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This study examines the determinants of the Jakarta Composite Index (JCI) based on three main macroeconomic factors namely inflation, the USD/IDR exchange rate, and the SBI interest rate (BI Rate) covering the period January 2020 to December 2025, in the context of post-COVID-19 pandemic recovery and global economic turmoil. A quantitative approach was employed using the Ordinary Least Squares (OLS) method, with 72 monthly observations derived from secondary data sourced from official institutions including Bank Indonesia (BI), the Central Statistics Agency (BPS), the Indonesia Stock Exchange (IDX), and the Financial Services Authority (OJK). Classical assumption tests were applied comprising the Jarque-Bera normality test, Variance Inflation Factor (VIF) for multicollinearity, Breusch-Godfrey for autocorrelation, White Test for heteroscedasticity, and Ramsey RESET for model specification. Partially, inflation, exchange rate, and BI Rate each demonstrate a positive and significant effect on the JCI (p < 0.05). Simultaneously, all three variables exert a significant combined influence on the JCI, with a coefficient of determination R² = 0.4414, indicating that the model explains 44.14% of the variation in the JCI. The remaining 55.86% is attributed to other variables outside the model. Classical assumption test results reveal violations of normality, autocorrelation, and heteroscedasticity assumptions, although the model is free from multicollinearity. These findings confirm that Bank Indonesia's monetary policy has a significant and measurable impact on capital market performance. Further research is recommended using more advanced time series models such as GARCH or VECM to address violations of classical assumptions and improve estimation efficiency.

The growth of aviation activities at I Gusti Ngurah Rai International Airport, Bali, has led to the rapid development of surrounding areas, potentially obstructing protected airspace. Obstacles on the approach surface of Runway 27 have become a critical concern, particularly for precision approach Category II (CAT II) operations, which require obstacle-free approach areas. This study aims to analyze obstacles within the approach area of Runway 27 and develop effective control strategies. Using a descriptive qualitative approach, data was collected through field observations, interviews, and documentation studies. The analysis follows the Obstacle Limitation Surfaces (OLS) standards according to ICAO and national regulations. The findings reveal obstacles such as mangrove vegetation, antennas, and ship activities in the Benoa Harbor area, which are located within the approach surface and could potentially impact the OLS limits. While these obstacles generally comply with existing regulations, their proximity to the threshold may reduce the safety margin of flight operations and limit CAT II implementation on Runway 27. This study proposes technical, operational, regulatory, and preventive strategies to improve obstacle control, enhancing aviation safety and ensuring the readiness for CAT II operations at the airport.

Multicollinearity is one of the common issues in multiple linear regression that can lead to instability in the estimation of regression coefficients. This study aims to examine the impact of multicollinearity on regression models and to evaluate the use of Ridge Regression as an alternative estimation method. The study employs simulated data consisting of 1,000 observations, including one dependent variable and four independent variables designed to exhibit high correlation. The analysis begins with model estimation using the Ordinary Least Squares (OLS) method, followed by multicollinearity testing using the Variance Inflation Factor (VIF). The OLS results indicate that most independent variables significantly influence the dependent variable, with a coefficient of determination (R²) of 0.9863. However, the high VIF values reveal the presence of strong multicollinearity in the model. To address this issue, Ridge Regression is applied, with the optimal penalty parameter determined through cross-validation, yielding a lambda value of 4.201589. The results show that the regression coefficients in the Ridge model undergo shrinkage, resulting in greater stability compared to the OLS estimates. Model evaluation indicates that the Mean Squared Error (MSE) for the OLS model is 24.77, whereas the Ridge model produces an MSE of 29.72. Although the Ridge model exhibits a slightly higher MSE, it effectively mitigates the impact of multicollinearity and provides more stable parameter estimates.