Machine Learning-Based Prediction of Household Internet Access Across Indonesian Provinces Using Socioeconomic and Temporal Features
Abstract
Unequal household internet access remains an important dimension of the digital divide across Indonesian provinces, requiring predictive approaches that account for both socioeconomic conditions and temporal dynamics. This study aims to predict provincial household internet access and evaluate the added predictive value of previous-year connectivity. Secondary data from Statistics Indonesia covering 34 provinces during 2020–2024 were analyzed using Linear Regression, Random Forest, Gradient Boosting, and XGBoost. Two feature configurations were compared: contemporaneous socioeconomic and ICT predictors and a lag-augmented configuration incorporating previous-year internet access. Model performance was assessed using an out-of-time 2024 holdout, GroupKFold cross-validation, and rolling-origin temporal validation. Adding the lagged feature improved RMSE across all models, with XGBoost achieving the best 2024 holdout performance (RMSE = 2.7260; = 0.4003), representing a 54.49% RMSE improvement over its no-lag counterpart. Previous-year internet access was the most influential feature (55.57%), followed by adjusted expenditure per capita (21.45%) and computer ownership (18.16%). Rolling-origin validation yielded a mean of 0.5866. These findings demonstrate the value of temporal information and temporally appropriate validation for predicting regional internet access and supporting digital inclusion monitoring.
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DOIAuthor Biographies
Department of Informatics Engineering, Faculty of Engineering and Computer Science, Universitas Indraprasta PGRI, South Jakarta City, Special Capital Region of Jakarta, Indonesia.
Department of Informatics Engineering, Faculty of Engineering and Computer Science, Universitas Indraprasta PGRI, South Jakarta City, Special Capital Region of Jakarta, Indonesia.
Department of Informatics Engineering, Faculty of Engineering and Computer Science, Universitas Indraprasta PGRI, South Jakarta City, Special Capital Region of Jakarta, Indonesia.
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