operational reliability.
date: 2026-06-05
date_type: published
pages: 127
institution: UIN SUNAN KALIJAGA YOGYAKARTA
department: FAKULTAS SAINS DAN TEKNOLOGI
thesis_type: skripsi
thesis_name: other
citation:   Ahmad Bidni Musyafa, NIM.: 22106050063  (2026) RANCANG BANGUN SISTEM DETEKSI GENDER MENGGUNAKAN MODEL YOLOV8 UNTUK PEMANTAUAN KEAMANAN PADA HUNIAN KHUSUS PUTRI BERBASIS FULL-BODY RECOGNITION.  Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.   
document_url: https://digilib.uin-suka.ac.id/id/eprint/77704/1/22106050063_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf
document_url: https://digilib.uin-suka.ac.id/id/eprint/77704/2/22106050063_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf