<mets:mets OBJID="eprint_32173" 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="2026-07-21T07:09:35Z"><mets:agent ROLE="CUSTODIAN" TYPE="ORGANIZATION"><mets:name>EPrints Universitas Amikom Yogyakarta</mets:name></mets:agent></mets:metsHdr><mets:dmdSec ID="DMD_eprint_32173_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:titleInfo><mods:title>PENERAPAN TRANFER LEARNING MENGGUNAKAN &#13;
EFFICIENTNETV2 UNTUK &#13;
KLASIFIKASI KESEGARAN &#13;
IKAN &#13;
BERBASIS CITRA</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">Faruq Putra</mods:namePart><mods:namePart type="family">Ahmadi</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>Kesegaran ikan &#13;
merupakan faktor &#13;
penting dalam &#13;
memastikan kualitas &#13;
pangan dan &#13;
keamanan konsumen. Penilaian kesegaran ikan &#13;
umumnya &#13;
dilakukan &#13;
melalui pengamatan visual mata &#13;
ikan, yang &#13;
bersifat &#13;
subjektif dan &#13;
rentan &#13;
terhadap &#13;
inkonsistensi. Evaluasi kesegaran yang &#13;
tidak &#13;
akurat dapat &#13;
mengurangi kualitas &#13;
pangan dan &#13;
menimbulkan risiko &#13;
kesehatan. Oleh &#13;
karena itu, &#13;
diperlukan metode &#13;
otomatis dan &#13;
objektif untuk &#13;
klasifikasi kesegaran ikan. &#13;
Penelitian ini &#13;
menerapkan pendekatan transfer learning &#13;
menggunakan &#13;
arsitektur EfficientNetV2 untuk &#13;
mengklasifikasikan kesegaran ikan &#13;
berdasarkan &#13;
citra mata ikan. &#13;
Dataset diperoleh dari &#13;
Mendeley Data. Model &#13;
EfficientNetV2 yang &#13;
telah dilatih sebelumnya disempurnakan dengan &#13;
memodifikasi lapisan &#13;
klasifikasi &#13;
akhir untuk &#13;
mengklasifikasikan dua &#13;
kategori: segar dan &#13;
tidak &#13;
segar. &#13;
Hasil &#13;
eksper1men menunjukkan bahwa model yang &#13;
dikembangkan mampu &#13;
mencapai akurasi 87,63%, menunjukkan bahwa &#13;
EfficientNetV2 dengan &#13;
transfer &#13;
learning efektif untuk &#13;
klasifikasi kesegaran ikan. &#13;
Sistem ini &#13;
dapat &#13;
dimanfaatkan &#13;
oleh pedagang ikan, &#13;
industri perikanan, dan &#13;
konsumen sebagai alat &#13;
bantu &#13;
untuk &#13;
mengevaluasi kesegaran ikan.</mods:abstract><mods:classification authority="lcc">000 Ilmu komputer, informasi dan pekerjaan umum</mods:classification><mods:classification authority="lcc">004 Pemrosesan data dan ilmu komputer</mods:classification><mods:originInfo><mods:dateIssued encoding="iso8061">2026-02-18</mods:dateIssued></mods:originInfo><mods:originInfo><mods:publisher>Universitas AMIKOM Yogyakarta;Fakultas Ilmu Komputer</mods:publisher></mods:originInfo><mods:genre>Thesis</mods:genre></mets:xmlData></mets:mdWrap></mets:dmdSec><mets:amdSec ID="TMD_eprint_32173"><mets:rightsMD ID="rights_eprint_32173_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:useAndReproduction>
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