<mets:mets OBJID="eprint_31831" 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="2026-07-05T22:22:00Z"><mets:agent ROLE="CUSTODIAN" TYPE="ORGANIZATION"><mets:name>EPrints Universitas Amikom Yogyakarta</mets:name></mets:agent></mets:metsHdr><mets:dmdSec ID="DMD_eprint_31831_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:titleInfo><mods:title>ANALISIS SENTIMEN DAN PEMETAAN TOPIK KURIKULUM&#13;
MERDEKA MENGGUNAKAN MODEL HYBRID&#13;
INDOROBERTA-BILSTM DENGAN OPTIMASI                 &#13;
RULE-BASED HEURISTIK</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">Hafid Afi</mods:namePart><mods:namePart type="family">Prabowo</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>Masyarakat saat ini aktif menyampaikan opini mengenai berbagai isu&#13;
pendidikan melalui media sosial, khususnya Twitter, termasuk mengenai&#13;
Kurikulum Merdeka. Namun, analisis sentimen konvensional belum memadai&#13;
untuk memahami persepsi publik secara menyeluruh tanpa adanya pemetaan topik&#13;
dan penanganan terhadap ambiguitas serta sarkasme. Penelitian ini bertujuan untuk&#13;
mengembangkan sistem analisis sentimen dan pemetaan topik terkait Kurikulum&#13;
Merdeka dengan mengintegrasikan model hybrid IndoRoBERTa-BiLSTM dan&#13;
optimasi Rule-Based Heuristik. Model IndoRoBERTa digunakan untuk ekstraksi&#13;
fitur berbasis transformer, sedangkan BiLSTM menangani ketergantungan jangka&#13;
panjang dalam teks. Untuk meningkatkan akurasi, diterapkan algoritma Rule-Based&#13;
Heuristik guna memperbaiki klasifikasi sentimen pada teks yang ambigu atau&#13;
sarkastik. Pemetaan topik dilakukan menggunakan pendekatan Keyword-Based&#13;
Classifier dan Non-Negative Matrix Factorization (NMF), yang hasilnya&#13;
divisualisasikan dalam dashboard Streamlit untuk analisis interaktif. Penelitian ini&#13;
menggunakan data tekstual dari Twitter dengan kata kunci “Kurikulum Merdeka”&#13;
periode 2023-2024 dengan total 11.564 data tweet. Hasil penelitian menunjukkan&#13;
bahwa kombinasi model ini mampu menghasilkan akurasi sebesar 0,96, dengan&#13;
nilai precision 0,91, recall 0,97, dan F1-score 0,93. Integrasi Rule-Based Heuristik&#13;
terbukti efektif dalam menangani kesalahan klasifikasi akibat sarkasme, sementara&#13;
pemetaan topik berhasil mengidentifikasi isu dominan yang relevan dengan&#13;
kebijakan Kurikulum Merdeka di Indonesia.</mods:abstract><mods:classification authority="lcc">000 Ilmu komputer, informasi dan pekerjaan umum</mods:classification><mods:originInfo><mods:dateIssued encoding="iso8061">2026-01-22</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_31831"><mets:rightsMD ID="rights_eprint_31831_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:useAndReproduction>
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