Implementation Of Support Vector Regression In Prediction Air Quakity Index In Banda Aceh City - Rizky Fasya Ramdhani; Rozzi Kesuma Dinata; Ar Razi (Faculty of Engineering, Malikussaleh University, 2024)
Air quality is one of the important aspects in maintaining environmental balance and public health. Increasing air quality in the environment is a matter of concern. Therefore, a method that can predict the Air Quality Index (AQI) effectively is needed to be able to monitor and support decision making on environmental impacts. This study aims to predict the Air Quality Index in Banda Aceh City using the Support Vector Regression algorithm, with five main parameters used in the study, namely particulate matter , Sulfur dioxide, Nitrogen dioxide, Carbon monoxide , and Ozone . In this research, the Support Vector Regression algorithm was chosen because of its ability to handle non-linear data and also because it can provide accurate predictions on data. The prediction system designed will be web-based using the flask framework and MySQL database, while the Support Vector Regression modeling will be done on google colab for the media used. In the process of modeling the data will be divided into 80% training data and 20% test data to ensure the model can capture long and short-term patterns. The results of the prediction will be compared using the Root Mean Squarred Error (RMSE) and Mean Squarred Error (MSE) evaluation metrics. The results of the evaluation using both metrics yielded RMSE values of 1.9001 and MSE of 3.6015. These values indicate good performance of the model in predicting the data. This research is expected to provide insight for future similar research in terms of prediction using the Support Vector Regression algorithm.