Open AccessAir Quality Classification in Urban Environment using Machine Learning Approach
Author(s) -
Faqih Hamami,
Iqbal Ahmad Dahlan
Publication year - 2022
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/986/1/012004
Subject(s) - air quality index , decision tree , random forest , air pollution , hyperparameter , logistic regression , machine learning , pollution , computer science , task (project management) , quality (philosophy) , artificial intelligence , environmental science , meteorology , geography , engineering , ecology , philosophy , chemistry , organic chemistry , systems engineering , epistemology , biology
Air pollution comes from human activities that can threaten living things. It is affected by gasses including PM 10 , SO 2 , CO, O 3 , NO 2 and others. Air pollution leads to dangerous diseases even death. Monitoring air quality is important task to understand pollution concentration. Air quality monitoring is better when it can classify whether air quality is habitable or not. This research proposes air quality classification using classification algorithms such as Logistic Regression, KKN, Decision Tree, and Random Forest algorithm. Dataset was taken from Jakarta’s open data for 12 months with several attributes including gas concentration and with several pre-processing steps. Based on experiment, decision tree model has the best accuracy to classify air quality level up to 100% with tuning several hyperparameters.