<![CDATA[This study aims to compare the performance of basic statistical approaches and machine learning in identifying environmental, social, and governance (ESG) issues that influence the stability of the rupiah through the Indonesian interest Rate. This study proposes two approaches: machine learning analysis (long short-term memory (LSTM) and multiple long short-term memory (M-LSTM)) and fundamental statistical models (autoregressive integrated moving average (ARIMA), transfer function, and Koyck regression) to forecast the Indonesian interest rate time series data based on ESG factors. The analysis uses a completely randomized design to capture complex patterns, utilizing interest rate data, PM2.5 air quality data, and rainfall index data. This study provides novelty through data splitting scenarios in its empirical analysis and a comparative study of approaches to the model. The analysis results indicate that ESG factors, specifically air quality (PM2.5) and the rainfall index, have a significant influence on Indonesian interest rate values, as determined by the transfer function results, Koyck regression, and M-LSTM model analyses. All proposed model approaches demonstrate good forecasting accuracy, empirically proven by MAPE values with MAPE<10%. The MAPE values of the basic statistical models (Koyck regression, transfer function, and ARIMA) are 0.2842%, 1.0350%, and 2.1245%, outperforming machine learning models (LSTM and M-LSTM) with MAPE values of 4.660% and 7.7353%.]]>
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