Sistem Pendukung Keputusan Penentuan Kelayakan Penangguhan Kredit Nasabah menggunakan Naïve Bayes

Authors

  • Aldy Sudrajat Program Studi Sistem Informasi, STMIK Royal Kisaran
  • Neni Mulyani Program Studi Sistem Informasi, STMIK Royal Kisaran
  • Nasrun Marpaung Program Studi Sistem Informasi, STMIK Royal Kisaran

DOI:

https://doi.org/10.29408/edumatic.v6i2.6298

Keywords:

classification, decision support system, naïve bayes, suspension of credit

Abstract

The COVID-19 (Corona Virus Disease 2019) pandemic is still spreading until 2022, so in responding to this, PT Adira Finance provides an opportunity to suspend credit installment payments to nasakabah. In order not to cause installments and defaults (breaking promises) it is necessary to have a decision support system to determine the creditworthiness of these customers. The purpose of this study is to build a decision support system to determine the feasibility of solicitation to customers using the naïve bayes method. The model used to build this system is the System Development Life Cycle (SDLC) with stages of analysis, design, testing, and implementation. The sample or training data used in this study was 20 customers. Meanwhile, the technique used to determine the feasibility of customer suspension uses naïve bayes by looking at the prior and conditional probability values of each criteria. Our findings result in a customer eligibility decision support system with the naïve bayes method is appropriate and accurate. So that with this system, it can be used as a consideration to make decisions by the manager of PT Adira Finance to determine whether or not customers are eligible to receive a credit suspension.

References

Ali, L., & Bukhari, S. A. C. (2021). An approach based on mutually informed neural networks to optimize the generalization capabilities of decision support systems developed for heart failure prediction. Irbm, 42(5), 345–352. https://doi.org/10.1016/j.irbm.2020.04.003

Alita, D., Sari, I., Isnain, A. R., & Styawati, S. (2021). Penerapan Naïve Bayes Classifier Untuk Pendukung Keputusan Penerima Beasiswa. Jurnal Data Mining Dan Sistem Informasi, 2(1), 17–23.

Ardiansyah, M., Sunyoto, A., & Luthfi, E. T. (2021). Analisis Perbandingan Akurasi Algoritma Naïve Bayes Dan C4. 5 untuk Klasifikasi Diabetes. Edumatic: Jurnal Pendidikan Informatika, 5(2), 147–156. https://doi.org/10.29408/edumatic.v5i2.3424

Armansyah, A., & Ramli, R. K. (2022). Model Prediksi Kelulusan Mahasiswa Tepat Waktu dengan Metode Naïve Bayes. Edumatic: Jurnal Pendidikan Informatika, 6(1), 1–10. https://doi.org/10.29408/edumatic.v6i1.4789

Belikov, A. N., Rogozov, Y. I., & Shevchenko, O. V. (2018). Synthesis of the life cycle stages of information systems development. Computer Science On-Line Conference, 331–337. https://doi.org/10.1007/978-3-319-91186-1_34

Damuri, A., Riyanto, U., Rusdianto, H., & Aminudin, M. (2021). Implementasi Data Mining dengan Algoritma Naïve Bayes Untuk Klasifikasi Kelayakan Penerima Bantuan Sembako. JURIKOM (Jurnal Riset Komputer), 8(6), 219–225.

Dulhare, U. N. (2018). Prediction system for heart disease using Naive Bayes and particle swarm optimization. Biomedical Research, 29(12), 2646–2649. https://doi.org/10.4066/biomedicalresearch.29-18-620

García-Díaz, V., Espada, J. P., Crespo, R. G., G-Bustelo, B. C. P., & Lovelle, J. M. C. (2018). An approach to improve the accuracy of probabilistic classifiers for decision support systems in sentiment analysis. Applied Soft Computing, 67, 822–833. https://doi.org/10.1016/j.asoc.2017.05.038

Hendrawan, I. R., Utami, E., & Hartanto, A. D. (2022). Comparison of Naïve Bayes Algorithm and XGBoost on Local Product Review Text Classification. Edumatic: Jurnal Pendidikan Informatika, 6(1), 143–149. https://doi.org/10.29408/edumatic.v6i1.5613

Hwang, B.-G., Shan, M., & Looi, K.-Y. (2018). Knowledge-based decision support system for prefabricated prefinished volumetric construction. Automation in Construction, 94, 168–178. https://doi.org/10.1016/j.autcon.2018.06.016

