Selection-Based Optimization of Naive Bayes and Decision Trees for Intelligent Classification of Sharia Financing Eligibility
DOI:
https://doi.org/10.29103/sisfo.v10i2.27634Keywords:
classification, decision tree, feature selection, Naïve BayesAbstract
Assessing sharia financing eligibility is a critical process for Islamic microfinance institutions such as Baitul Maal wat Tamwil (BMT), as inaccurate financing decisions may increase financing risk and affect institutional sustainability. However, financing evaluations are often conducted manually and rely heavily on subjective judgment, leading to inconsistencies and potential bias in decision-making. Therefore, this study aims to evaluate the effectiveness of Correlation-Based Feature Selection (CFS) in identifying relevant financing attributes and to compare the classification performance of the Naïve Bayes and Decision Tree (J48) algorithms for sharia financing eligibility assessment. The dataset used in this study consists of 500 historical financing records obtained from BMT Indragiri, comprising 127 eligible and 373 ineligible financing applications. The research process included data preprocessing, feature selection using CFS with the BestFirst search strategy, model construction, and classification using WEKA 3.8. Model performance was evaluated using 10-fold cross-validation based on accuracy, precision, recall, F1-score, Kappa statistic, and Area Under the Curve (AUC). The feature selection results showed that all predictor attributes, namely income, number of dependents, employment status, and financing history, were retained by the CFS algorithm, indicating that each attribute contributes relevant information to financing eligibility classification. Experimental results revealed that the Naïve Bayes classifier achieved an accuracy of 92.4%, precision of 92.3%, recall of 92.4%, F1-score of 92.2%, Kappa statistic of 0.7896, and AUC of 0.971. Meanwhile, the Decision Tree (J48) classifier achieved superior performance with an accuracy of 95.6%, precision of 95.7%, recall of 95.6%, F1-score of 95.6%, Kappa statistic of 0.8851, and AUC of 0.957. In addition, the Decision Tree model generated 23 decision rules and a tree size of 42 nodes, providing transparent and interpretable knowledge to support financing eligibility assessment. The findings indicate that the Decision Tree (J48) algorithm outperformed Naïve Bayes in classifying sharia financing eligibility and offers the additional advantage of interpretable decision rules. The proposed approach can support more objective, consistent, and transparent financing decision-making processes in Islamic microfinance institutions.
References
[1] H. Sastypratiwi, Yulianti, and H. Muhardi, “Uji Komparasi Algoritma Naïve Bayes dan Decision Tree Classification,” JEPIN (Jurnal Edukasi dan Penelit. Inform., vol. 8, no. 1, pp. 1–6, 2022.
[2] H. Darmawan, Zulfa Muflihah, and Tita Karlita, “Deep Metric Learning with Different Distance Metrics for Enhanced Classification Model in Typing Style,” JUTI J. Ilm. Teknol. Inf., vol. 23, no. 2, pp. 111–125, 2025, doi: 10.12962/j24068535.v23i2.a1292.
[3] A. N. Huda and S. S. Kusumawardani, “KUBERNETES CLUSTER MANAGEMENT FOR CLOUD COMPUTING PLATFORM: A SYSTEMATIC LITERATURE REVIEW | Huda | JUTI: Jurnal Ilmiah Teknologi Informasi,” no. Vm, pp. 75–83, [Online]. Available: http://juti.if.its.ac.id/index.php/juti/article/view/1103
[4] S. Safaatulloh, R. Hariyanto, and M. Udin, “Penerapan Metode Naïve Bayes Classifier Untuk Kerusakan Perangkat Edc Yang Ada Di Spbu,” J. Mnemon., vol. 7, no. 2, pp. 207–212, 2024, doi: 10.36040/mnemonic.v7i2.10687.
[5] V. S. Moertini and B. Sitohang, “Algorithms of Clustering and Classifying Batik Images Based on Color, Contrast and Motif,” ITB J. Eng. Sci., vol. 37, no. 2, pp. 141–160, 2005, doi: 10.5614/itbj.eng.sci.2005.37.2.5.
[6] M. K. Al Fatach and A. Wibowo, “Perbandingan Metode Algoritma C4.5 Dan Naive Bayes Untuk Memprediksi Penjualan Kosmetik Pada Toko Jelita,” J. Mnemon., vol. 7, no. 2, pp. 220–225, 2024, doi: 10.36040/mnemonic.v7i2.10730.
[7] L. B. Adzy, A. Asriyanik, and A. Pambudi, “Algoritma Naïve Bayes Untuk Klasifikasi Kelayakan Penerima,” J. Mnemon., vol. 6, no. 1, pp. 1–10, 2023.
[8] A. Fauziyyah et al., “Analisis Data Penjualan Alat-Alat Pancing Menggunakan Algoritma Apriori pada Toko Zacozi Pancing Fishing,” vol. 5, no. 1, pp. 64–74, 2026.
[9] E. Windia Ambarsari, “Klasifikasi Daya Tarik Konten Artikel Media Daring Dari Data Google Analytics Dengan C-FDT,” J. Inform. J. Pengemb. IT, vol. 3, no. 2, pp. 211–218, 2018, doi: 10.30591/jpit.v3i2.876.
[10] S. Styawati, A. R. Isnain, N. Hendrastuty, and L. Andraini, “Comparison of Support Vector Machine and Naïve Bayes on Twitter Data Sentiment Analysis,” J. Inform. J. Pengemb. IT, vol. 6, no. 1, pp. 56–60, 2021, doi: 10.30591/jpit.v6i1.3245.
