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All Journal Jurnal Online Mahasiswa (JOM) Bidang Matematika dan Ilmu Pengetahuan Alam Journal of Intelligent Systems Ensiklopedia of Journal Jurnal Riset Informatika Jurnal Peduli Masyarakat Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Melek IT: Information Technology Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Journal of Accounting Information System Journal of Computer Science Contributions (Jucosco) Research Journal of Social Economics Empowerment (ROSEE)
Indah Suryani
STMIK Nusa Mandiri
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Journal : Jurnal Riset Informatika

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Comparison of Decision Tree, Naive Bayes and Random Forest Algorithm to get the Best Performance of Algorithm for Customer Credit Classification Suryani, Indah
Jurnal Riset Informatika Vol. 6 No. 3 (2024): June 2024
Publisher : Kresnamedia Publisher

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Abstract

Credit is a potential income and the most significant business operation risk for a bank. Bad credit has become an ingrained problem in the banking world. Therefore, this research aims to classify customer data profiles who have the opportunity to be able to apply for a loan or not to reduce the risk of bad credit in the future by classifying using three commonly used data mining algorithms, namely the Decision Tree algorithm, Naïve Bayes and Random forest. The research was conducted using an experimental, descriptive method by testing the accuracy of the three methods to get the best performance. Based on the experiments' results, the accuracy performance with the confusion matrix was 73.20% for the Decision Tree algorithm, then the accuracy for the Naive Bayes algorithm was 74.4% and Random Forest was 77.4%. Meanwhile, performance evaluation is based on the Receiver Operating Characteristics (ROC) curve by looking at the resulting Area Under Curve (AUC) value of 0.717 for the Decision Tree algorithm, while Naive Bayes produces an AUC value of 0.741 and the largest is Random Forest at 0.796. So it can be concluded that the best performance of the classification carried out is the one that uses the Random Forest algorithm. Then, from the validation results using the T-Test of the three methods being compared, Random Forest produces a significant difference in the level of accuracy compared to the accuracy produced by the Decision Tree, namely with an alpha value of 0.031.
Co-Authors ', Mashadi A Gunawan Ade Christian, Ade Al Adzani, Muhammad Raulfie Anggraeini, Sita Arsa Pratama, Rakha Aslina, Aslina Aslina, Aslina Deppagoga, Wilson Benedict Endriana, E. Fina Ericka Darmawan Fitri Latifah, Fitri Hani Harafani, Hani Ibnu Rusdi Indah Purnamasari Lukman Hakim M. Natsir Maeza, Al Thorik Marsyah, Ulfy Muhammad Faisal Muhammad Ismail Muhammad Ridwan Nurkhayati, Sri Nurwahyuni Perdana, Muhammad Rizky Priatna, Nico Purnomo, Azis Rahma, Amelia Rahman, Muhammad Rizqul Mubarok, Salman Romi Satria Wahono Sandra Dewi Saraswati Supriyadi Supriyadi Suryani, Ulfa Tri Wahyono, Tri Ummu Radiyah, Ummu Verra Sofica Yazia, Velga