<mets:mets OBJID="eprint_23212" 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="2026-09-03T08:35:17Z"><mets:agent ROLE="CUSTODIAN" TYPE="ORGANIZATION"><mets:name>Repository Universitas Nusantara PGRI Kediri</mets:name></mets:agent></mets:metsHdr><mets:dmdSec ID="DMD_eprint_23212_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:titleInfo><mods:title>ANALISIS WAKTU OPTIMASI VALIDASI SILANG PENCARIAN ACAK&#13;
BERTAHAP PADA MODEL PEMBELAJARAN MESIN</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">Alief Cahyo Utomo</mods:namePart><mods:namePart type="family">Alief Cahyo Utomo</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>Penelitian ini bertujuan untuk menganalisis efektivitas&#13;
metode Halving Random Search Cross Validation sebagai alternatif optimasi&#13;
hyperparameter pada model machine learning dibandingkan dengan Grid&#13;
Search Cross Validation dan Random Search Cross Validation. Dataset yang&#13;
digunakan adalah Internet Service Churn dengan empat algoritma: KNN,&#13;
Decision Tree, SVM, dan Gaussian Naive Bayes. Proses pengujian melibatkan&#13;
10-fold cross validation dan pengulangan tiga kali untuk memastikan validitas&#13;
hasil.&#13;
Hasil eksperimen menunjukkan bahwa Halving Random Search Cross&#13;
Validation mampu mencapai performa akurasi, presisi, dan recall yang&#13;
kompetitif (selisih &lt; 0,5%) dibandingkan Grid Search pada sebagian besar&#13;
model, dengan penghematan waktu komputasi hingga 62–74% pada KNN,&#13;
Decision Tree, dan SVM. Namun, pada Gaussian Naive Bayes dengan ruang&#13;
hyperparameter kecil, metode ini lebih lambat karena overhead successive&#13;
halving. Random Search menunjukkan kecepatan tinggi tetapi kurang stabil&#13;
pada SVM dan Gaussian Naive Bayes.&#13;
Kesimpulan penelitian menyatakan bahwa Halving Random Search Cross&#13;
Validation adalah metode paling seimbang untuk kasus bisnis seperti prediksi&#13;
churn, dengan rekomendasi penerapan pada model kompleks dan&#13;
pengembangan lanjutan menggunakan Hyperband atau Bayesian Optimization.</mods:abstract><mods:classification authority="lcc">459 Computer science</mods:classification><mods:classification authority="lcc">461 Information systems</mods:classification><mods:originInfo><mods:dateIssued encoding="iso8061">2025-12-29</mods:dateIssued></mods:originInfo><mods:originInfo><mods:publisher>Universitas Nusantara PGRI Kediri;KODEPRODI57201#Sistem Informasi</mods:publisher></mods:originInfo><mods:genre>Thesis</mods:genre></mets:xmlData></mets:mdWrap></mets:dmdSec><mets:amdSec ID="TMD_eprint_23212"><mets:rightsMD ID="rights_eprint_23212_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:useAndReproduction>
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