ANALISIS PERBANDINGAN METODE NEURAL NETWORKS DAN NAÏVE BAYES UNTUK KLASIFIKASI KELAYAKAN PEMBERIAN KREDIT (STUDI KASUS: PT. ADIRA FINANCE KOTA TERNATE)

Ahmad Nur Arfandi; Assaf Arief; Muhammad Fhadli; Rosihan

doi:10.33387/jati.v2i2.115

COMPARATIVE ANALYSIS OF NEURAL NETWORKS AND NAÏVE BAYES METHODS FOR CREDIT APPROVAL CLASSIFICATION (CASE STUDY: PT. ADIRA FINANCE TERNATE CITY)

Authors

Ahmad Nur Arfandi

Program Studi Teknik Informatika, Fakultas Teknik, Universitas Khairun Jl. Jati Metro, Kota Ternate Selatan , Program Studi Teknik Informatika, Fakultas Teknik, Universitas Khairun Jl. Jati Metro, Kota Ternate Selatan
Assaf Arief

Program Studi Teknik Informatika, Fakultas Teknik, Universitas Khairun Jl. Jati Metro, Kota Ternate Selatan , Program Studi Teknik Informatika, Fakultas Teknik, Universitas Khairun Jl. Jati Metro, Kota Ternate Selatan
Muhammad Fhadli

Program Studi Teknik Informatika, Fakultas Teknik, Universitas Khairun Jl. Jati Metro, Kota Ternate Selatan , Program Studi Teknik Informatika, Fakultas Teknik, Universitas Khairun Jl. Jati Metro, Kota Ternate Selatan
Rosihan

Program Studi Teknik Informatika, Fakultas Teknik, Universitas Khairun Jl. Jati Metro, Kota Ternate Selatan , Program Studi Teknik Informatika, Fakultas Teknik, Universitas Khairun Jl. Jati Metro, Kota Ternate Selatan

DOI:

https://doi.org/10.33387/jati.v2i2.115

Keywords:

data mining, Classification, credit risk, neural network, naive bayes

Abstract

Credit is one of the shortcuts sometimes taken by people to fulfill their needs, but it carries significant risks for companies, such as defaulting credit customers. Data mining classification was chosen to help provide a solution to this problem. In this research, classification using the Neural Network and Naïve Bayes algorithm models was employed and compared to determine which algorithm model had the best accuracy in classifying the credit status of customers using the Python programming language. The evaluation was then conducted using a confusion matrix. The results of the Neural Network algorithm, after testing with the confusion matrix, showed the highest accuracy value at 85.0%, while the Naïve Bayes algorithm, after testing with the confusion matrix, showed the highest accuracy value at 84.7%. Based on these results, it can be concluded that the Neural Network performs slightly better in classifying credit customer data compared to Naïve Bayes.

References

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Published

2023-12-23

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Vol. 2 No. 2: DESEMBER 2023 - MEI 2024

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How to Cite

COMPARATIVE ANALYSIS OF NEURAL NETWORKS AND NAÏVE BAYES METHODS FOR CREDIT APPROVAL CLASSIFICATION (CASE STUDY: PT. ADIRA FINANCE TERNATE CITY). (2023). Jurnal Jaringan Dan Teknologi Informasi, 2(2), 31-38. https://doi.org/10.33387/jati.v2i2.115

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