MultiClass Decision Forest Machine Learning Artificial Intelligence

  • Bambang Siswoyo STKOM Al-Ma’soem Jatinangor JABAR
Keywords: Artificial intelligence, Machine Learning, Modeling, Multi Class Decesion Forest

Abstract

Artificial intelligence is the best solution in dealing with various fields. The Multi Class Decesion Forest Machine Learning algorithm which is part of artificial intelligence is an interesting field to be applied in the banking field. This research was developed to build a model that can predict and evaluate bankruptcy in the banking industry. The predictor variable is the financial ratio obtained from the publication of the site http://www.idx.co.id. Modeling machine learning with six variables, where five variables as input and one variable as a target. Overall, the Multi Class Decistion Forest Machine Learning is able to train data input-output relationships and modeling behavior well, 92% accuracy value, 92% precision value and 90% under area curve value.

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References

W. Herdinigtyas and L. S. Almilia, ‘Analisis rasio CAMEL terhadap prediksi kondisi bermasalah pada lembaga perbankan perioda 2000-2002’, J. Akunt. dan Keuang., vol. 7, no. 2, pp. 131–147, 2006.

A. F. Irpanto, ‘Analisis pengaruh rasio camel terhadap prediksi kondisi bermasalah pada lembaga perbankan’, Universitas Negeri Malang, 2012.

D. E. Kurniawan, A. Saputra, P. Prasetyawan, and others, ‘Perancangan Sistem Terintegrasi pada Aplikasi Siklus Akuntansi dengan Evaluasi Technology Acceptance Model (TAM)’, J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 2, no. 1, pp. 315–321, 2018.

F. Pedregosa et al., ‘Scikit-learn: Machine learning in Python’, J. Mach. Learn. Res., vol. 12, no. Oct, pp. 2825–2830, 2011.

F. Barboza, H. Kimura, and E. Altman, ‘Machine learning models and bankruptcy prediction’, Expert Syst. Appl., vol. 83, pp. 405–417, 2017.

L. Zhou, K. K. Lai, and J. Yen, ‘Bankruptcy prediction using SVM models with a new approach to combine features selection and parameter optimisation’, Int. J. Syst. Sci., vol. 45, no. 3, pp. 241–253, 2014.

R. P. Hauser and D. Booth, ‘Predicting bankruptcy with robust logistic regression’, J. Data Sci., vol. 9, no. 4, pp. 565–584, 2011.

Y. Hua, J. Guo, and H. Zhao, ‘Deep belief networks and deep learning’, in Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, 2015, pp. 1–4.

A. SETIAWAN MALAKA, ‘MODEL PREDIKSI KEPAILITAN BANK UMUM DI INDONESIA MENGGUNAKAN ALGORITMA BACKPROPAGATION’, J. Ilmu Manaj., vol. 2, no. 4, 2015.

A. S. Malaka, ‘Hartojo,“Model Prediksi Kepailitan Bank Umum Di Indonesia Menggunakan Algoritma Backpropagation,”’ J. Ilmu Manaj., vol. 2, no. 4, pp. 1714–1724, 2014.

J. Yao, N. Teng, H.-L. Poh, and C. L. Tan, ‘Forecasting and analysis of marketing data using neural networks’, J. Inf. Sci. Eng., vol. 14, no. 4, pp. 843–862, 1998.

Y. Mishina, R. Murata, Y. Yamauchi, T. Yamashita, and H. Fujiyoshi, ‘Boosted random forest’, IEICE Trans. Inf. Syst., vol. 98, no. 9, pp. 1630–1636, 2015.

G. Wang, J. Ma, and S. Yang, ‘An improved boosting based on feature selection for corporate bankruptcy prediction’, Expert Syst. Appl., vol. 41, no. 5, pp. 2353–2361, 2014.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, ‘Dropout: a simple way to prevent neural networks from overfitting’, J. Mach. Learn. Res., vol. 15, no. 1, pp. 1929–1958, 2014.

P. Prasetyawan, I. Ahmad, R. I. Borman, Ardiansyah, Y. A. Pahlevi, and D. E. Kurniawan, ‘Classification of the Period Undergraduate Study Using Back-propagation Neural Network’, in 2018 International Conference on Applied Engineering (ICAE), 2018, pp. 1–5.

A. Dzikri and D. E. Kurniawan, ‘Hand Gesture Recognition for Game 3D Object Using The Leap Motion Controller with Backpropagation Method’, in 2018 International Conference on Applied Engineering (ICAE), 2018, pp. 1–5.

Published
2020-01-23
How to Cite
[1]
B. Siswoyo, “MultiClass Decision Forest Machine Learning Artificial Intelligence”, JAIC, vol. 4, no. 1, pp. 1-7, Jan. 2020.
Section
Articles