Enhancing Prediction Accuracy of the Happiness Index Using Multi-Estimator Stacking Regressor and Web Application Integration

Rofi Nafiis Zain; Nisa Hanum Harani; Syafrial Fachri Pane

doi:10.20895/dinda.v5i2.1871

Rofi Nafiis Zain Universitas Logistik dan Bisnis Internasional
Nisa Hanum Harani Universitas Logistik dan Bisnis Internasional https://orcid.org/0000-0003-2218-4165
Syafrial Fachri Pane Universitas Logistik dan Bisnis Internasional https://orcid.org/0000-0001-5119-3808

DOI: https://doi.org/10.20895/dinda.v5i2.1871

Keywords: Prediction, Happiness, Stacking Regressor, SHAP, Web-based Application

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.

Author Biography

Syafrial Fachri Pane, Universitas Logistik dan Bisnis Internasional

Syafrial Fachri Pane was born in Medan, North Sumatra in April 1988. He obtained his bachelor of informatics degree from Pasundan University and master of informatics from Bina Nusantara University, Bandung, in 2019 and 2021, respectively. Currently, she is pursuing her doctoral program at Telkom University, Bandung. He is involved in data science and machine learning research. He is also a lecturer at the University of Logistics and International Business (ULBI), Bandung. His research interests include data analytics and machine learning. His research dissertation focuses on Hybrid Multi-objective Metaheuristic Machine Learning for Pandemic Modelling.