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

Billy Montolalu

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.

Keywords


Financial distress; Altman Zscore; Fuzzy C-Means; data mining

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References


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DOI: http://dx.doi.org/10.25139/ojsinf.v4i2.1792

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