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Abstract
The bank conducts an analysis or survey in the credit system to determine whether the customer is eligible to receive credit. With a case study of Bank BJB debtor data in December 2021, credit classification analysis was carried out by forming a model using the Naïve Bayes Classifier and Decision Tree J48. Thus it is expected to minimize the occurrence of bad loans. The data are divided into several categories: debtors with good, substandard, doubtful, and bad credit. The analysis was carried out using a 10-fold cross-validation model, where the results obtained from both tests, the highest accuracy value was the Decision Tree J48 of 78.26%. While the Naïve Bayes Classifier has a lower level of accuracy, the prediction results tend to be better than the Decision Tree J48. The prediction results with the Naïve Bayes Classifier can predict all classes and the most influential variable in classifying credit is the loan term.
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References
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References
Kasmir, Manajemen Perbankan, PT. Raja Grafindo Persada, Jakarta, 2011.
A. N. Kholifah and N. Insani, Analisis Klasifikasi Pada Nasabah Kredit Koperasi X Menggunakan Decision Tree C4.5 dan Naive Bayes, Jurnal Pendidikan Matematika dan Sains (2016).
Rahmadeni, Susandi, R. Yendra, and A. P. Desvina, Analisis Diskriminan Fisher Untuk Klasifikasi Risiko Kredit, Seminar Nasional Teknologi Informasi, Komunikasi dan Industri (SNTIKI) 11 (2019).
D. S. L. Ting, P. W. H. Ip, and D. H. C. Tsang, Is Naïve Bayes a Good Classifier for Document Classification, Int. J. Softw. Eng. its Appl., 5(3) (2011) 37–46.
Ketjie, V. C. Mawardi, and N. J. Perdana, Prediction of Credit Card Using the Naïve Bayes Method and C4.5 Algorithm, IOP Conf. Ser.: Mater. Sci. Eng., (2020).
A. Nafalski and A. P. Wibawa, Machine translation with Javanese speech levels’ classification, Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 6(1) (2016) 21–25.
Bustami, Penerapan Algoritma Naive Bayes Untuk Mengklasifikasi Data Nasabah Asuransi, Jurnal Informatika, 8(1) (2014) 884–898.
D. Yusuf and E. Sestri, Metode Decision Tree Dalam Klasifikasi Kredit Pada Nasabah PT Bank Perkreditan Rakyat (Studi Kasus : PT BPR Lubuk Raya Mandiri), Jurnal Sistem Informasi (JUSIN), 1(1) (2020) 21–28.
M. R. Camana, S. Ahmed, C. E. Garcia, and I. Koo, Extremely Randomized Trees-BasedScheme for Stealthy Cyber-Attack Detection in Smart Grid Networks, IEEE Access 4(2016) (2020) 1–13.
Rusito and M. T. Firmansyah, Implementasi Metode Decision Tree dan Algoritma C4.5 Untuk Klasifikasi Data Nasabah, INFOKAM, 1(12) (2016) 1–12.
E. v. Venkatesan, Performance Analysis of Decision Tree Algorithms for Breast Cancer Classification, Indian Journal of Science and Technology (2015).
N. Saravanan and V. Gayathri, Performance and Classification Evaluation of J48 Algorithm and Kendall’s Based J48 Algorithm (KNJ48), Int. j. comput. intell. inform., 7(4) (2018) 188–198.
P. Gulati, A. Sharma, and M. Gupta, Theoretical Study of Decision Tree Algorithms to Identify Pivotal Factors for Performance Improvement: A Review, Int. J. Comput. Appl., 141(14) (2016) 19–25.
T.-T. Wong and P.-Y. Yeh, Reliable Accuracy Estimates from k-Fold Cross Validation, IEEE Trans. Knowl. Data Eng., 32(8) (2020).
I. M. Nasir, M. A. Khan, M. Yasmin, J. H. Shah, M. Gabryel, and R. Damaševičius, Pearson Correlation-Based Feature Selection for Document Classification Using Balanced Training, sensors, 20(6793) (2020).