PERBANDINGAN ALGORITMA NAIVE BAYES DAN NAIVE BAYES BERBASIS PSO UNTUK ANALISIS KREDIT PADA PT. BPR SYARIAH PAOKMOTONG

Yupi Kuspandi Putra, Muhamad Sadali

Abstract


According to Law No.21 of 2008, it is stated that Islamic banks are business entities that collect funds from the public in the form of deposits and channel to the public in the form of loans and / or other forms in order to improve people's lives. Credit is the provision of money or bills that can be equated with it, based on a loan and loan agreement between the bank and another party which requires the borrower to repay the debt after a certain period of time with interest (Banking Law No.10 of 1998).

In analyzing a credit, sometimes an analysis performs an inaccurate analysis, so there are some customers who are less able to pay credit installments, resulting in substandard loans and even defaults. From the above problems, researchers conducted a credit analysis using computerized techniques by utilizing RapidMiner software in processing data. The right data processing technique to use is classification. One method of classification of data mining is the Naive Bayes algorithm. The researcher used weighting by applying Particle Swarm Optimization (PSO) for attribute selection to improve the accuracy of Naive Bayes.

After testing with the Naive Bayes algorithm model of 71.00%, the Naive Bayes algorithm based on particle swarm optimization results in a higher accuracy value of 88.51% compared to the Naive Bayes algorithm model. From these results the difference between the two models is 17.51%.

DOI : 10.29408/jit.v2i2.1460


Keywords


Analysis, Credit, Naive Bayes, Particle Swarm Optimization

Full Text:

PDF

References


Larose, Daniel T, 2005, Discovering Knowledge in Data: An Introduction to Data Mining, John Willey & Sons. Inc

Daniel T. Larose. Data Mining Methods and Models. Hoboken, New Jersey :John Wiley & Sons, Inc.2007.

J. Han and M. Kamber, “Data Mining: Concepts and Techniques,†Ann. Phys. (N. Y)., vol. 54, p. 770, 2006.

Abraham, A., Grosan, C., & Ramos, V. (2006). Swarm Intelligence in Data Mining. New York: Springer., p. 2006, 2006.

Han, J., & Kamber, M. (2007). Data Mining Concepts and Technique. Morgan Kaufmann publisher

Yu, L., Chen, G., Koronios, a., Zhu, S., & Guo, X. (2007). Application and Comparison of Classification Techniques in Controlling Credit Risk. World Scientific , 111.

Berndtssom, M., Hansson, J., Olsson, B., & Lundell, B. (2008). A Guide for Students in Computer Science and Information Systems. London: Springer., p. 2008, 2008

Dawson, C. W. (2009). Project in Computing and Information System A Student's Guide. England: Addison-Wesley

Vercellis, C. (2009). Business Intelligence : Data Mining and Optimization for Decision Making. John Wiley & Sons, Ltd

Shukla, A., Tiwari, R., & Kala, R. (2010). Real Life Application of Soft Computing. CRC Press.., p. 2010, 2010.

Kasmir. 2012. Bank dan Lembaga Kuangan Lainnya. Jakarta: PT. Raja Grafindo Persada.

Sudarsono, Heri. 2012. Bank dan Lembaga Keuangan Syariah. Edisi Keempat. Yogyakarta : Ekonisia.

Saduldyn Pato (2013). Analisis Pemberian Kredit Mikro Pada Bank Syariah Mandiri Cabang Manado. Jurnal EMBA Vol. 1 No.4, 875-885.

Siti Maspirah, (2015). Algoritma Klasifikasi C4.5 berbasis Particle Swarm Optimization untuk Evaluasi Penentuan kelayakan Prmberian Kredit Koperasi Syariah.

Muhammad Saiful, (2016). Klasifikasi Kinerja Dosen STT Hamzanwadi Menggunakan Naive Bayes Berbasis PSO. Jurnal Informatika Hamzanwadi Vol. 1 No.1.

Ade Mubarok, (2019). Sistem Pendukung Keputusan kelayakan Prmberian Kredit Dengan Metode TOPSIS. Jurnal Informatika Vol. 6 No.1, 37-46.

UU RI nomor 9 tahun 1995 pasal 1

UU Perbankan No.10 Tahun 1998

UU No.21 tahun 2008




DOI: https://doi.org/10.29408/jit.v2i2.1460

Refbacks

  • There are currently no refbacks.


Copyright (c) 2019 Infotek: Jurnal Informatika dan Teknologi

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

View My Stats