Aplikasi Data Mining Pada Analisis Financial Distress Model Altman z-score Untuk Memprediksi Potensi Kebrangkutan Pada Industri Properti Go-Public Di Indonesia

  • Institut Teknologi Telkom Surabaya
https://doi.org/10.25139/inform.v4i2.1792
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Abstract

Early thought systems are needed in companies to overcome financial difficulties that can challenge industrial operations. Altman Z Score is one model that can be used to predict financial distress in a company by analyzing the company's financial statements. This research was conducted to analyze financial distress in property companies going public using the Altman Z Score model. In this model there are 5 financial ratio indicators that are used to predict financial distress. The financial report data used is the financial statements for 2015-2016 and there are 23 companies. The results of these calculations are then clustered with Fuzzy C-Means in two, namely safe zone and gray zone. Cluster validation testing uses the Silhouetee Index with a validation value of 0.9541 which indicates that the cluster process is valid. The results of this study indicate that there is one company that is included in the cluster gray zone, namely Intiland Development Tbk. Analysis of financial ratios found that the most influential is the variable X3 where the results of profits before tax are very small can affect payment of obligations. So it's easy to bring up financial distress conditions. And for those companies that have been in the gray zone condition, they are expected to be careful in financial management to anticipate financial distress.

References

Suhendra. Cnnindonesia, 11 Oktober 2015. Diakses pada 13 Juni 2017. http://cnnindonesia.com

Purnandam, Amiyatosh. 2008. Financial Distress And Coorporate Risk Management : Theory And Evidence. Journal Of Financial Economic 87. pp 706-739

Hammouda, Khaled. A Comparative Studi Of Data Klastering Techniques. Journal Universitas Of Waterloo. Ontario. Canada

Firdausi,Nabiladkk. Analisi Financial Distress Dengan Pendekatan Data Mining Pada Industri Manufaktur Go Public Di Indonesia. Jurnal ITS. Surabaya

Febrianti,Fitria. Hafiyussholeh. Ahmad Hanif Ashar. 2016. Perbandingan Pengkluateran Data Iris Menggunakan Metode K-Means Dan Fuzzy C-Means. Jurnal Mantik. Surabaya

Toto, Prihadi . 2011. Analisis Laporan Keuangan Teori Dan Aplikasi ;332. Jakarta. Ppm.

Toto, Prihadi . 2011. Analisis Laporan Keuangan Teori Dan Aplikasi. Jakarta. Ppm.

Sawira, Agnes. 2004. Analisis Kinerja Keuangan Dan Perencanaan Keuangan 6 Perusahaan. Jakarta: PT. Gramedia Pustaka Utama

Toto, Prihadi . 2011. Analisis Laporan Keuangan Teori Dan Aplikasi ;336. Jakarta. Ppm.

Adnan dan Kurniasih. 2005. Tingkat Perusahaan Untuk Memprediksi Potensi Kebangkrutan Dengan Pendekatan Altman. Jurnal Ekonomi Bisnis

Kusumadewi, Sri. Purnomo, Hadi. 2010.Aplikasi Logika Fuzzy Untuk Pendukung Keputusan Eds.2.Yogyakarta. Graha Ilmu

Kusumadewi, Sri hartati. 2006. Neuro fuzzy : integrasi sistem fuzzy dan jaringan syaraf. Yogyakarta. Graha ilmu

Prasetyo,Eko. 2014. (Suhendra, 2015)Data Mining Mengolah Data Menjadi Informasi Menggunakan Matlab. Andi Yogyakarta.

Prasetyo,Eko. 2014. (Suhendra, 2015)Data Mining Mengolah Data Menjadi Informasi Menggunakan Matlab. Andi Yogyakarta.
Published
2019-07-31
How to Cite
(2019). Aplikasi Data Mining Pada Analisis Financial Distress Model Altman z-score Untuk Memprediksi Potensi Kebrangkutan Pada Industri Properti Go-Public Di Indonesia. Inform : Jurnal Ilmiah Bidang Teknologi Informasi Dan Komunikasi, 4(2). https://doi.org/10.25139/inform.v4i2.1792
Issue
Vol. 4 No. 2 (2019)
Section
Volume 4 No. 2 2019

Copyright (c) 2019 Billy Montolalu

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