Topic Classification of Islamic Question and Answer Using Naïve Bayes and TF-IDF Method

Aura Sukma Andini; Danang Triantoro Murdiansyah; Kemas Muslim Lhaksmana

doi:10.18495/comengapp.v10i3.385

[1] N. Hardifa, K. Lhaksmana and J. Jondri, "Topic Classification of Islamic Question and Answer Using Naive Bayes Classifier," Indonesia Journal on Computing (Indo-JC), 2019.
[2] M. Rahman, N. Samsudin, A. Mustapha and A. Abdullahi, "Comparative Analysis for Topic Classification in Juz Al-Baqarah," Indones. J. Electr. Eng. Comput. Sci, 2018.
[3] M. Afandi, A. Adiwijaya and W. Astuti, "Klasifikasi Multilabel Pada Hadis Bukhari Terjemahan Bahasa Indonesia Menggunakan Mutual Information Dan Support Vector Machine," in eProceedings of Engineering, 2019.
[4] M. Rahman and Y. Akter, "Topic Classification from Text Using Decision Tree, K-NN and Multinomial Naïve Bayes," in 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), 2019.
[5] A. Saurkar, K. Pathare and S. Gode, "An Overview On Web Scraping Techniques And Tools," International Journal on Future Revolution in Computer Science & Communication Engineering, 2018.
[6] J. Han, M. Kamber and J. Pei, Data Mining Concepts and Techniques, San Fransisco, 2012.
[7] Suyanto, Data Mining, Bandung: Penerbit Informatika, 2017.
[8] L. H. Patil and M. Atique, "A novel approach for feature selection method TF-IDF in Document Clustering," in Proceedings of the 2013 3rd IEEE International Advance Computing Conference, 2013.
[9] I. Syuriadi, A. Adiwijaya and W. Astuti, "Klasifikasi Teks Multi Label Pada Hadis Dalam Terjemahan Bahasa Indonesia Berdasarkan Anjuran, Larangan Dan Informasi Menggunakan TF-IDF Dan KNN," in eProceedings of Engineering, 2019.
[10] S. Putra, Y. Sugiarti, G. Dimas, M. Gunawan, T. Sutabri and A. Suryatno, "Document Classification using Naïve Bayes for Indonesian Translation of the Quran," in 2019 7th International Conference on Cyber and IT Service Management (CITSM), 2019.
[11] L. Putri, M. Mubarok and A. Adiwijaya, "Sentimen Pada Ulasan Buku Berbahasa Inggris Menggunakan Information Gain Dan Naïve Bayes," in eProceedings of Engineering, 2017.
[12] R. Pane, M. Mubarok and N. Huda, "Multi-Lable Classification on Topics of Quranic Verses in English Translation Using Multinomial Naive Bayes," in 2018 6th International Conference on Information and Communication Technology (ICoICT), 2018.
[13] S. Al Faraby, E. R. R. Jasin and A. Kusumaningrum, "Classification of hadith into positive suggestion, negative suggestion, and information," Journal of Physics: Conference Series, 2018.
[14] B. Chen, W. Gu and J.Hu, "An Improved Multi-Label Classification Based on Label Ranking and Delicate Boundary SVM," in The 2010 International Joint Conference on Neural Networks (IJCNN), 2010.

Abstract viewed - 880 times
PDF downloaded - 838 times

Affiliations

Aura Sukma Andini
Affiliation not stated

Danang Triantoro Murdiansyah
Telkom University

Kemas Muslim Lhaksmana
Affiliation not stated

Content Top

Topic Classification of Islamic Question and Answer Using Naïve Bayes and TF-IDF Method

Aura Sukma Andini ,
Danang Triantoro Murdiansyah ,
Kemas Muslim Lhaksmana

Vol 10 No 3 (2021)

DOI 10.18495/comengapp.v10i3.385

Submitted: Aug 19, 2021

Published: Oct 1, 2021

Abstract

Information spread through the internet is widely used by people to find anything. One of the most searched information on the internet is information related to Islamic religious knowledge. However, the large amount of information available from various sources makes it difficult for people to find the correct information. Previous researchers have researched this topic, but the dataset used only comes from one source. Therefore, in this study, a classification system for Islamic question and answer topics was built using the Naïve Bayes and TF-IDF methods. This study using 1000 question and answer article data taken from Islamic consultation websites, namely rumahfiqih.com and islamqa.info. The multi-class classification uses five categories which are manually labeled using the category classes on the website. From several test scenarios in this study, the Naïve Bayes classification method using TF-IDF (n-gram level) with a maximum feature of 1000 at a data separation ratio of 70:30 produces the highest accuracy of 81%. The 81% accuracy value was also generated by the SVM classification method, but the difference was in the SVM the highest accuracy value using TF-IDF (word level). It is expected that in the subsequent research will be used more website sources and the use of other classification and feature extraction methods with more optimal value than previous research.