Assessing National Reading Habits through Machine Learning

Insights from the Indonesian Reading Interest Rate Survey (2020–2023)

Abstract

Reading interest is a vital component of educational development, yet many regions face low engagement in reading activities. This study employs advanced machine learning methods to analyze and predict provincial reading interest trends in Indonesia (2020–2023). We performed classification and regression analyses using top-performing models, including CatBoost, LightGBM, XGBoost, Random Forest, ExtraTrees, k-Nearest Neighbors, and neural networks. Classification models categorized provinces by reading interest level with exceptional accuracy, reaching up to 100% using ensemble neural networks. Regression models predicted continuous reading interest index scores precisely, achieving a root mean square error (RMSE) around 1.0 on a 0–100 scale. Our findings demonstrate that modern machine learning approaches effectively uncover underlying patterns in reading interest data. Additionally, we observed a notable decline in reading interest in 2021, coinciding with the COVID-19 pandemic, highlighting digital disruption effects. Ensemble tree-based models and neural networks exhibited superior performance, capturing both linear and non-linear relationships in the dataset, whereas simpler methods (e.g., k-NN) underperformed. This aligns with prior research emphasizing the impact of digital media on reading habits and literacy development. By leveraging predictive analytics, educators and policymakers can proactively identify declining reading interest and implement targeted interventions to foster sustained reading engagement in an increasingly digital world.