Thematic Textual Hadith Classification: An Experiment in Rapidminer using Support Vector Machine (SVM) and Naïve Bayes Algorithm

Abstract

There are many existing problems in Hadith studies trending in the study field. The issues are changeable from the digitalization of the Hadith data to an exact case study of estimation of narrators' chain for a particular Hadith. However, in this paper, we are not concentrating on the such learning of estimating, confirming or authenticating a Hadith. It focuses more on the data mining use to the Hadith dataset. We put on the Hadith dataset onto one of machine learning tools which is text classification. The Hadith dataset is put into experiment for Hadith textual classification. It concentrates on the thematic classification based on the themes and words occurrences from the Hadith text (matn). The Hadith textual classification does not trace on the hukm and position or class of Hadith. This research does not categorize the Hadith into hukm Sahih, Hasan, Dhaif, or Mawdhoo'. However, the Hadith thematic dataset of this study use only Hadith from Sahih Bukhari, where all Hadith in the Book is categorized as sahih by Imam Al-Bukhari. The classification for this thematic Hadith dataset is implemented using Rapidminer, a machine learning tool using Naïve Bayes and Support Vector Machine (SVM) methods. From the results, the different value of accuracy for both SVM and Naïve Bayes Algorithm was 2.4%. The Naïve Bayes Algorithm displayed better result comparing to SVM. We believe that the result could be better by improving the data, algorithms, algorithm tuning or ensemble methods for the future experiments.

Klasifikasi Mazhab Menggunakan Metode Naïve Bayes (Studi Kasus: Salat)

“…Penelitian oleh Yusof, dkk (2020), mengklasifikasikan teks hadis berdasarkan tema dan kata-kata dari teks Hadits dengan menerapkan algoritma Naïve Bayes dan Support Vector Machine (SVM) [16]. Penelitian tersebut menunjukkan bahwa Naïve Bayes memberikan nilai akurasi 2,4% lebih baik dibandingkan dengan SVM.…”

Classification of the Final Project Utilized a Modified Naïve Bayes Algorithm

“…Generally, classification on the naïve Bayes method resulting in 2 targets, anyway in the adaptation on the case study in the real condition, it could be developed and resulting in 3 targets [25]. Based on the results of research comparing the performance of the Naïve Bayes Method with other methods, it shows that the performance of the Naïve Bayes Method is much better [26] [27] [28] Besides that, the naïve Bayes method is already replicated in any disciplined study [29] to settled the classification data problem. And also, the calculation formula of the probability value is applicable to the more accurate result.…”