{
      "department": "FAKULTAS SAINS DAN TEKNOLOGI",
      "subjects": [
        "005.36."
      ],
      "eprintid": 77704,
      "thesis_type": "skripsi",
      "date": "2026-06-05",
      "userid": 12460,
      "documents": [
          {
            "language": "id",
            "placement": 1,
            "eprintid": 77704,
            "files": [
                {
                  "hash_type": "MD5",
                  "mtime": "2026-07-29 01:43:28",
                  "datasetid": "document",
                  "fileid": 1853140,
                  "objectid": 1068718,
                  "uri": "http:\/\/digilib.uin-suka.ac.id\/id\/file\/1853140",
                  "mime_type": "application\/pdf",
                  "hash": "3d12eee43a048e9786f3682d0764192c",
                  "filesize": 7012190,
                  "filename": "22106050063_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf"
                }
            ],
            "content": "published",
            "rev_number": 3,
            "uri": "http:\/\/digilib.uin-suka.ac.id\/id\/document\/1068718",
            "main": "22106050063_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf",
            "mime_type": "application\/pdf",
            "docid": 1068718,
            "format": "text",
            "security": "public",
            "pos": 1,
            "formatdesc": "RANCANG BANGUN SISTEM DETEKSI GENDER MENGGUNAKAN MODEL YOLOV8 UNTUK PEMANTAUAN KEAMANAN PADA HUNIAN KHUSUS PUTRI BERBASIS FULL-BODY RECOGNITION"
          },
          {
            "language": "id",
            "placement": 2,
            "eprintid": 77704,
            "files": [
                {
                  "hash_type": "MD5",
                  "mtime": "2026-07-29 01:43:30",
                  "datasetid": "document",
                  "fileid": 1853143,
                  "objectid": 1068719,
                  "uri": "http:\/\/digilib.uin-suka.ac.id\/id\/file\/1853143",
                  "mime_type": "application\/pdf",
                  "hash": "79722eb9ba48513342a52e1f56d433ed",
                  "filesize": 15499867,
                  "filename": "22106050063_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf"
                }
            ],
            "content": "published",
            "rev_number": 3,
            "uri": "http:\/\/digilib.uin-suka.ac.id\/id\/document\/1068719",
            "main": "22106050063_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf",
            "mime_type": "application\/pdf",
            "docid": 1068719,
            "format": "text",
            "security": "validuser",
            "pos": 2,
            "formatdesc": "RANCANG BANGUN SISTEM DETEKSI GENDER MENGGUNAKAN MODEL YOLOV8 UNTUK PEMANTAUAN KEAMANAN PADA HUNIAN KHUSUS PUTRI BERBASIS FULL-BODY RECOGNITION"
          }
      ],
      "rev_number": 10,
      "creators": [
        {
          "name": {
            "lineage": null,
            "given": "NIM.: 22106050063",
            "honourific": null,
            "family": "Ahmad Bidni Musyafa"
          }
        }
      ],
      "dir": "disk0\/00\/07\/77\/04",
      "keywords": "Yolov8n, Deteksi Gender, Full-Body Recognition, Computer Vision, Sistem Real-Time, Early Warning System",
      "lastmod": "2026-07-29 01:52:51",
      "ispublished": "pub",
      "metadata_visibility": "show",
      "date_type": "published",
      "eprint_status": "archive",
      "status_changed": "2026-07-29 01:52:51",
      "datestamp": "2026-07-29 01:52:51",
      "uri": "http:\/\/digilib.uin-suka.ac.id\/id\/eprint\/77704",
      "thesis_name": "other",
      "note": "Nurochman, S.Kom., M.Kom.",
      "full_text_status": "restricted",
      "contact_email": "muh.khabib@uin-suka.ac.id",
      "divisions": [
        "Informatika(S1)"
      ],
      "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.\r\nThe 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.\r\nEvaluation 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.",
      "type": "thesis",
      "title": "RANCANG BANGUN SISTEM DETEKSI GENDER MENGGUNAKAN MODEL YOLOV8 UNTUK PEMANTAUAN KEAMANAN PADA HUNIAN KHUSUS PUTRI BERBASIS FULL-BODY RECOGNITION",
      "institution": "UIN SUNAN KALIJAGA YOGYAKARTA",
      "pages": 127
    }