Optimasi Algoritma Random Forest Dengan Fitur Seleksi Backward Elimination Untuk Penilaian Kelayakan Kredit
DOI:
https://doi.org/10.70309/ticom.v13i3.151Keywords:
random forest, backward elimination, confusion matrix, ROC curvaAbstract
Kredit sekarang menjadi tren di masyarakat. Problem kredit adalah sejarah penggunaan kartu kredit yang salah. Dampak yang ditimbulkan dapat menyebabkan kredit macet. Jika pelanggan tidak membayar utang yang telah disepakati dengan bank, mereka dapat meningkatkan risiko kredit mereka. Dalam penelitian ini, peneliti menerapkan algoritma Random Forest tanpa optimasi dan Algorima Random Forest dengan Optimasi Fitur Seleksi Backward Elimination untuk mengklasifikasikan status kelayakan kredit. Peneliti menggunakan 481 catatan kredit kendaraan dengan ulasan ”bad” dan ”good”. Variabel independen digunakan dalam penelitinan adalah status tanggungan, usia, pendidikan terkahir, status pernikahan, pekerjaan, status perusahaan, pendapatan, status pekerjaan, kondisi rumah, lama tinggal dan uang muka. Dari hasil penelitian dan pengujian, performa model random forest tanpa backward elimination untuk penilaian kelayakan kredit memberikan tingkat akurasi kebenaran sebesar 78,60% dengan nilai area under the curva (AUC) sebesar 0,907. Sedangkan Performa model random forest dengan backward elimination memberikan tingkat akurasi kebenaran sebesar 89,81% dengan nilai area under the curve (AUC) sebesar 0,922. Hal ini membuktikan bahwa optimasi dengan backward elimination dapat meningkatkan kinerja metode klasifikasi yang digunakan.
References
T. Himberg, “Loan Default Prediction with Machine Learning,” 2020. Doi: 10.1007/978-3-030-37720-5_4.
D. A. A. Al-Sayed, W. A. Q. Awad, and M. T. M. Salem, “a Comparative Study of Forecasting Corporate Credit Ratings Using Artificial Neural Networks, Support Vector Machine, Random Forest, the Naive Bayes, Decision Tree and -Nearest Neighbor,” Adv. Appl. Stat., vol. 91, no. 2, pp. 125–139, 2023. Doi: 10.17654/0972361724010.
M. Lasena and S. R. Ahmad, “Sistem Pendukung Keputusan Kelayakan Pemberian Kredit Nasabah Dengan Metode Electre,” Bull. Inf. Technol., vol. 4, no. 2, pp. 232–238, 2023. Doi: 10.47065/bit.v4i2.690.
A. B. Alfian and A. H. D. Nugroho, “Analisis Sistem Pengendalian Internal Terhadap Efektivitas Pemberian Kredit Kendaraan Bermotor di PT. Indomobil Finance Indonesia Cabang Semarang,” J. Econ. Bussines Account., vol. 7, no. 4, pp. 9071–9084, 2024. Doi: 10.31539/costing.v7i4.8902.
Y. Jin, W. Zhang, X. Wu, Y. Liu, and Z. Hu, “A Novel Multi-Stage Ensemble Model with a Hybrid Genetic Algorithm for Credit Scoring on Imbalanced Data,” IEEE Access, vol. 9, pp. 143593–143607, 2021. Doi: 10.1109/ACCESS.2021.3120086.
Y. Song, “Enterprise Credit Rating Prediction Model Based on Data Mining Algorithm,” in Innovative Computing Vol 1 - Emerging Topics in Artificial Intelligence, J. C. Hung, J.-W. Chang, and Y. Pei, Eds., Singapore: Springer Nature Singapore, 2023, pp. 745–751.
Y. Oktafriani, G. Firmansyah, B. Tjahjono, and A. M. Widodo, “Analysis of Data Mining Applications for Determining Credit Eligibility Using Classification Algorithms C4.5, Naïve Bayes, K-NN, and Random Forest,” Asian J. Soc. Humanit., vol. 1, no. 12, pp. 1139–1158, 2023. Doi: 10.59888/ajosh.v1i12.119.
P. Ziemba, J. Becker, A. Becker, A. Radomska-Zalas, M. Pawluk, and D. Wierzba, “Credit decision support based on real set of cash loans using integrated machine learning algorithms,” Electron., vol. 10, no. 17, pp. 1–22, 2021. Doi: 10.3390/electronics10172099.
A.- Amrin and O.- Pahlevi, “Implementation of Logistic Regression Classification Algorithm and Support Vector Machine for Credit Eligibility Prediction,” J. Informatics Telecommun. Eng., vol. 5, no. 2, pp. 433–441, 2022. Doi: 10.31289/jite.v5i2.6220.
Y. Religia, A. Nugroho, and W. Hadikristanto, “Analisis Perbandingan Algoritma Optimasi pada Random Forest untuk Klasifikasi Data Bank Marketing,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 1, pp. 187–192, 2021. Doi: 10.29207/resti.v5i1.2813.
J. B. E. Putry, A. T. Sasongko, and W. Hadikristanto, “Optimasi Decision Tree Menggunakan Particle Swarm Optimization (PSO) pada Risiko Kredit KMG Bank DKI,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 4, pp. 1403–1410, 2024. Doi: 10.57152/malcom.v4i4.1521.
I. Ubaedi and Y. M. Djaksana, “Optimasi Algoritma C4.5 Menggunakan Metode Forward Selection Dan Stratified Sampling Untuk Prediksi Kelayakan Kredit,” JSiI (Jurnal Sist. Informasi), vol. 9, no. 1, pp. 17–26, 2022. Doi: 10.30656/jsii.v9i1.3505.
S. I. Novichasari, “Peningkatan Akurasi Kelayakan Kredit Menggunakan Particle Swarm Optimization,” J. Multimatrix, vol. 3, no. 1, pp. 86–90, 2021, [Online]. Available: https://jurnal.unw.ac.id/index.php/mm/article/view/1533%0Ahttps://jurnal.unw.ac.id/index.php/mm/article/view/1533/992
A. A. Agustian and A. Bisri, “Data Mining Optimization Using Sample Bootstrapping and Particle Swarm Optimization in the Credit Approval Classification,” Indones. J. Artif. Intell. Data Min., vol. 2, no. 1, pp. 18–27, 2019. Doi: 10.24014/ijaidm.v2i1.6299.
R. Racmatl and R. D. Likliwati, “Optimasi K-Nearest Neighbor Dengan Particle Swarm Optimization Pada Klasifikasi Nasabah Kredit Kendaraan,” J. JTRISTE, vol. 6, no. 1, pp. 9–16, 2019, [Online]. Available: https://jurnal.kharisma.ac.id/jtriste/article/view/89
F. Gorunescu, Data Mining: Concepts, Models, and Techniques. Verlag Berlin Heidelberg: Springer, 2011.
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