{ "date_type": "completed", "creators": [ { "name": { "lineage": null, "given": "Fajar", "family": "Ardillah", "honourific": null }, "id": 2213020084 } ], "contact_email": "digilib@unpkdr.ac.id", "department": "KODEPRODI55201#Teknik Informatika", "date": "2026-07-15", "divisions": [ "sch_psy" ], "ispublished": "unpub", "institution": "Universitas Nusantara PGRI Kediri", "pages": 129, "full_text_status": "restricted", "dir": "disk0\/00\/02\/50\/23", "rev_number": 18, "abstract": "Kegiatan presensi murid pengajian di TPA Setonopande masih dilakukan secara manual sehingga berpotensi menimbulkan kesalahan pencatatan, kehilangan data, dan membutuhkan waktu yang cukup lama dalam proses rekapitulasi kehadiran. Penelitian ini bertujuan untuk merancang dan membangun sistem presensi murid pengajian berbasis pengenalan wajah menggunakan metode Convolutional Neural Network (CNN) dengan arsitektur MobileNetV2, serta mengevaluasi kinerja model menggunakan metrik accuracy, precision, recall, dan F1-score. Dataset penelitian berupa citra wajah murid yang diperoleh secara langsung di TPA Setonopande dan diproses melalui tahap preprocessing sebelum digunakan pada proses pelatihan, validasi, dan pengujian model. \r\nHasil penelitian menunjukkan bahwa sistem presensi berbasis pengenalan wajah berhasil diimplementasikan dalam bentuk aplikasi berbasis website. Berdasarkan hasil evaluasi model, MobileNetV2 memperoleh accuracy sebesar 97% dengan nilai precision, recall, dan F1-score yang menunjukkan kemampuan klasifikasi yang baik dalam mengenali wajah murid pengajian. Selain itu, hasil pengujian fungsional menunjukkan bahwa seluruh fitur sistem berjalan sesuai dengan kebutuhan pengguna. Dengan demikian, penerapan MobileNetV2 mampu memberikan kinerja yang baik dalam mendukung proses presensi murid pengajian secara otomatis serta menghasilkan pencatatan kehadiran yang lebih cepat, akurat, dan terintegrasi. mendukung sistem presensi murid pengajian yang akurat, efisien, dan mudah diterapkan.", "type": "thesis", "status_changed": "2026-08-02 15:09:45", "subjects": [ 457, 459, 461 ], "metadata_visibility": "show", "documents": [ { "language": "id", "files": [ { "hash_type": "MD5", "datasetid": "document", "fileid": 470301, "filesize": 4232215, "mime_type": "application\/pdf", "filename": "RAMA_55201_2213020084.pdf", "mtime": "2026-08-02 14:15:20", "hash": "faf38048f5d325104bff9fe020f56cf0", "uri": "http:\/\/repository.unpkediri.ac.id\/id\/file\/470301", "objectid": 207152 } ], "pos": 1, "formatdesc": "Full text", "mime_type": "application\/pdf", "placement": 1, "content": "accepted", "format": "text", "rev_number": 2, "eprintid": 25023, "security": "validuser", "main": 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"http:\/\/repository.unpkediri.ac.id\/id\/file\/470436", "objectid": 207199 } ], "pos": 8, "formatdesc": "References", "mime_type": "application\/pdf", "placement": 8, "content": "bibliography", "format": "text", "rev_number": 2, "eprintid": 25023, "security": "public", "main": "RAMA_55201_2213020084_0729098903_0713059502_06_ref.pdf", "docid": 207199, "license": "cc_public_domain", "uri": "http:\/\/repository.unpkediri.ac.id\/id\/document\/207199" }, { "language": "id", "files": [ { "hash_type": "MD5", "datasetid": "document", "fileid": 470447, "filesize": 1916237, "mime_type": "application\/pdf", "filename": "RAMA_55201_2213020084_0729098903_0713059502_07_lamp.pdf", "mtime": "2026-08-02 14:50:55", "hash": "6dff6053c4748de429436a46f7a966fd", "uri": "http:\/\/repository.unpkediri.ac.id\/id\/file\/470447", "objectid": 207201 } ], "pos": 9, "formatdesc": "Lampiran", "mime_type": "application\/pdf", "placement": 9, "content": "accepted", "format": "text", "rev_number": 2, "eprintid": 25023, "security": "validuser", "main": "RAMA_55201_2213020084_0729098903_0713059502_07_lamp.pdf", "docid": 207201, "license": "cc_public_domain", "uri": "http:\/\/repository.unpkediri.ac.id\/id\/document\/207201" } ], "uri": "http:\/\/repository.unpkediri.ac.id\/id\/eprint\/25023", "thesis_type": "other", "keywords": "Convolutional Neural Network (CNN), Deep learning , MobileNetV2, Pengenalan Wajah, Presensi Otomatis", "eprint_status": "archive", "contributors": [ { "name": { "lineage": null, "given": "Umi", "family": "Mahdiyah", "honourific": null }, "type": "http:\/\/www.loc.gov\/loc.terms\/relators\/THS", "id": 729098903 }, { "name": { "lineage": null, "given": "Wahyu Cahyo", "family": "Utomo", "honourific": null }, "type": "http:\/\/www.loc.gov\/loc.terms\/relators\/THS", "id": 713059502 } ], "title": "SISTEM PRESENSI DAN PENJADWALAN TUGAS MURID PENGAJIAN BERBASIS PENGENALAN WAJAH MENGGUNAKAN METODE MOBILENETV2", "thesis_name": "dphil", "userid": 65023, "eprintid": 25023, 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