BIG DATA ANALYSIS MODEL PROFITABILITY RATIO IN DETERMINING PREDICTION OF COMPANY PERFORMANCE ERA 4.0
Abstract
In 2011 Indonesia entered a new era of industry 4.0, in which in the following years, opportunities and challenges began to be felt. If you do not adjust for the changes, it is feared that they will be left behind and disrupted. Therefore it is very important for management at the end of each period to pay close attention to the company's financial performance, in order to maintain stability and to anticipate not getting into serious problems. Company performance is the result of management activities through the financial statement information approach. Performance analysis measures the success of the company's operations through financial ratios from financial reports. Performance analysis is needed by management, creditors and investors as an early warning provider of the company's future business conditions. On that basis, how to make Big data model application for prediction determines company performance. This study aims to develop a Big data model application for predicting the determination of company performance using the k-Nearest Neighbor (k-NN) algorithm using profitability ratio input data. Research data from the Indonesia Stock Exchange (IDX). The population of data for 2010-2019 was 13 automotive manufacturing industries. Processing of financial statements of profitability ratios for the past four periods as model input data. The functional tools classify the Matlab GUI program are used as a model structure for training and testing. The output of the model is compared with the target profitability ratio, the results show that the model's output is very optimal and accurately approaches the target profitability average of 92%.
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