Klasifikasi Multi-Label Hadits Shahih Muslim Menggunakan Metode Support Vector Machine (SVM)
- Dinda Lutfiah Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
-
Nazruddin Safaat Harahap
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
- Febi Yanto Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Eka Pandu Cynthia Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
Abstract−In Islamic studies, hadith is a form of text used as a reference in understanding religious teachings and practices. The problem of this research is the complexity of hadith classification because a single text can contain several categories of meaning at once, such as recommendations, prohibitions, and information, so that a single-label approach is not sufficient to represent the contents of the hadith as a whole. This study aims to apply Support Vector Machine (SVM) for multi-label classification of the Indonesian translation of Sahih Muslim Hadith. The dataset consists of 5,362 hadith texts, namely 4,557 training data and 805 test data. The research stages include text preprocessing, TF-IDF feature extraction, One-Vs-Rest classification, and evaluation using precision, recall, F1-score, hamming loss, and subset accuracy. The baseline SVM obtained micro precision of 0.8990, micro recall of 0.8080, micro F1-score of 0.8511, macro precision of 0.6404, macro recall of 0.4446, macro F1-score of 0.4798, hamming loss of 0.1226, and subset accuracy of 0.6696. After hyperparameter tuning using GridSearchCV, the model obtained micro precision of 0.7919, micro recall of 0.8395, micro F1-score of 0.8150, macro precision of 0.5482, macro recall of 0.6341, macro F1-score of 0.5809, hamming loss of 0.1652, and subset accuracy of 0.5764. The results showed that tuning increased macro recall and macro F1-score, but decreased micro F1-score, hamming loss, and subset accuracy. The main contribution lies in the performance evaluation of label imbalance using macro and micro metrics as well as the analysis of dominant features in each category of hadith meaning. Thus, SVM can be used for multi-label classification of Sahih Muslim Hadith, although label imbalance still affects the model performance.
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References
M. R. Athallah and K. M. Lhaksmana, “Hadisth Text Classification Based on Topic Using Convolutional Neural Network ( CNN ) and TF-IDF,” J. Renew. Energy, Electr. Comput. Eng., vol. 5, no. November, pp. 30–36, 2025.
M. Yuslan and A. Bakar, “Klasifikasi Teks Hadis Bukhari Terjemahan Indonesia Menggunakan Recurrent Convolutional Neural Network,” J. JTIIK, vol. 8, no. 5, pp. 907–918, 2021, doi: 10.25126/jtiik.202183750.
C. H. Saputra, “Analisis Bert-Cnn Untuk Klasifikasi Multi-Label Diskusi Keagamaan Dan Asosiasi Dengan Al-Qur’an Dan Hadits,” JTIK (Jurnal Tek. Inform. Kaputama), vol. 12, no. 6, pp. 1415–1424, 2025.
R. A. Munir, A. Voutama, U. S. Karawang, T. Timur, and J. Barat, “Analisis Sentimen Cuitan Di Media Sosial X Tentang Program Makan Bergizi Gratis Dengan Metode NLP,” J. Inform. dan Elektro Terap., vol. 13, no. 3, pp. 589–595, 2025.
R. Aria, K. M. Lhaksmana, and R. Forest, “Klasifikasi Multi-Label Ayat-Ayat Al-Qur ’ an Menggunakan Random Forest dan Word Centrality,” J. Log., vol. 2, no. 2, pp. 9–14, 2024.
R. Kustiawan and M. D. Purbolaksono, “A Multi-label Classification on Topic of Hadith Verses in Indonesian Translation using CART and Bagging,” vol. 6, no. April, pp. 868–875, 2022, doi: 10.30865/mib.v6i2.3787.
I. Akbar et al., “Penerapan Long Short-Term Memory untuk Klasifikasi Multi-Label,” J. JOINTECH, vol. 7, no. 1, pp. 41–54, 2026.
M. T. Hidayat, M. Arifin, and S. Muzid, “Prediction Sentiment Analysis Grab Reviews using SVM Linear Based Streamlit,” Indones. J. Comput. Cybern. Syst., vol. 19, no. 2, pp. 1–12, 2025, doi: 10.22146/ijccs.104924.
L. Pertiwi, “Penerapan Algoritma Text Mining, Steaming Dan Texrank Dalam Peringkasan Bahasa Inggris,” Bull. Multi-Disciplinary Sci. Appl. Technol., vol. 1, no. 3, pp. 100–104, 2022.
Y. T. Handika, S. Defit, and G. W. Nurcahyo, “Text Mining Dalam Membandingkan Metode Naïve Bayes Dengan C.45 Dalam Mengidentifikasi Berita Hoax Pada Media Sosial,” Rang Tek. J., vol. 5, no. 1, pp. 116–123, 2022, doi: 10.31869/rtj.v5i1.2855.
H. Farman, M. A. Hussain, S. Hassan, S. Shaikh, and K. Ali, “Activation function impact on rainfall prediction: comparative insights across ML and DL architectures,” Model. Earth Syst. Environ., vol. 11, no. 6, 2025, doi: 10.1007/s40808-025-02630-6.
R. Meenal, K. Kailash, P. A. Michael, J. J. Joseph, F. T. Josh, and E. Rajasekaran, “Machine learning based smart weather prediction,” Indones. J. Electr. Eng. Comput. Sci., vol. 28, no. 1, pp. 508–515, 2022, doi: 10.11591/ijeecs.v28.i1.pp508-515.
Sopiatul Ulum, R. F. Alifa, P. Rizkika, and C. Rozikin, “Perbandingan Performa Algoritma KNN dan SVM dalam Klasifikasi Kelayakan Air Minum,” Gener. J., vol. 7, no. 2, pp. 141–146, 2023, doi: 10.29407/gj.v7i2.20270.
N. Rini, “Sistem Informasi Manajemen Pengklasifikasian Hadist Shahih Bukhari dan Muslim Menggunakan Algoritma Neural Network,” J. NCBMA, vol. 2, no. june, pp. 1011–1023, 2024.
D. F. Surianto, M. F. B, and M. R. Mulia, “Comparative Analysis of the Performance of Hadith Text Classification Methods : A Case Study with ANN and SVM,” J. JESSI, vol. 05, no. 01, pp. 89–98, 2024.
E. Damayanti, A. V. Vitianingsih, S. Kacung, and D. Cahyono, “Sentiment Analysis of Alfagift Application User Reviews Using Long Short-Term Memory ( LSTM ) and Support Vector Machine ( SVM ) Methods,” J. Decod., vol. 4, no. 2, pp. 509–521, 2024.
R. Z. N. Ahmad, “Perbandingan Performa Random Forest dan Long Short-Term Memory Dalam Klasifikasi Teks Multi Abel Terjemahan Hadist Bukhari,” J. Malcom, vol. 5, no. 3 juli, pp. 862–874, 2025.
H. Mustakim and S. Priyanta, “Aspect-Based Sentiment Analysis of KAI Access Reviews Using NBC and SVM,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 16, no. 2, p. 113, 2022, doi: 10.22146/ijccs.68903.
C. A. Bahri and L. H. Suadaa, “Aspect-Based Sentiment Analysis in Bromo Tengger Semeru National Park Indonesia Based on Google Maps User Reviews,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 17, no. 1, p. 79, 2023, doi: 10.22146/ijccs.77354.
A. S. Aiman and K. M. Lhaksmana, “Topic Classification of Quranic Verses in English Translation,” J. REST, vol. 5, no. 158, pp. 803–809, 2026.
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