Prediksi Financial Distress Menggunakan Model Artificial Neural Network pada Perusahaan Perhotelan yang Terdaftar di BEI Tahun 2017-2021

Natasya Salsabila Nafis, Farida Titik Kristanti, Hanif Kurniawan Atmanto

Abstract


This study aims to determine early warning models, differences in calculation results, and prediction results of financial distress using the Artificial Neural Network (ANN) model on data testing of hotel industry companies listed on the Indonesia Stock Exchange (IDX) from 2017-2021. In this study, researchers used quantitative research methods where the results obtained from this study were in the form of numbers or data whose values were numbered. The population in this study are companies Hotels, Resorts & Cruise Lines which are listed on the Indonesia Stock Exchange in 2017-2021 with a total of 17 companies. The data processing procedure in this study begins with calculating financial ratios using Microsoft Excel, then analyzing descriptive statistics, and testing the artificial neural network model using the PYTHON programming language to predict financial distress conditions in companies.


Keywords


artificial neural networks; financial distress; hospitality industry.

Full Text:

PDF

References


Altman R et al, 1986. The American Collage of Rheumatology criteria For the classification and Reporting of osteoarthritis of the knee. Arthritis Rheum, 29, 1039-1049

Abdullah, M. 2021. The implication of machine learning for financial solvency prediction: an empirical analysis on public listed companies of Bangladesh. Journal of Asian Business and Economic Studies, 28(4), 303–320.

Adisa, J., Ojo, S., Owolawi, P., & Pretorius, A. 2019. Financial Distress Prediction: Principle Component Analysis and Artificial Neural Networks. Proceedings. International Multidisciplinary Information Technology and Engineering Conference, 1-6. IMITEC 2019.

Alamsyah, A., Kristanti, N., & Kristanti, F. 2021. Early warning model for financial distress using Artificial Neural Network. IOP Conference Series: Materials Science and Engineering, 1098(5), 2-6.

Annisa, H., Rochmah, H., & Ekasari, W. 2022. Pengaruh tata kelola dan kinerja perusahaan terhadap financial distress pada perusahaan consumer goods industry. Jurnal Akuntansi Aktual, 9(2), 96–110.

Chen, W., & Du, Y. 2009. Using neural networks and data mining techniques for the financial distress prediction model. Expert Systems with Applications, 4075–4086.

Dutapersadajogja.com. 2021. Fakta Pertumbuhan Industri Perhotelan di Indonesia. Retrieved from Duta Persada Jogja: https://www.dutapersadajogja.com/categories/detail/199/fakta-pertumbuhan-industri-

Fahmi, I. 2012. Pengantar Managemen Keuangan, (1st ed.). Bandung: ALFABETA.

Fitri, M., & Dillak, V. 2020. Arus Kas Operasi, Leverage, Sales Growth Terhadap Financial Distress. Jurnal Riset Akuntansi Kontemporer, 12(2), 60-64.

Fan, A., Palaniswami, M. 2000. Selecting bankruptcy predictors using a support vector machine approach. Paper presented at 2000 IEEE-INNS-ENNS International Joint Conference on Neural Networks.

Hanafi, M., & Halim, A. 2016. Analisis Laporan Keuangan, 5th ed. Yogyakarta: UPPSTIM YKPN.

Harahap, S. 2015. Analisis Kritis atas Laporan Keuangan. Jakarta: Raja Grafindo Persada.

Hariningsih, E., & Harsono, M. 2019. Kajian Kritis Kontribusi Signaling Theory di Area Penelitian Online Commerce.

Kristianto, H., & Rikumahu, B. 2019. A cross model telco industry financial distress prediction in Indonesia: Multiple discriminant analysis, logit and artificial neural network. 7th International Conference on Information and Communication Technology, 1-5.

Lorenz et al., 2022, Generation of four iPSC lines from four patients with Leigh syndrome carrying homoplasmic mutations m.8993T > G or m.8993T > C in the mitochondrial gene MT-ATP6, Stem Cell Res., 61,

Marso, S., & Merouani, M. E. 2020. Predicting financial distress using hybrid feedforward neural network with cuckoo search algorithm. Procedia Computer Science, 1134–1140.

Ohlson, J. A., 1980. Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, New York: 18(1), 109–131

Odom, M. and Sharda, R. 1990, A Neural Network for Bankruptcy Prediction. International Joint Conference on Neural Networks, 2, 163-168.

Paule-Vianez, J., Gutiérrez-Fernández, M., & Coca-Pérez, J. 2020. Prediction of financial distress in the Spanish banking system: An application using artificial neural network. Applied Economic Analysis, 28(82), 69-87.

Sugiyono. 2019. Metode Penelitian Kuantitatif, Kualitatif, dan R&D. Alphabet.

Syuhada, P., Muda, I., & Rujiman, F. 2020. Pengaruh Kinerja Keuangan dan Ukuran Perusahaan Terhadap Financial Distress Pada Perusahaan Property dan Real Estate di Bursa Efek Indonesia. Jurnal Riset Akuntansi Dan Keuangan, 8(2), 319–336.

Wu, D., Ma, X., & Olson, D. 2022. Financial distress prediction using integrated Z-score and multilayer perceptron neural networks. Decision Support Systems.

Wulandari, E. W., & Jaeni. 2021. Faktor-Faktor Yang Mempengaruhi Financial Distress. JIUBJ: Jurnal Ilmiah Universitas Batanghari Jambi, 21(2).

Zmijewski, M. 1984. Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research, 22, 59-82.




DOI: http://dx.doi.org/10.33087/jiubj.v23i3.3902

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

ADRESS JOURNAL

JURNAL ILMIAH UNIVERSITAS BATANGHARI JAMBI (JIUBJ)
Published by Lembaga Penelitian dan Pengabdian kepada Masyarakat
Adress: Jl.Slamet Ryadi, Broni-Jambi, Kec.Telanaipura, Kodepos: 36122, email: jiubj.unbari@gmail.com, Phone: 0741-670700

Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.