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All Journal JURNAL SAINS PERTANIAN EQUATOR Jurnal Ilmiah Pangabdhi Pendas : Jurnah Ilmiah Pendidikan Dasar JURNAL PENDIDIKAN TAMBUSAI Journal of Computer System and Informatics (JoSYC) Jurnal At-Taujih : Bimbingan dan Konseling Islam Jurnal Pengabdian Masyarakat Madani Fruitset Sains : Jurnal Pertanian Agroteknologi Jurnal Minfo Polgan (JMP) Indonesian Research Journal on Education BAKIRA: Jurnal Pengabdian Kepada Masyarakat Bina Bahari : Jurnal Pengabdian Kepada Masyarakat Hudan Linnaas: Jurnal Ilmu Sosial dan Humaniora
Sulistia Ningsih, Sulistia
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Journal : Journal of Computer System and Informatics (JoSYC)

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Pengaruh Penyeimbangan Data Pada Klasifikasi Terjemahan Al-Quran Dengan Metode Naïve Bayes dan Long Short Term Memory Ningsih, Sulistia; Safaat, Nazruddin; Agustian, Surya; Yusra, Yusra; Cynthia, Eka Pandu
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5181

Abstract

The Al Qur'an is a holy book of Muslims which is a guide to life for all mankind. Studying and understanding the translation of the Al-Quran is not easy, one way that can be done is to classify the translation of Al-Quran verses into existing topics. This research uses Naïve Bayes and LSTM methods in the classification process. The data used comes from translation data of the Al-Quran in Indonesian which has been labeled based on multi-class classification. One of the main problems faced is data imbalance. To overcome this problem, data balancing, text preprocessing, feature construction and feature extraction processes were carried out using the Bag of Words (BoW) and TF.IDF techniques. The research results indicate that the most optimal Naïve Bayes model achieved an average accuracy of 55.39% on test data from juz 30, 61.59% on test data from juz 10-20, and 59.53% on test data from juz 25-28. Meanwhile, the most optimal LSTM model yielded an accuracy of 58.02% on test data from juz 30, 59.64% on test data from juz 10-20, and 58.59% on test data from juz 25-28. The main aim of this research is to improve classification performance and compare the accuracy between naïve Bayes and lstm.
Co-Authors Adwiyani, Pustika Agneska, Widya Agung Iman, Sabda Agustian, Surya Al Farisi, Muhammad Salman Alifiya Herwitarahman Anindya, Ajeng Dara Annisa, Baaqy Amri Azhari Azhari Ceri, Bunia DAHLIA WULAN SARI Didik Didik DJOKO KRISTIANTO Dongoran, Anita Edy Syahputra Efriyani, Sari Eka Pandu Cynthia, Eka Pandu Elliska Murni Harfinda Fachria, Rizqy Fadilasih, Tawazalul Fadjar Harimurti Farazwati, Adinda Gaffar, Andi Widya Mufila Gunadi Gunadi Handayani, Nonia Kusuma, Eka Widiawati Wijaya Mahmudi Manurung, Widia Lestari Maref, Hikmal Gustriora Meiratania, Marisa Muliandari, Nadya Nadia, Shevina Putri Nazruddin Safaat Nengsih, Nengsih Pangestu, Gigih Budhiawan Paturrahman, Aprian Prasetyo, Riko Pratiwi, Destri Aulia Putri, Aulya Herliyana Qurniasih, Ilmawati Rahayu, Ajeng RAHAYU, WIDYA Rahmat Hidayat Ramadhani, Amelia Arum Ramadona, Bunga Okta Ramdan Satra Ria Andriana Rustianti, Ida Safriadi Safriadi, Safriadi Saloko, Yogo Tri Sari, Anisa Juliana Sembiring, Zamaludin SEPTIANA SEPTIANA Sigit Normagiat Simarmata, Lisa Afrilia Sinurat, Ester Siregar, Achis Martua Sitorus, Dermawati Suari, Silvia Dian Syahirah, Dhiyan Tia Nuraya Tohari Tohari TRIS HARIS RAMADHAN Wahyu Setiyadi Was’an, Guruh Herman Yanti, Alta Dama Yopi Yusra, Yusra