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
Received: 8 Jan 2018; Published: 30 Apr 2018.
Open Access
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Section: Research Articles
Language: IND
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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

Keywords
Bankruptcy Prediction; Financial Ratios; Data Mining; Artificial Neural Network

Article Metrics:

  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).