<mets:mets OBJID="eprint_5222" LABEL="Eprints Item" xsi:schemaLocation="http://www.loc.gov/METS/ http://www.loc.gov/standards/mets/mets.xsd http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-3.xsd" xmlns:mets="http://www.loc.gov/METS/" xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><mets:metsHdr CREATEDATE="2026-06-25T13:05:26Z"><mets:agent ROLE="CUSTODIAN" TYPE="ORGANIZATION"><mets:name>Repository Universitas Bojonegoro</mets:name></mets:agent></mets:metsHdr><mets:dmdSec ID="DMD_eprint_5222_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:titleInfo><mods:title>Laporan Akhir Penelitian Internal Dosen : Smart Camera for Personal Protective Equipment (PPE) Usage Detection Using the YOLO-NAS Method for Occupational Safety and Health</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">Ashari</mods:namePart><mods:namePart type="family">Faisal Ashari</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart type="given">Stighfarrinata</mods:namePart><mods:namePart type="family">Rizky Stighfarrinata</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart type="given">Zahra</mods:namePart><mods:namePart type="family">Nayla Farikha Zahra</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>Penelitian ini bertujuan untuk mengembangkan sistem Intelligent Quality Control berbasis computer vision dengan arsitektur Neural Architecture Search (NAS) untuk mendeteksi kelengkapan penggunaan Alat Pelindung Diri (APD) dalam industri. Penelitian ini penting mengingat tingginya angka kecelakaan dan penyakit akibat kerja, baik secara global maupun nasional. Fokus utama penelitian adalah merancang dan mengimplementasikan sistem deteksi tiga jenis APD, yaitu helm, rompi, dan sepatu safety, melalui input gambar dari webcam. Dataset APD diperoleh dari Roboflow, dianotasi, dan dilatih menggunakan framework SuperGradients di Google Colab untuk menghasilkan model deteksi dengan performa optimal. Luaran dari penelitian ini berupa sistem deteksi APD real-time dengan akurasi tinggi, ditunjukkan melalui hasil precision 97,16%, recall 98,35%, F1- score 97,25%, dan mAP 96,07%, serta implementasi berbasis webcam dengan F1- score 94,84%. Hasil ini menunjukkan kemampuan NAS dalam menghasilkan model yang handal dan efektif. Penelitian ini memiliki implikasi positif dalam mendukung keselamatan kerja melalui pemanfaatan teknologi kecerdasan buatan. Diharapkan sistem ini dapat berkontribusi pada pengurangan risiko kecelakaan kerja serta meningkatkan kepatuhan penggunaan APD di lingkungan industri.</mods:abstract><mods:classification authority="lcc">Prodi Teknik Industri</mods:classification><mods:originInfo><mods:dateIssued encoding="iso8601">2026-01-01</mods:dateIssued></mods:originInfo><mods:originInfo><mods:publisher>LPPM UNIVERSITAS BOJONEGORO</mods:publisher></mods:originInfo><mods:genre>Other</mods:genre></mets:xmlData></mets:mdWrap></mets:dmdSec><mets:amdSec ID="TMD_eprint_5222"><mets:rightsMD ID="rights_eprint_5222_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:useAndReproduction>
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