Irawan, D., Perkasa, E. B., Yurindra, Y., Wahyuningsih, D., & Helmud, E. (2021). Perbandingan Klassifikasi SMS Berbasis Support Vector Machine, Naive Bayes Classifier, Random Forest dan Bagging Classifier. Jurnal Sisfokom (Sistem Informasi Dan Komputer), 10(3), 432–437. https://doi.org/10.32736/sisfokom.v10i3.1302

Kinyua, J. (2020). Cybersecurity in the software development life cycle. In Cybersecurity for Information Professionals (pp. 265–290). Auerbach Publications. https://doi.org/10.1201/9781003042235-12

Kurniawati, R. D., & Ahmad, I. (2021). Sistem Pendukung Keputusan Penentuan Kelayakan Usaha Mikro Kecil Menengah Dengan Menggunakan Metode Profile Matching Pada Uptd Plut Kumkm Provinsi Lampung. Jurnal Teknologi Dan Sistem Informasi, 2(1), 74–79.

Magrisa, T., Wardhani, K. D. K., & Saf, M. R. A. (2018). Implementasi Metode SMART pada Sistem Pendukung Keputusan Pemilihan Kegiatan Ekstrakurikuler untuk Siswa SMA. Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer, 13(1), 49–55. https://doi.org/10.30872/jim.v13i1.648

Martensen, H., Diependaele, K., Daniels, S., Van den Berghe, W., Papadimitriou, E., Yannis, G., Van Schagen, I., Weijermars, W., Wijnen, W., & Filtness, A. (2019). The European road safety decision support system on risks and measures. Accident Analysis & Prevention, 125, 344–351. https://doi.org/10.1016/j.aap.2018.08.005

Rahmadi, M., Kaurie, F., & Susanti, T. (2020). Uji Akurasi Dataset Pasien Pasca Operasi Menggunakan Algoritma Naïve Bayes Menggunakan Weka Tools. JURIKOM (Jurnal Riset Komputer), 7(1), 134–139. https://doi.org/10.30865/jurikom.v7i1.1761

Rifqo, M. H., & Veronica, N. D. M. (2019). Implementasi Algoritme Naïve Bayes Berbasis Particle Swarm Optimization Dalam Penentuan Pemberian Kredit. Pseudocode, 6(1), 1–12. https://doi.org/10.33369/pseudocode.6.1.1-12

Riswanto, I., & Laluma, R. H. (2020). Klasifikasi Kelayakan Pinjaman Pada Koperasi Karyawan Menggunakan Metode Naïve Bayes Classifier Berbasis Web. Infotronik: Jurnal Teknologi Informasi Dan Elektronika, 5(1), 11–16. https://doi.org/10.32897/infotronik.2020.5.1.357

Sari, V., Firdausi, F., & Azhar, Y. (2020). Perbandingan Prediksi Kualitas Kopi Arabika dengan Menggunakan Algoritma SGD, Random Forest dan Naive Bayes. Edumatic: Jurnal Pendidikan Informatika, 4(2), 1–9. https://doi.org/10.29408/edumatic.v4i2.2202

Septilia, H. A., Parjito, P., & Styawati, S. (2020). Sistem Pendukung Keputusan Pemberian Dana Bantuan menggunakan Metode AHP. Jurnal Teknologi Dan Sistem Informasi, 1(2), 34–41. https://doi.org/10.35957/jtsi.v1i2.513

Setiyawati, N., & Widiyanto, E. E. (2021). Sistem Pendukung Keputusan Penentuan Kualitas Benih Bunga Viola Menggunakan Simple Additive Weighting. Sistemasi: Jurnal Sistem Informasi, 10(3), 662–675. https://doi.org/10.32520/stmsi.v10i3.1391

Sihombing, L. O., Hannie, H., & Dermawan, B. A. (2021). Sentimen Analisis Customer Review Produk Shopee Indonesia Menggunakan Algortima Naïve Bayes Classifier. Edumatic: Jurnal Pendidikan Informatika, 5(2), 233–242. https://doi.org/10.29408/edumatic.v5i2.4089

Sumanto, S., Marita, L. S., Mazia, L., & Ratnasari, T. W. (2021). Analisis Kelayakan Kredit Rumah Menggunakan Metode Naïve Bayes untuk Mengurangi Kredit Macet. Applied Information System and Management (AISM), 4(1), 17–22. https://doi.org/10.15408/aism.v4i1.20274

Zong, K., Yuan, Y., Montenegro-Marin, C. E., & Kadry, S. N. (2021). Or-based intelligent decision support system for e-commerce. Journal of Theoretical and Applied Electronic Commerce Research, 16(4), 1150–1164. https://doi.org/10.3390/jtaer16040065

Downloads

Published

2022-12-20