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      "date": "2026-03-02",
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            "given": "NAUFAL ANANDA",
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      "keywords": "Genshin Impact, Dictionary-based NER, Knowledge Graph,\r\nEvaluasi Performa, Text Mining, Performance Evaluation.",
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      "abstract": "Struktur cerita dan lore game \"Genshin Impact\" sangat luas dan kompleks,\r\nbahkan tersebar dalam ribuan baris teks dialog yang tidak terorganisir. Kondisi ini\r\nmembuat identifikasi hubungan antar entitas menjadi sulit. Tujuan dari penelitian\r\nini adalah untuk mengevaluasi efektivitas dan akurasi metode Named Entity\r\nRecognition (NER) yang berbasis kamus dalam proses pengekstraksi entitas\r\notomatis. Selain itu, penelitian ini akan menggunakan struktur Knowledge Graph\r\nuntuk menggambarkan hubungan antara entitas di Region Fontaine dan\r\nmembangun struktur Knowledge Graph. \r\nTahapan penelitian dimulai dengan mengumpulkan data menggunakan\r\nteknik Web Scraping, dilanjutkan dengan proses pembersihan data dan penyusunan\r\nkamus entitas manual. Ekstraksi informasi dilakukan menggunakan pendekatan\r\nDictionary-based NER yang dikombinasikan dengan algoritma co-occurrence\r\nuntuk memetakan hubungan antar entitas ke dalam struktur graf. Hasil penelitian\r\nmenunjukkan bahwa penerapan NER mampu mengidentifikasi entitas karakter,\r\nlokasi, dan elemen secara sistematis. Pembangunan Knowledge Graph melalui\r\nanalisis sentralitas berhasil merepresentasikan dinamika hubungan narasi, di mana\r\ntokoh sentral seperti Paimon yang terbukti memiliki konektivitas tertinggi.\r\nPenelitian ini menunjukkan bahwa pendekatan Knowledge Graph berbasis\r\nDictionary-based NER sangat efektif dalam mengubah teks narasi yang rumit\r\nmenjadi representasi pengetahuan yang terstruktur dan akurat. Hasil evaluasi yang\r\ndilakukan terhadap sampel data dialog menunjukkan tingkat akurasi yang tinggi,\r\ndengan nilai F1-Score sebesar 98,41%.",
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