Thermal Comfort Projection on Northern Coast of Central Java Using Machine Learning

Abstract

The Thermal Humidity Index (THI) serves as a critical measure of environmental thermal comfort, particularly vital for living beings in densely populated regions. This study projects and classifies THI in the western northern coastal areas of Central Java using Machine Learning (ML) techniques. Utilizing temperature and humidity data from 2018 to 2024, THI projections were conducted using the XGBoost algorithm, whereas comfort level classifications were performed using the Random Forest algorithm. The results indicate that Semarang City, eastern Kendal, Pemalang, and Tegal frequently experienced slightly uncomfortable conditions (THI 27–30), particularly during the rainy and transitional seasons, whereas other regions maintained comfortable levels (THI < 27). The THI projection model for 2025–2029 achieved an accuracy of 73%, while the classification model attained a remarkably high accuracy of 99.94%. These findings highlight the need for enhanced regional management strategies in areas with reduced thermal comfort.