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All Journal Journal of Embedded Systems, Security and Intelligent Systems
Indanasufya, Indanasufya
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Computer Science & IT

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Comparative Analysis of the Performance of Hadith Text Classification Methods: A Case Study with ANN and SVM Surianto, Dewi Fatmarani; Fajar B, Muhammad; Mulia, Musda Rida; Indanasufya, Indanasufya
Journal of Embedded Systems, Security and Intelligent Systems Vol 5, No 1 (2024): March 2024
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v5i1.2942

Abstract

Hadith is the second holy book for Muslims after the Quran, containing instructions from the Prophet Muhammad SAW, and narrated by Ulama / Mufti. As one of the main sources of Islamic teachings, hadith is used to explain and illustrate the teachings of the Quran. This study aims to compare the performance of hadith text classification using Artificial Neural Network (ANN) and Support Vector Machine (SVM) with Hadith Bukhari dataset. The stages include preprocessing, feature extraction with TF-IDF, classification, and evaluation. The evaluation results show different performance between ANN and SVM in two scenarios: with and without stemming. The use of stemming has a significant impact on model performance, reducing word variation and can result in a decrease in accuracy. The SVM model consistently showed higher accuracy than ANN in both scenarios, with the highest accuracy reaching 85% for classification without stemming. This study provides insight into the application of ANN and SVM in hadith text classification, emphasizing the importance of selecting a method that suits the characteristics of the data.
Co-Authors Dewi Fatmarani Surianto Muhammad Fajar B Mulia, Musda Rida