<mets:mets OBJID="eprint_31982" 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="2026-07-21T07:08:33Z"><mets:agent ROLE="CUSTODIAN" TYPE="ORGANIZATION"><mets:name>EPrints Universitas Amikom Yogyakarta</mets:name></mets:agent></mets:metsHdr><mets:dmdSec ID="DMD_eprint_31982_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:titleInfo><mods:title>ANALISIS SENTIMENT TERHADAP APLIKASI BCA MOBILE&#13;
MENGGUNAKAN METODE NAÏVE BAYES</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">M. Habibi</mods:namePart><mods:namePart type="family">Amin</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>Penelitian ini bertujuan untuk menganalisis sentimen pengguna terhadap&#13;
aplikasi BCA Mobile berdasarkan ulasan yang tersedia di Google Play Store.&#13;
Seiring dengan meningkatnya penggunaan layanan perbankan digital, opini dan&#13;
pengalaman pengguna menjadi faktor penting dalam mengevaluasi kualitas layanan&#13;
aplikasi. Namun, banyaknya jumlah ulasan membuat proses analisis secara manual&#13;
menjadi tidak efisien. Oleh karena itu, diperlukan metode otomatis untuk&#13;
mengklasifikasikan sentimen pengguna secara cepat dan akurat.&#13;
Metode yang digunakan dalam penelitian ini adalah algoritma Naïve Bayes&#13;
dengan pembobotan TF-IDF sebagai representasi fitur teks. Dataset yang&#13;
digunakan sebanyak 509.726 ulasan berbahasa Indonesia yang dikumpulkan&#13;
melalui proses scraping dari Google Play Store. Data kemudian melalui tahap&#13;
preprocessing yang meliputi cleaning, case folding, stopword removal, tokenizing,&#13;
dan stemming. Pelabelan sentimen dilakukan berdasarkan rating, yaitu positif (4–&#13;
5), netral (3), dan negatif (1–2). Dataset dibagi menjadi 80% data latih dan 20%&#13;
data uji.&#13;
Hasil penelitian menunjukkan bahwa mayoritas ulasan bersentimen positif&#13;
sebesar 72,29%, diikuti negatif 21,92% dan netral 5,80%. Model Naïve Bayes&#13;
menghasilkan akurasi sebesar ±85%, dengan performa sangat baik pada kelas&#13;
positif dan cukup baik pada kelas negatif, namun kurang optimal pada kelas netral&#13;
karena ketidakseimbangan data. Penelitian ini dapat dimanfaatkan oleh pihak&#13;
pengembang untuk meningkatkan kualitas layanan aplikasi serta menjadi referensi&#13;
bagi penelitian selanjutnya dalam bidang analisis sentimen.</mods:abstract><mods:classification authority="lcc">000 Ilmu komputer, informasi dan pekerjaan umum</mods:classification><mods:originInfo><mods:dateIssued encoding="iso8061">2026-02-26</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_31982"><mets:rightsMD ID="rights_eprint_31982_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:useAndReproduction>
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