DOI: https://doi.org/10.21456/vol8iss1pp107-114

Analisis Prediksi Kebangkrutan Perusahaan Menggunakan Artificial Neural Network Pada Sektor Pertambangan Batubara

*Rizki Amalia Nurdini  -  Telkom University, Indonesia
Yudi Priyadi  -  Telkom University, Indonesia
Norita .  -  Telkom University, Indonesia
Open Access Copyright (c) 2018 JSINBIS (Jurnal Sistem Informasi Bisnis)

Citation Format:
Abstract

Indonesia’s coal mining industry has been decreased since the last five years and causing the financial performance of companies in the industry to deteriorate. The aim of this paper is to analyze the bankruptcy prediction on coal mining sector companies listed in Indonesia Stock Exchange (IDX) in 2012 – 2016 using data mining prediction method that is artificial neural network model with three financial ratios as an input parameter. The financial ratios used are shareholder’s equity ratio, current ratio and return on assets. The results indicate that these ratios are very suitable to be used as an input parameter because it shows a quite significant difference in calculation results between bankrupted and non-bankrupted companies.The ANN training model used in the prediction process in this study resulted in the best training performance with the model architecture of 15 neurons on input layer and one hidden layer with 30 neurons in it. The training model produces training performance with the lowest MSE of 0,000000313 and the highest R of 99,9%. Bankruptcy prediction result using ANN showed that 7 (seven) coal mining sector companies are predicted to be bankrupt

Fulltext View|Download
Keywords: Bankruptcy Prediction; Financial Ratios; Data Mining; Artificial Neural Network
Funding: Telkom University

Article Metrics:

