SELEKSI ATRIBUT PADA DATA TIDAK SEIMBANG NASABAH KOPERASI DENGAN OPTIMASI SMOTE DAN ADABOOST
Keywords:Selekasi Atribut, Adaboost, SMOTE, Hutan Acak, Nasabah, Koperasi
The credit procedure is the provision of credit on the basis of the bank's belief in the ability and ability of the customer to repay. In this study, the customer data tested was divided into 3 classes, namely 39 LOW customers, 84 MEDIUM customers, and 11 HIGH customers. Unbalanced datasets are pre-processed using the Synthetic Minority Over-sampling (SMOTE) technique. Classification methods such as Random Forest and Support Vector Machine will test cooperative customer data. The data is tested based on the attribute that has the highest matching value using the Particle Swarm Optimization method, this test is also optimized with the Adaboost method to increase its accuracy. The results of tests carried out using the Random Forest method obtained an accuracy of 89.05% and the Support Vector Machine algorithm obtained an accuracy of 81.75%. Meanwhile, testing with two methods optimized with Adaboost showed an increase in accuracy with Random Forest getting 91.24% accuracy and Support Vector Machine getting 82.48% accuracy. The highest accuracy in the cooperative customer data classification test was obtained from the Random Forest algorithm which was optimized with Adaboost at 91.24%
R. Oktapiani, D. Prayudi, A. Fajria, N. S. Z. Nufus, and R. N. Lestari, “Sistem Pendukung Keputusan Untuk Menentukan Managemen Kelayakan Pemberian Kredit Di Bank Mandiri Taspen Sukabumi Menggunakan Metode Analytic Hierarchy Process,” Indones. J. Softw. Eng., vol. 8, no. 1, pp. 36–45, 2022, doi: 10.31294/ijse.v8i1.12054.
S. B. Riono, “Jurnal Ilmiah Manajemen Dan Kewirausahaan,” Jimak, vol. 1, no. 3, pp. 2809–2406, 2022.
M. Zhang, J. Fan, A. Sharma, and A. Kukkar, “Data mining applications in university information management system development,” J. Intell. Syst., vol. 31, no. 1, pp. 207–220, 2022, doi: 10.1515/jisys-2022-0006.
A. H. M. Alaidi, R. M. Al_airaji, H. T. S. ALRikabi, I. A. Aljazaery, and S. H. Abbood, “Dark Web Illegal Activities Crawling and Classifying Using Data Mining Techniques,” Int. J. Interact. Mob. Technol., vol. 16, no. 10, pp. 122–139, 2022, doi: 10.3991/ijim.v16i10.30209.
X. Wang and W. Yao, “applied sciences A Discrete Particle Swarm Optimization Algorithm for Dynamic Scheduling of Transmission Tasks,” MDPI, vol. 13, no. 7, 2023.
S. Susanti, “Klasifikasi Kemampuan Perawatan Diri Anak dengan Disabilitas Menggunakan SMOTE Berbasis Neural Network,” J. Inform., vol. 6, no. 2, pp. 175–184, 2019, doi: 10.31311/ji.v6i2.5798.
N. Thanh-Long, Tran-Minh, and L. Hong-Chuong, “A Back Propagation Neural Network Model with the Synthetic Minority Over-Sampling Technique for Construction Company Bankruptcy Prediction,” Int. J. Sustain. Constr. Eng. Technol., vol. 13, no. 3, pp. 68–79, 2022, doi: 10.30880/ijscet.2022.13.03.007.
J. L. Putra and A. Subekti, “Prediksi Kinerja Siswa Pada E-Learning Moodle Platform Menggunakan Algoritma Adaptive Boosting,” J. Inform., vol. 10, no. 1, 2023.
M. T. Ramakrishna, V. K. Venkatesan, I. Izonin, M. Havryliuk, and C. R. Bhat, “Homogeneous Adaboost Ensemble Machine Learning Algorithms with Reduced Entropy on Balanced Data,” MDPI, vol. 25, no. 2, 2023, doi: 10.3390/e25020245.
H. Fei et al., “Cotton Classification Method at the County Scale Based on Multi-Features and Random Forest Feature Selection Algorithm and Classifier,” MDPI, vol. 14, no. 4, 2022, doi: 10.3390/rs14040829.
Y. Guan, K. Grote, J. Schott, and K. Leverett, “Prediction of Soil Water Content and Electrical Conductivity using Random Forest Methods with UAV Multispectral and Ground-coupled Geophysical Data,” MDPI, vol. 14, no. 4, 2022, doi: 10.3390/rs14041023.
Normah, B. Rifai, S. Vambudi, and R. Maulana, “Analisa Sentimen Perkembangan Vtuber Dengan Metode Support Vector Machine Berbasis SMOTE,” J. Tek. Komput. AMIK BSI, vol. 8, no. 2, pp. 174–180, 2022, doi: 10.31294/jtk.v4i2.
R. A. Saputra, D. Puspitasari, and T. Baidawi, “Deteksi Kematangan Buah Melon dengan Algoritma Support Vector Machine Berbasis Ekstraksi Fitur GLCM,” J. Infortech, vol. 4, no. 2, 2022, [Online]. Available: http
How to Cite
Copyright (c) 2023 Richky, Andre, Imam, Andi, Eko Pramono, Fajar Akbar
This work is licensed under a Creative Commons Attribution 4.0 International License.