<mets:mets OBJID="eprint_33193" 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="2026-10-24T13:38:41Z"><mets:agent ROLE="CUSTODIAN" TYPE="ORGANIZATION"><mets:name>EPrints Universitas Amikom Yogyakarta</mets:name></mets:agent></mets:metsHdr><mets:dmdSec ID="DMD_eprint_33193_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:titleInfo><mods:title>IMPLEMENTASI METODE NAMED ENTITY RECOGNITION&#13;
(NER) DAN KNOWLEDGE GRAPH UNTUK MEMBANGUN&#13;
SISTEM TANYA JAWAB LORE GAME GENSHIN IMPACT</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">NAUFAL ANANDA</mods:namePart><mods:namePart type="family">SURYANTO</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>Struktur cerita dan lore game "Genshin Impact" sangat luas dan kompleks,&#13;
bahkan tersebar dalam ribuan baris teks dialog yang tidak terorganisir. Kondisi ini&#13;
membuat identifikasi hubungan antar entitas menjadi sulit. Tujuan dari penelitian&#13;
ini adalah untuk mengevaluasi efektivitas dan akurasi metode Named Entity&#13;
Recognition (NER) yang berbasis kamus dalam proses pengekstraksi entitas&#13;
otomatis. Selain itu, penelitian ini akan menggunakan struktur Knowledge Graph&#13;
untuk menggambarkan hubungan antara entitas di Region Fontaine dan&#13;
membangun struktur Knowledge Graph. &#13;
Tahapan penelitian dimulai dengan mengumpulkan data menggunakan&#13;
teknik Web Scraping, dilanjutkan dengan proses pembersihan data dan penyusunan&#13;
kamus entitas manual. Ekstraksi informasi dilakukan menggunakan pendekatan&#13;
Dictionary-based NER yang dikombinasikan dengan algoritma co-occurrence&#13;
untuk memetakan hubungan antar entitas ke dalam struktur graf. Hasil penelitian&#13;
menunjukkan bahwa penerapan NER mampu mengidentifikasi entitas karakter,&#13;
lokasi, dan elemen secara sistematis. Pembangunan Knowledge Graph melalui&#13;
analisis sentralitas berhasil merepresentasikan dinamika hubungan narasi, di mana&#13;
tokoh sentral seperti Paimon yang terbukti memiliki konektivitas tertinggi.&#13;
Penelitian ini menunjukkan bahwa pendekatan Knowledge Graph berbasis&#13;
Dictionary-based NER sangat efektif dalam mengubah teks narasi yang rumit&#13;
menjadi representasi pengetahuan yang terstruktur dan akurat. Hasil evaluasi yang&#13;
dilakukan terhadap sampel data dialog menunjukkan tingkat akurasi yang tinggi,&#13;
dengan nilai F1-Score sebesar 98,41%.</mods:abstract><mods:classification authority="lcc">003 Sistem-sistem</mods:classification><mods:originInfo><mods:dateIssued encoding="iso8061">2026-03-02</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_33193"><mets:rightsMD ID="rights_eprint_33193_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:useAndReproduction>
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