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      "abstract": "Masyarakat Muslim awam mengalami kesulitan dalam memahami tafsir\r\nAl-Qur’an klasik, khususnyaTafsir Ibn Kathir, akibat kompleksitas bahasa Arab\r\nklasik, besarnya volume teks, serta rendahnya literasi membaca di era digital.\r\nKondisi ini membatasi akses terhadap pengetahuan keislaman yang kredibel dan\r\nmemperlebar kesenjangan antara sumber rujukan klasik dan kebutuhan informasi\r\nmasyarakat modern yang menuntut kecepatan serta kemudahan akses.\r\nPenelitian ini mengembangkan sistem penjawab tafsir Al-Qur’an berbasis\r\nRetrieval-Augmented Generation(RAG) dengan pendekatanHybrid\r\nSparse-Dense Retrieval. Metode yang digunakan mengombinasikan algoritma\r\nBM25 untuk pencocokan leksikal dan model embedding E5 Large Multilingual\r\nuntuk menangkap kesamaan semantik lintas bahasa, dengan model bahasa Llama\r\n3.2 3B sebagai penyusun jawaban. Evaluasi sistem dilakukan menggunakan\r\nmetrik Mean Average Precision (MAP), Normalized Discounted Cumulative Gain\r\n(nDCG), BERTScore, serta pengukuran latensi respons.\r\nHasil penelitian menunjukkan bahwa konfigurasi hybrid dengan bobot\r\nseimbang (α= 0,5) menghasilkan performa terbaik dengan nilai nDCG@10\r\nsebesar 0,94 (leksikal) dan 0,99 (semantik). Sistem mampu menghasilkan\r\njawaban yang relevan dengan sumber tafsir dan latensi rata-rata 5,93 detik.\r\nPenelitian ini berkontribusi pada penerapan kecerdasan buatan di bidang studi\r\nkeislaman dan dapat dimanfaatkan oleh institusi pendidikan serta masyarakat\r\numum untuk meningkatkan literasi digital keagamaan.",
      "keywords": "Hybrid Retrieval, Retrieval-Augmented Generation, Sistem Tanya\r\nJawab Tafsir Al-Qur’an, Tafsir Ibn Kathir, NLP Keislaman, Qur’anic Tafsir\r\nQuestion Answering, Islamic NLP.",
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      "institution": "Universitas AMIKOM Yogyakarta",
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