<mets:mets OBJID="eprint_32629" 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="2026-09-14T18:38:32Z"><mets:agent ROLE="CUSTODIAN" TYPE="ORGANIZATION"><mets:name>EPrints Universitas Amikom Yogyakarta</mets:name></mets:agent></mets:metsHdr><mets:dmdSec ID="DMD_eprint_32629_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:titleInfo><mods:title>KLASIFIKASI KUALITAS SARANG BURUNG WALET &#13;
MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL&#13;
NETWORK (CNN)</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">Hasni</mods:namePart><mods:namePart type="family">Anas</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>Kualitas sarang burung walet merupakan faktor penting dalam menentukan&#13;
nilai jual, sehingga diperlukan sistem klasifikasi yang akurat dan otomatis.&#13;
Penelitian ini bertujuan mengembangkan model klasifikasi mutu sarang burung&#13;
walet menggunakan empat arsitektur Convolutional Neural Network (CNN), yaitu&#13;
MobileNetV2, VGG16, ResNet50, dan EfficientNetV2B1 dengan pendekatan&#13;
transfer learning. Dataset yang digunakan terdiri atas 3.406 citra sarang burung&#13;
walet yang diperoleh langsung dari petani, kemudian diproses melalui augmentasi&#13;
agresif dan penghapusan latar belakang guna menyoroti objek utama. Data dibagi&#13;
ke dalam data pelatihan sebanyak 2.723 citra dan data validasi sebanyak 683 citra,&#13;
mencakup tiga kelas kualitas: tinggi, sedang, dan rendah. Setiap model dilatih&#13;
dalam dua tahap, yaitu frozen base dan fine-tuning. Hasil pengujian menunjukkan&#13;
bahwa semua model mengalami peningkatan performa setelah tahap fine-tuning,&#13;
dengan performa terbaik diperoleh pada arsitektur ResNet50 yang mencapai akurasi&#13;
sebesar 98%, diikuti oleh MobileNetV2 sebesar 97%, VGG16 sebesar 95%, dan&#13;
EfficientNetV2B1 sebesar 94%. Proses augmentasi data terbukti meningkatkan&#13;
kemampuan generalisasi model terhadap variasi citra. Penelitian ini berkontribusi&#13;
dalam pengembangan sistem klasifikasi mutu sarang burung walet berbasis citra&#13;
digital dengan akurasi tinggi, yang berpotensi diimplementasikan pada industri&#13;
untuk mendukung proses penyortiran secara objektif, efisien, dan konsisten.</mods:abstract><mods:classification authority="lcc">005 Pemrograman komputer, program dan data</mods:classification><mods:originInfo><mods:dateIssued encoding="iso8061">2026-02-02</mods:dateIssued></mods:originInfo><mods:originInfo><mods:publisher>Universitas AMIKOM Yogyakarta;PJJ Magister Informatika</mods:publisher></mods:originInfo><mods:genre>Thesis</mods:genre></mets:xmlData></mets:mdWrap></mets:dmdSec><mets:amdSec ID="TMD_eprint_32629"><mets:rightsMD ID="rights_eprint_32629_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:useAndReproduction>
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