PREDIKSI FINANCIAL DISTRESS MENGGUNAKAN MODEL NEURO FUZZY DAN RASIO ALTMAN
DOI:
https://doi.org/10.22441/jimb.v6i1.7273Keywords:
Financial Distress, Predicted, Altman Ratio, Neuro-Fuzzy, Time Series Data.Abstract
Financial distress is a condition of decline in corporate finance before business failure. This failure is closely related to insolvency, where the company has failed in carrying out its operations for profit. Financial distress prediction methods have long been developed by researchers in the field of accounting and finance using Multiple Discriminant Analyst (MDA). A common method is that the Altman model is used to predict financial distress one year in the future, this is as an early warning of the company's financial condition.The purpose of this study is to develop a data mining application neuro fuzzy algorithm model using the Altman model input data ratio to predict the company's financial distress for the next few years optimally. The data used are financial statements from 45 LQ45 Issuers on the Indonesia Stock Exchange (IDX) in 2013-2017. The initial process of calculating the balance sheet and profit (loss) financial statements through the Altman model ratio to get zscore values of three categories (safe, gray and distress). The values are sorted by four backward periods as input data to the model. Functional tools from the Matlab GUI program will be used for the formation, training and testing of the neuro fuzzy structure of the ANFIS 4MFs model. Furthermore, the predicted value of the model is compared with the value of the calculation of the target Altman ratio. The final results show that the ANFIS 4MFs model can provide highly optimal predictive values close to the average target ratio value of 100% in the membership functions of Gauss, Trapezoid, and G-bell.
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References
A. Baroroh, “Analisis Multivariat dan Time Series dengan SPSS 21.
Altman, Edward I. "Predicting financial distress of companies: revisiting the Z-score and ZETA models." Stern School of Business, New York University: 9-12.
Azizah, N., 2016. Metode Adaptive Neuro Fuzzy Inference System (ANFIS) untuk Prediksi Tingkat Layanan Jalan. Jurnal DISPROTEK, 7(1).
Bachir, O. and Zoubir, A.F., 2012. Adaptive Neuro-fuzzy inference system based control of Puma 600 robot manipulator. International Journal of Electrical and Computer Engineering, 2(1), p.90.
Bisht, D.C. and Jangid, A., 2011. Discharge modelling using adaptive neuro-fuzzy inference system. International Journal of Advanced Science and Technology, 31(1), pp.99-114.
Brigham, F. Eugene, Houston, and F. Joel 2011. Dasar-dasar Manajemen Keuangan, 11th ed. Penerjemah Ali.
Chaniago, R. and Wardani, K.R.R., 2015. Prediksi Cuaca Menggunakan Metode Case Based Reasoning dan Adaptive Neuro Fuzzy Inference System. Jurnal Informatika, 12(2), pp.90-95.
Eddy Herjanto, 2007. Manajemen Operasi, Penulis Edisi Ketiga. Jakarta: PT Raja Grasindo Persada.
Hanafi, Mamduh M. 2007. Analisa Laporan Keuangan, UPP AMP YKPN, Yoygyakarta.
Hasanah, H. and Nurmalitasari, N., 2017. Perancangan Aplikasi Sistem Cerdas Untuk Prediksi Energi Listrik Pemakaian Sendiri Di PT Indonesia Power Sub Unit Plta Kabupaten Wonogiri. Prosiding SNATIF, pp.33-41.
Heizer, Jay & Barry Render. 2009. 8th ed. Operation Management. Upper Saddle River, New Jersey : Prentice Hall.
Jang, J.S., 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), pp.665-685.
K. Dewi and S. Hartati, Neuro-Fuzzy 2006. Integrasi Sistem Fuzzy dan Jaringan Syaraf, 2nd ed. Yogyakarta: Graha Ilmu.
Munawir, Slamet 2002. Akuntansi Keuangan dan Manajemen, Edisi Pertama. Yogyakarta: BPFE.
Nikentari, N., Bettiza, M. and Pratiwi, H.S., 2018. Prediksi Kecepatan Angin Menggunakan Adaptive Neuro Fuzzy (ANFIS) dan Radial Basis Function Neural Network (RBFNN). JEPIN (Jurnal Edukasi dan Penelitian Informatika), 4(1), pp.70-75.
Nilawati, L. and Wahyudi, M., 2015. Penilaian Properti Menggunakan Metode ANFIS. Konferensi Nasional Ilmu Pengetahuan dan Teknologi, 1(1), pp.123-128.
Puspitasari, I. and Sutijo, B., 2013. Model Selection in Adaptive Neuro Fuzzy Inference System (ANFIS) by using Inference of R2 Incremental for Time Series Forecasting.
S. Kusumadewi and H. Purnomo 2010. Aplikasi Logika Fuzzy untuk Pendukung Keputusan, 2nd ed. Graha Ilmu. Yogyakarta.
Samarakoon, L.P. and Hasan, T., 2003. Altman’s Z-Score models of predicting corporate distress: Evidence from the emerging Sri Lankan stock market. Journal of the Academy of Finance, 1, pp.119-125.
Siddiqui, S.A., 2012. Business bankruptcy prediction models: A significant study of the Altman’s Z-score model. Available at SSRN 2128475.
Takagi, T. and Sugeno, M., 1985. Fuzzy identification of systems and its applications to modeling and control. IEEE transactions on systems, man, and cybernetics, (1), pp.116-132.
Toto, Prihadi. 2011. Analisis Laporan Keuangan Teori dan Aplikasi. Jakarta: PPM.
website IDX ; http://www.idx.co.id
website Kompas ; http://www.kompas.com.
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