Open AccessApplying Machine Learning for Improving Performance Classification on Driving Behavior
Author(s) -
Ahmad Iwan Fadli,
Selo Sulistyo,
Sigit Basuki Wibowo
Publication year - 2021
Publication title -
ijitee (international journal of information technology and electrical engineering)
Language(s) - English
Resource type - Journals
ISSN - 2550-0554
DOI - 10.22146/ijitee.56919
Subject(s) - support vector machine , computer science , random forest , machine learning , artificial intelligence , multilayer perceptron , preprocessor , data pre processing , feature extraction , global positioning system , naive bayes classifier , data mining , artificial neural network , telecommunications
Traffic accident is a very difficult problem to handle on a large scale in a country. Indonesia is one of the most populated, developing countries that use vehicles for daily activities as its main transportation. It is also the country with the largest number of car users in Southeast Asia, so driving safety needs to be considered. Using machine learning classification method to determine whether a driver is driving safely or not can help reduce the risk of driving accidents. We created a detection system to classify whether the driver is driving safely or unsafely using trip sensor data, which include Gyroscope, Acceleration, and GPS. The classification methods used in this study are Random Forest (RF) classification algorithm, Support Vector Machine (SVM), and Multilayer Perceptron (MLP) by improving data preprocessing using feature extraction and oversampling methods. This study shows that RF has the best performance with 98% accuracy, 98% precision, and 97% sensitivity using the proposed preprocessing stages compared to SVM or MLP.