Analysis of Air Pollution Standard Index Using Support Vector Machine Algorithm - Fitra Hidayat Lubis; Raissa Amanda Putri; M Fakhriza (Building of Informatics, Technology and Science (BITS), 2026)
Air pollution is one of the major environmental problems in urban areas, including Medan City, Indonesia. The Air Pollution Standard Index (Indeks Standar Pencemar Udara / ISPU) data provided by the Environmental Agency is often difficult for the public to interpret due to its numerical format. This study aims to analyze and classify air quality using the Support Vector Machine (SVM) algorithm and present the results through data visualization. The dataset used in this research is secondary data obtained from the Environmental Agency of Medan City, including pollutant parameters such as PM10, PM2.5, SO₂, NO₂, CO, O₃, and HC. The research method follows a quantitative descriptive approach, including data preprocessing, ISPU calculation based on government regulations, classification using SVM, and visualization using graphical methods such as line charts, bar charts, and heatmaps. The results indicate that SVM is effective in classifying air quality categories into Good, Moderate, Unhealthy, Very Unhealthy, and Hazardous. Additionally, visualization techniques improve the interpretability of air quality data, making it easier for stakeholders and the public to understand environmental conditions. This study contributes to decision support systems for environmental monitoring and public awareness.