Article Info
Section: Research Articles
Language : IND
Recent articles
Penerapan Model Promosi Berbasis Web Captive Portal Hotspot dengan Manajemen Terpusat Klasifikasi Opini Masyarakat Terhadap Jasa Ekspedisi JNE dengan Naïve Bayes Sistem Pengendali Inventori Supply Chain Dengan Pendekatan Probabilitas Pada Industri Pakaian More recent articles
Most cited articles
Support Vector Machine Untuk Klasifikasi Citra Jenis Daging Berdasarkan Tekstur Menggunakan Ekstraksi Ciri Gray Level Co-Occurrence Matrices (GLCM) Sistem Informasi Pengukuran Kinerja Pada Perkebunan Kelapa Sawit Dengan Menggunakan Metode Balanced Scorecard Sistem Informasi Geografis Berbasis Web untuk Pemetaan Sebaran Alumni Menggunakan Metode K-Means Sistem Pendukung Keputusan Penilaian Proposal Kegiatan PNPM MPd Menggunakan Metode Profile Matching dan Analytic Hierarchy Process (AHP) Penerapan Green Supply Chain Management Untuk Peningkatan Kinerja Keuangan Perusahaan More cited articles
  1. Akers, M., Bellovary, J., Giacomino, D., 2007. A Review of bankrupty prediction studies: 1930 to present. Journal of Financial Education, 33(5), 1-42
  2. Almilia, L.C., Kristijadi, 2003. Analisis Rasio Keuangan Untuk Memprediksi Financial Distress Perusahaan Manufaktur yang Terdaftar di Bursa Efek Jakarta. JAAI, 7(2)
  3. Brédart, X., 2014. Bankruptcy prediction model using artificial neural networks. Journal Accounting and Finance Research, 3(2)
  4. Djuriš, J., Medarević, D., Krstić, M., Vasiljević, I., Mašić, I., Ibrić, S., 2012. Design space approach in optimization of fluid bed granulation and tablets compression process. The Scientific World Journal
  5. Gordon, M.J., 1971. Towards a theory of financial distress. The Journal of Finance, 26, 347-356
  6. Hapsari, E.I., 2012. Kekuatan rasio keuangan dalam memprediksi kondisi financial distress perusahaan manufaktur di BEI. Jurnal Dinamika Manajemen, 3(2), 101 -109
  7. Haq, S., Arfan, M., Siswar, D., 2013. Analisis rasio keuangan dalam memprediksi financial distress (studi pada perusahaan yang terdaftar di Bursa Efek Indonesia). Jurnal Akuntansi Pascasarjana Universitas Syiah Kuala, 2(1), 37-46
  8. Harahap, S.S., 2015. Analisis Kritis Atas Laporan Keuangan. Jakarta, Rajawali Pers
  9. Hu, Y.C., Ansell, J., 2005. Developing Financial Distress Prediction Models. Management School and Economics, University of Edinburgh, 1 -22
  10. Indrawati, 2015. Metode Penelitian Manajemen dan Bisnis Konvergensi Teknologi Komunikasi dan Informasi. Bandung, Refika Aditama
  11. Investopedia, 2017. Shareholder’s Equoty Ratio. http://www.investopedia.com/terms/s/shareholdersequity.asp, diakses tanggal 10 Oktober 2017
  12. Jones, F., 1987. Current techniques in bankruptcy prediction. Journal of Accounting Literature, 6, 131-164
  13. Larose, D.T., 2006. Data Mining, Methods and Models. New Jersey, John Wiley & Sons
  14. Mansouri, A., Nazari, A., Ramazani, M., 2016. A Comparison of artificial neural network model and logistic regression in prediction of companies’ bankruptcy (A Case of Tehran Stock Exchange). International Journal of Advanced Computer Research, 6(24)
  15. Mcleod, R., 2002. Introduction to Information System: A Problem Solving Approach. United States, Pergamun Press
  16. Munawir, 2014. Analisa Laporan Keuangan. Yogyakarta, Liberty
  17. Nasser, Aryati, 2002. Model analisis camel untuk memprediksi financial distress pada sektor perbankan yang go public. Jurnal Akuntansi dan Auditing Indonesia, 11-127
  18. Paquet, P., 1997. L’utilisation des réseaux de neurons artificiels en finance. Working Paper University of Orleans
  19. Platt, H., Platt, M. B., 2002. Predicting financial distress. Journal of Financial Service Professionals, 56
  20. Prasetyo, E., 2014. Data Mining Mengolah Data Menjadi Informasi Menggunakan Matlab. Yogyakarta, Andi
  21. Refait, C., 2004. La prévision de la faillite fondée sur l’analyse financière de l’entreprise: un état des lieux. Economie, & prévision, 162(1), 129-147
  22. Saleh, A., Sudiyatno, B., 2013. Pengaruh rasio keuangan untuk memprediksi probabilitas kebangkrutan pada perusahaan manufaktur yang terdaftar di Bursa Efek Indonesia. Jurnal Dinamika Akuntansi, Keuangan dan Perbankan, 2(1), 82-91
  23. Saleh, E., Noor, E., Djatna, T., Irzaman, 2013. Prediksi Masa Kadaluwarsa Wafer dengan Artificial Neural Network (ANN) Berdasarkan Parameter Nilai Kapasitansi. Agritech, 33(4)
  24. Salehi, M., Shiri, M.M., Pasikhani, M.B., 2016. Predicting corporate financial distress using data mining techniques an application in tehran stock exchange. International Journal of Law and Management, 58(2), 216- 230
  25. Salehi, M., Pour, M.D., 2016. Bankruptcy prediction of listed companies in Tehran Stock Exchange. International Journal of Law and Management, 58(5)
  26. Stack Exchange, 2014. What is the difference between test set and validation set. https://stats.stackexchange.com/questions/19048/what-is-the-difference-between-test-set-and-valid
  27. ation-set, diakses tanggal 10 Oktober 2017
  28. Sugiyono, 2013. Metode Penelitian Kuantitatif dan R&D. Bandung, Alfabeta
  29. Supardi, Mastuti, S., 2003. Validitas Penggunaan Z-Score Altman Untuk Menilai Kebangkrutan pada Perusahaan Perbankan Go Public di Bursa Efek Jakarta. Kompak, Januari-April
  30. Titman, S., Keown, A., Martin, J., 2011. Financial Management: Principles and Application (11th ed.). United States, Pearson Education, Inc
  31. Widarjo, W., Setiawan, D., 2009. pengaruh rasio keuangan terhadap kondisi financial distress perusahaan otomotif. Jurnal Bisnis dan Akuntansi, 11(2), 107-119
  32. Widodo, P.P., Handayanto, R.T., Herlawati, 2013. Penerapan Data Mining dengan Matlab. Bandung, Rekayasa Sains
  33. Weygandt, J., Kimmel, P., Kieso, D., 2011. Financial Accounting (IFRS ed.). Hoboken, New Jersey., United States, John Wiley & Sons, Inc
  34. Yashpal, S., 2009. Neural Networks in Data Mining. United Institute of Management, Allahabad, India, 41
  35. Yuliastary, E.C., Wirakusuma, M.G., 2014. Analisis financial distress dengan metode Z-Score Altman, Springate, Zmijewski. E-Jurnal Akuntansi Universitas Udayana, 6(3)
  36. Yuanita, I., 2010. Prediksi financial distress dalam industri textile dan garment. Jurnal Akuntansi dan Manajemen, 5(1)

Last update:

No citation recorded.

Last update: 2024-12-25 21:27:50

No citation recorded.