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All Journal Journal of Dinda : Data Science, Information Technology, and Data Analytics Nuansa Informatika
Zain, Rofi Nafiis
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Computer Science & IT

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Predicting the Happiness Index Based on the HDI Indicator in Indonesia Using the Ensemble Learning Approach: Prediksi Indeks Kebahagiaan Berdasarkan Indikator IPM di Indonesia Menggunakan Pendekatan Ensemble Learning Pane, Syafrial Fachri; Zain, Rofi Nafiis; Setiawan, Iwan; Putratama, Virdiandry
NUANSA INFORMATIKA Vol. 19 No. 2 (2025): Nuansa Informatika 19.2 Juli 2025
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v19i2.410

Abstract

Machine Learning is used to analyze complex data in various fields of research. In this study, we applied an ensemble learning approach consisting of Random Forest Regression (RF), XGBoost Regression (XGB), Decision Tree Regression (DT) and Pearson correlation analysis as well as Shapley Additive Explanations (SHAP) to analyze the relationship between the HDI and Happiness indicators in Indonesia. Second, building a prediction model with an ensemble learning approach, namely stacking, which consists of several algorithms including RF, XGB, DT. The results of this study, one, based on the results of Pearson correlation analysis, Permutation Importance (PI), and SHAP, show that the happiness score of Indonesian people has a strong correlation with the Human Development Index variable. The Pearson correlation result shows a value of 0.88, which indicates a very strong positive relationship between HDI and happiness. In addition, the Permutation Importance and SHAP analysis also confirms that HDI is one of the most influential variables in predicting happiness scores in Indonesia. Second, the performance model for predicting happiness using stacking regressors with an R-Squared value of 97.68\%, MAE 0.002900, MSE 0.000021, and RMSE 0.004604.
Enhancing Prediction Accuracy of the Happiness Index Using Multi-Estimator Stacking Regressor and Web Application Integration Zain, Rofi Nafiis; Harani, Nisa Hanum; Pane, Syafrial Fachri
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i2.1871

Abstract

This study proposes a novel approach to enhance the prediction accuracy of the Happiness Index using a multi-estimator stacking regressor model and web application integration. By combining diverse regression models, such as decision tree, random forest, gradient boosting, LGBM, and support vector regressor (SVR), the proposed ensemble architecture achieved superior predictive performance with an score of 0.9814. A custom Happiness Score was formulated using weighted indicators derived from Pearson’s correlation analysis. Furthermore, SHapley Additive exPlanations (SHAP) were used to interpret model predictions, revealing the Human Development Index, Female Labour Force Rate, and Life Expectancy as key contributing features. The final model was deployed via a Python Flask-based web dashboard, enabling stakeholders to visualize happiness metrics interactively. The results suggest that stacking-based regression, when combined with interpretability techniques and real-time deployment, can offer a powerful solution for socioeconomic modeling and supporting urban policy.
Co-Authors Harani, Nisa Hanum Iwan Setiawan Putratama, Virdiandry Syafrial Fachri Pane, Syafrial Fachri