Klasifikasi Multi-Label Hadits Shahih Muslim Menggunakan Metode Support Vector Machine (SVM)
Abstract
Abstrak−Dalam kajian Islam, hadits merupakan salah satu bentuk teks yang digunakan sebagai rujukan dalam memahami ajaran dan
praktik keagamaan. Isi hadits memiliki karakteristik yang beragam sehingga satu hadits dapat memuat lebih dari satu kategori makna
secara bersamaan, seperti anjuran, larangan, dan informasi. Kondisi tersebut menjadikan proses klasifikasi hadits lebih kompleks
karena satu data dapat termasuk ke dalam beberapa kategori sekaligus. Salah satu metode yang cukup banyak digunakan dalam
klasifikasi teks adalah Support Vector Machine (SVM) karena memiliki kemampuan yang baik dalam menangani data berdimensi
tinggi serta menghasilkan performa yang stabil pada proses klasifikasi. Penelitian ini menerapkan metode SVM untuk melakukan
klasifikasi multi-label pada Hadits Shahih Muslim yang dipilih karena merupakan salah satu kitab hadits yang memiliki tingkat
keotentikan tinggi dan banyak digunakan sebagai rujukan dalam kajian keislaman. Dataset yang digunakan terdiri dari 5.362 teks
matan hadits terjemahan Bahasa Indonesia yang dibagi menjadi 85% data latih dan 15% data uji. Hasil penelitian menunjukkan
bahwa model memperoleh nilai micro F1-score sebesar 0,8722, macro F1-score sebesar 0,5547, hamming loss sebesar 0,1048, dan
subset accuracy sebesar 0,7031. Performa terbaik diperoleh pada kategori informasi, sedangkan kategori anjuran dan larangan masih
menunjukkan hasil yang lebih rendah. Hasil tersebut menunjukkan bahwa metode SVM mampu melakukan klasifikasi multi-label
pada teks Hadits Shahih Muslim dengan baik, meskipun distribusi label yang tidak seimbang masih mempengaruhi performa model
pada beberapa kategori.
Kata Kunci: Klasifikasi, Hadits, Multi-Label, Machine Learning, Support Vector Machine
Abstract−In Islamic studies, hadith is a form of text used as a reference in understanding religious teachings and practices. The
content of hadith has diverse characteristics so that a single hadith can contain more than one category of meaning simultaneously,
such as recommendations, prohibitions, and information. This condition makes the hadith classification process more complex
because a single data can be included in several categories simultaneously. One method that is quite widely used in text classification
is the Support Vector Machine (SVM) because it has good capabilities in handling high-dimensional data and produces stable
performance in the classification process. This study applies the SVM method to perform multi-label classification on the Hadith
Sahih Muslim, which was chosen because it is one of the hadith books with a high level of authenticity and is widely used as a
reference in Islamic studies. The dataset used consists of 5,362 texts of the hadith texts translated into Indonesian, divided into 85%
training data and 15% test data. The results showed that the model obtained a micro F1-score of 0.8722, a macro F1-score of 0.5547,
a hamming loss of 0.1048, and a subset accuracy of 0.7031. The best performance was obtained in the information category, while
the recommendations and prohibitions categories still showed lower results. These results indicate that the SVM method is capable of
performing multi-label classification on the text of the Sahih Muslim Hadith well, although the unbalanced label distribution still
affects the model's performance in some categories.
Keywords: Classification, Hadith, Multi-Label, Machine Learning, Support Vector Machine
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