Implementation of Decision Tree Algorithm for Activity Recommendations Based on Air Quality Index (AQI) and PM2.5 Pollution in Indonesia

The increasing air pollution in major cities across Indonesia has raised serious public health concerns. This research aims to develop a recommendation system for daily activities based on the Air Quality Index (AQI) and PM2.5 levels. Using the Decision Tree algorithm, this study categorizes air quality conditions and provides appropriate activity recommendations, such as whether it is safe to exercise outdoors or if it is better to stay indoors. The model utilizes AQI and PM2.5 data collected from various Indonesian cities. The results indicate that the Decision Tree algorithm is effective in providing accurate activity recommendations based on air quality, demonstrating significant accuracy in classifying air conditions. The implementation of this system is expected to aid individuals in making informed decisions about their daily activities, thereby mitigating health risks associated with air pollution exposure. The urgency of this research lies in the need for a more adaptive and personalized system to provide activity recommendations based on real-time air quality data. Amid the rising cases of respiratory illnesses and diseases related to air pollution exposure, this study plays a crucial role in supporting a healthier and safer lifestyle. In addition, the implementation of this system can serve as a foundation for public policy and environmental risk mitigation strategies that are more data-driven and technology-based in the future.