ANALYSIS OF HEALTH LEVEL OF COMMERCIAL BANK USING MULTIPLE LINEAR REGRESSION AND TIME SERIES
Banks are business institutions that are closely intertwined with the lives of the community and play a crucial role in the economic sustainability of a country. In Indonesia, several types of banks are recognized by law, namely the Central Bank, Commercial Banks, and Rural Credit Banks. The health...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/83539 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Banks are business institutions that are closely intertwined with the lives of the community and play a crucial role in the economic sustainability of a country. In Indonesia, several types of banks are recognized by law, namely the Central Bank, Commercial Banks, and Rural Credit Banks. The health of a bank reflects its condition and performance. Therefore, good bank health conditions are necessary for banks to effectively carry out their tasks. In this final project, we discuss discusses the analysis of bank health levels using a mathematical model of multiple linear regression and time series to predict future bank health. The data used in this final project includes the financial ratios of commercial banks, namely CAR, NPL, LDR, ROA, NIM and BOPO. The time series model employed is the ARIMA model. The method used for parameter estimation in multiple linear regression and ARIMA time series is the least squares method, while the financial ratios of commercial banks are weighted using the RGEC method. Based on this research, the predicted health scores of Bank M for the next four quarters are 96,64; 96,06; 95,99 and 95,9, while the predicted health scores of Bank CN for the next four quarters are 94,53; 94,39; 94,53 and 94,49, indicating that Bank M is in better health for the next four quarters. |
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