Dust Particle Matter (2.5) with Meteorological Parameters to Predict Air Quality Based on Support Vector Machine Approaches

Air condition is influenced by the amount of pollution that occurs in an area such as particle matter, ozone, nitrogen oxide, carbon dioxide, and sulfur dioxide. These pollutants have standard values which have been categorized as AQI (Air Quality Index). At a certain concentration level these pollutants can directly affect human health. The disease can result in reducing the value of productivity of humans. Therefore, by using a prediction system for particulate dust (2.5) it can reduce diseases that result from poor air quality. We then do comparative study by comparing Support Vector Machine. This study also uses 9730 training data and 15 testing data. After testing, it can be concluded that the process of predicting dust particulates (2.5) based on air quality has an accuracy rate of 95.76%.