[11] N. Ratama, “Analisa Dan Perbandingan Sistem Aplikasi Diagnosa Penyakit Asma Dengan Algoritma Certainty Factor Dan Algoritma Decision Tree Berbasis Android,” J. Inform. J. Pengemb. IT, vol. 3, no. 2, pp. 177–183, 2018, doi: 10.30591/jpit.v3i2.848.
[12] R. Pardeshi and P. Deshmukh, “Automatic Classification of Desmids Using Transfer Learning,” J. Appl. Eng. Technol. Sci., vol. 4, no. 2, pp. 885–894, 2023, doi: 10.37385/jaets.v4i2.1864.
[13] J. Sofie Azizah, W. J. Pranoto, and R. Hasudungan, “Penerapan Metode Naive Bayes Klasifiksi Kelayakan Penerima Bantuan Pangan Non Tunai (Bpnt),” J. Mnemon., vol. 8, no. 1, pp. 9–14, 2025, doi: 10.36040/mnemonic.v8i1.12778.
[14] J. Fernando and Fathoni, “Analisis Sentimen pada E-Learning Universitas XYZ Menggunakan Metode Naïve Bayes Classifier dan Support Vector Machine,” J. Ilm. Teknol. Inf., vol. 21, no. 2, pp. 81–97, 2023.
[15] Y. Romadhoni and K. F. H. Holle, “Analisis Sentimen Terhadap PERMENDIKBUD No.30 pada Media Sosial Twitter Menggunakan Metode Naive Bayes dan LSTM,” J. Inform. J. Pengemb. IT, vol. 7, no. 2, pp. 118–124, 2022, doi: 10.30591/jpit.v7i2.3191.
[16] S. P. Backar, P. Purnawansyah, H. Darwis, and W. Astuti, “Hybrid Fourier Descriptor Naïve Bayes dan CNN pada Klasifikasi Daun Herbal,” J. Inform. J. Pengemb. IT, vol. 8, no. 2, pp. 126–133, 2023, doi: 10.30591/jpit.v8i2.5186.
[17] Y. A. Firdaus, “Perbandingan Algoritma Naive Bayes Dan Decision Tree Pada Analisis Sentimen Media Sosial Terhadap Kendaraan Listrik Indonesia Menggunakan Metode Tf-Idf,” J. Mnemon., vol. 8, no. 2, pp. 183–192, 2025.
[18] L. A. Ma’rifah, I. Afrianty, E. Budianita, and F. Syafria, “Klasifikasi Tulang Tengkorak Berdasarkan Jenis Kelamin Menggunakan Correlation-Based Feature Selection (CFS) dengan Backpropagation Neural Network (BPNN),” J. Inform. J. Pengemb. IT, vol. 10, no. 2, pp. 333–347, 2025, doi: 10.30591/jpit.v10i2.8616.
[19] M. Nabil, A. V. Vitianingsih, and S. Kacung, “Jurnal Teknologi dan Manajemen Informatika Sentiment Analysis of NTB Syariah Bank Application Services using The Naïve Bayes and Support Vector Machine Methods,” vol. 11, no. 2, pp. 177–188, 2025.
[20] S. Suyadi, A. Setyanto, and H. Al Fattah, “Analisis Perbandingan Algoritma Decision Tree (C4.5) Dan K-Naive Bayes Untuk Mengklasifikasi Penerimaan Mahasiswa Baru Tingkat Universitas,” Indones. J. Appl. Informatics, vol. 2, no. 1, p. 59, 2017, doi: 10.20961/ijai.v2i1.13258.
[21] R. Rahmayanti, R. Herteno, S. W. Saputro, T. H. Saragih, and F. Abadi, “Comparative Study of Filter, Wrapper, and Hybrid Feature Selection Using Tree-Based Classifiers for Software Defect Prediction,” Indones. J. Electron. Electromed. Eng. Med. Informatics, vol. 8, no. 1, pp. 1–16, 2025, doi: 10.35882/ijeeemi.v8i1.294.
[22] W. A. Firmansyach, U. Hayati, and Y. Arie Wijaya, “Analisa Terjadinya Overfitting Dan Underfitting Pada Algoritma Naive Bayes Dan Decision Tree Dengan Teknik Cross Validation,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 1, pp. 262–269, 2023, doi: 10.36040/jati.v7i1.6329.
[23] A.- Husaini, I. Hariyanti, and A. R. Raharja, “Perbandingan Algoritma Decision Tree dan Naive Bayes dalam Klasifikasi Data Pengaruh Media Sosial dan Jam Tidur Terhadap Prestasi Akademik Siswa,” Technol. J. Ilm., vol. 15, no. 2, p. 332, 2024, doi: 10.31602/tji.v15i2.14381.
[24] V. S. K. Reddy, P. Meghana, N. V. S. Reddy, and B. A. Rao, “Prediction on Cardiovascular disease using Decision tree and Naïve Bayes classifiers,” J. Phys. Conf. Ser., vol. 2161, no. 1, 2022, doi: 10.1088/1742-6596/2161/1/012015.
[25] D. H. Depari, Y. Widiastiwi, and M. M. Santoni, “Perbandingan Model Decision Tree, Naive Bayes dan Random Forest untuk Prediksi Klasifikasi Penyakit Jantung,” Inform. J. Ilmu Komput., vol. 18, no. 3, p. 239, 2022, doi: 10.52958/iftk.v18i3.4694.
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