Klasifikasi Multi-Label Hadis Terjemahan Shahih Bukhari Berdasarkan Kategori Anjuran, Larangan, dan Informasi Menggunakan Metode Extreme Gradient Boosting (XGBoost)
- Tasya Dwi Yanti Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
-
Fitra Kurnia
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
- Nazruddin Safaat Harahap Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Iwan Iskandar Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Reski Mai Candra Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
Hadith is one of the sources of Islamic teachings that contains meanings such as recommendations, prohibitions, and
information. The large number of hadiths makes manual classifications time consuming, therefore an automatic classification method
is needed. This study aims to perform multi-label classifications on translated Shahih Bukhari hadiths using the XGBoost method with
the Binary Relevence approach and hyperparameter tuning using GridSearchCV. the result showed that the implementation of
hyperparameter tuning improved model performance, especially in recall and F1-score values. In addition, the hamming loss value
decraesed from 9.19% to 9.10%, indicating that hyperparameter tuning was able to reduce prediction errors in the model.
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Copyright (c) 2026 Tasya Dwi Yanti, Fitra Kurnia, Nazruddin Safaat Harahap, Iwan Iskandar, Reski Mai Candra
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