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This study aims to analyze the impact of the Domestic Investment program (PMDN), Bank Indonesia interest rate (BI Rate), and exchange rate on the Jakarta Composite Index (JCI) in Indonesia. To test the relationship between the independent variable (PMDN, BI Rate, and exchange rate) and the dependent variable (JCI) over a certain period of time, multiple regression analysis method is used. This research uses data obtained from various reliable and relevant sources. The findings of this study are expected to provide a deeper understanding of the factors that influence JCI in Indonesia and provide insight for policymakers to improve the investment climate. The conclusions and policy recommendations generated from this study are expected to be able to make a positive contribution to Indonesia's economic development in the long term. [ABSTRACT FROM AUTHOR]

The purpose of this study is to determine and examine if cotton imports into Indonesia are influenced by cotton prices, cotton consumption, cotton production and exchange rate.. This study falls under the category of quantitative research. Data on cotton prices, cotton consumption, cotton production, exchange rate, and cotton imports make up this study population. The study sample consists of yearly data from Indonesia organized into timeseries that are consecutive and span the years 2003 through 2020. The secondary data documentation approach is the data gathering strategy used in this study. The analysis technique used is multiple linear regression analysis which meets the BLUE (Best Linear Unbiased Estimate) assumption. Based on the statistical t test results, the variable Cotton Prices (X1) 3,299 > ttable 2,160, Cotton Consumption (X2) = 6,398 > ttable 2,160, Cotton Production (X3) = 4,324 > ttable 2,160, and that the Exchange Rate (X4) = 3,128 > ttable 2,160 has an effect on Cotton Imports (Y) in Indonesia. The result of statistical testing indicates that H0 is rejected when Fcount > Ftable. The research found that although the four variables Cotton Prices (X1), Cotton Consumption (X2), Cotton Production (X3), and Exchange Rates (X4) had a simultaneous and limited influence on Imports of Cotton (Y), the Cotton Prices (X1) variable had the most impact. [ABSTRACT FROM AUTHOR]