Integrasi AdaBoost dengan Naive Bayes untuk Klasifikasi Kualitas Hafalan Tematik Santri PPICeLKISI
DOI:
https://doi.org/10.26905/jisad.v3i2.16138Keywords:
klasifikasi; , naive bayes; , adaboost; , hafalan tematik; , pondok pesantrenAbstract
Islamic boarding schools (pondok pesantren) play a vital role in shaping the character and spiritual intelligence of students (santri), one of which is through thematic memorization programs. The quality of thematic memorization needs to be evaluated objectively to enhance learning effectiveness. This study aims to classify the quality of thematic memorization among students at the Islamic Center eLKISI Boarding School using the Naive Bayes method enhanced by the Adaboost boosting algorithm. Naive Bayes is known as a simple yet effective classification method, while Adaboost improves accuracy by combining multiple weak learners into a strong model. The data used were obtained from the memorization evaluation results of several students based on multiple assessment attributes.
This research employed a quantitative approach with stages including preprocessing, model training, and performance evaluation. The experimental results show that the application of Adaboost to Naive Bayes significantly improves accuracy compared to the standard Naive Bayes. The conclusion of this study is that the implementation of Adaboost effectively optimizes the performance of Naive Bayes in classifying the quality of thematic memorization and can serve as a reference for developing automated evaluation systems based on machine learning.
Downloads
References
[1] Andriani, R., & Hartati, S. (2023). "Kolaborasi Naïve Bayes dan AdaBoost dalam Klasifikasi Bakteri E.coli." Jurnal SIMTEK, 15(2), 45–51. https://www.ojs.stmikplk.ac.id/index.php/simtek/article/view/756
[2] Anwar, M. F., & Rochmawati, E. (2024). "Perbandingan Metode Naive Bayes, CART, dan CART Adaboost dalam Memprediksi Penjualan Produk Ban." Jurnal Matematika, Statistika dan Komputasi, 21(1), 50–58. https://journal.unhas.ac.id/index.php/jmsk/article/view/33187
[3] Az-Zahra, S. N., & Fadhilah, R. (2023). "Optimization of Naive Bayes and Decision Tree Algorithms Through Bagging and Adaboost Techniques for Predicting Student Study Success." Jurnal INFOTEL, 15(3), 125–132. https://ejournal.ittelkom-pwt.ac.id/index.php/infotel/article/view/1258
[4] Dewi, R. K., & Nurhidayati, N. (2024). "Peningkatan Kinerja Naive Bayes Menggunakan AdaBoost dalam Klasifikasi Diabetes Tipe II." International Journal of Advances in Applied Sciences (IJAAS), 12(1), 77–83. https://ijaas.iaescore.com/index.php/IJAAS/article/view/21131
[5] Fikri, M. A., & Lestari, W. (2024). "Sistem Pengujian Hafalan Al-Qur’an Surah Al-A’la Melalui Suara Menggunakan Transformasi Haar Wavelet." Jurnal Teknik Terapan dan Sains (TTS), 8(1), 60–68. https://ojs.unimal.ac.id/index.php/tts/article/view/19681
[6] Hamid, A., & Nuraini, T. (2022). "Rancang Bangun Aplikasi Hafalan Al-Qur’an dengan Google Speech API Berbasis Android." Jurnal Informatika Mulawarman, 17(2), 111–120. https://e- journals.unmul.ac.id/index.php/INF/article/view/8006
[7] Kurniawan, F., & Mustofa, A. (2022). "Text Classification of Translated Qur'anic Verses Using Supervised Learning Algorithm." Jurnal Predatecs, 5(1), 33-40.https://www.journal.irpi.or.id/index.php/predatecs/article/view/870
[8] Maulana, H., & Rakhmadani, A. (2023). "Multi-label Classification of Indonesian Al-Quran Translation based CNN, BiLSTM, and FastText." Technologia: Jurnal Ilmiah, 14(2), 87–95. https://publikasi2.dinus.ac.id/index.php/technoc/article/view/9925
[9] Nurfadhilah, S., & Fauzi, M. (2024). "Systematic Literature Review: Quran Memorization Methods in Indonesia." Journal of Qur’anic Education and Research (JOQER), 1(1), 1–15. https://joqer.intischolar.id/index.php/joqer/article/view/5
[10] Nurdiansyah, R., & Hidayat, T. (2025). "Enhancement Performance of the Naïve Bayes Method Using AdaBoost for Classification of Diabetes Mellitus Dataset Type II." IJAAS, 13(1), 58–64. https://ijaas.iaescore.com/index.php/IJAAS/article/view/21131
[11] Rahmatullah, I., & Maulidi, A. (2025). "Evaluation of the Pronunciation of Tajweed Rules Based on DNN as a Step Towards Interactive Recitation Learning." arXiv preprint. https://arxiv.org/abs/2503.23470
[12] Ramadhan, A., & Zulkarnain, A. (2023). "Implementasi Metode Ensemble Learning dalam Klasifikasi Teks Keagamaan." Jurnal Ilmu Komputer dan Agama, 6(2), 90–98.
[13] Sari, A. P., & Hasanah, L. (2021). "Penerapan Adaboost untuk Meningkatkan Akurasi Naive Bayes pada Klasifikasi Data Akademik Mahasiswa." Jurnal Sains dan Informatika, 7(1), 43–50.
[14] Susanto, D., & Putri, R. (2023). "Penggunaan Machine Learning untuk Evaluasi Hafalan Santri Pondok Pesantren Berbasis Suara dan Teks." Jurnal Teknologi Pendidikan Islam, 9(2), 102–112.
[15] Zaini, H., & Mulyadi, D. (2022). "Penerapan Klasifikasi Tema Ayat Al-Qur’an Menggunakan Naive Bayes." Jurnal Teknologi Informasi dan Keislaman, 5(1), 55–63.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Journal of Information System and Application Development
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish in this journal agree to the following terms:
(1) Copyright of the published articles will be transferred to the journal as the publisher of the manuscripts. Therefore, the author confirms that the journal has managed the copyright.
(2) Publisher of JISAD: Journal of Information System and Application Development is the University of Merdeka Malang.
(3) The copyright follows Creative Commons Attribution ShareAlike License (CC BY SA): This license allows to Share - copy and redistribute the material in any medium or format, Adapt - remix, transform, and build upon the material, for any purpose, even commercially.
