Predictive Modeling of Jakarta's Social Cohesion: GBDT Leads Comparative Analysis

Abstract

In this study, we address the challenge of predicting the Social Cohesion Index in Jakarta through a comprehensive analysis of machine learning models. Finding the most accurate and effective predictive model for this crucial urban evaluation task is the primary goal of our research. We use a variety of machine learning algorithms, comparing their performance using metrics like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and computational cost. These algorithms include Gradient Boosted Decision Trees (GBDT), Polynomial Regression, Random Forest, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). It should be noted that GBDT stands out as a top performer, regularly displaying outstanding accuracy with a competitive MAE of 0.692, RMSE of 0.887, and MAPE of 25.59%. The computational efficiency of GBDT is also impressive, with predictions taking only 0.05 seconds. These results underscore the potential of GBDT as a practical and precise tool for real-time assessments of social cohesion in large urban environments like Jakarta. The findings offer a data-driven way to guide policy decisions and community development activities, with important implications for urban planning and governance. Overall, this research emphasizes the promise of GBDT in boosting social cohesion evaluation approaches and increases our understanding of the application of machine learning in addressing complex urban difficulties.