Ahmad Toha, Purwono Purwono, Windu Gata
Air pollution continues to increase in Jakarta. The city ranks 12th in the world as the capital of a country with high levels of pollution. The Jakarta Environmental Service requires processing air quality data generated by the Air Quality Monitoring Station in order to produce valuable information as a decision-making tool. This data processing can be processed with data mining techniques to seek new knowledge from the database so as to find valid, useful and easy-to-learn patterns. The SVM data mining classification model is proposed in this study. Our contribution in this research is to create a classification model with SVM with new techniques, namely improvements in data processing to perform hyperparameter tuning. We saw that previous researchers only pursued high accuracy scores. In contrast to previous studies, we used the gridsearch cv hyperparameter optimization technique on the SVM classification model. The kernel polynomial with 2 degrees is the best parameter recommendation from the grid search cv technique. The accuracy before optimization is 73,31%, while after optimization is 94,8%. This shows an increase in accuracy of 3.2% after applying the grid search cv method to the classification of air quality monitoring using the SVM model. © 2022, Universitas Ahmad Dahlan. All rights reserved.
Program Studi Ilmu Komputer, Universitas Nusa Mandiri, Indonesia; Program Studi Informatika, Universitas Harapan Bangsa, Indonesia
Research at a Glance
Register to unlock
Topics & SDG Alignment
Register to unlock
Collaboration
Register to unlock
Author Profile (Selected)
Register to unlock
References Overview
Register to unlock
Journal & Source
Register to unlock
Metadata & Integrity
Register to unlock