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All Journal IJITEE (International Journal of Information Technology and Electrical Engineering)
Ahmad Iwan Fadli
Universitas Gadjah Mada
Electrical & Electronics Engineering

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Applying Machine Learning for Improving Performance Classification on Driving Behavior Ahmad Iwan Fadli; Selo Sulistyo; Sigit Wibowo
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 4, No 1 (2020): March 2020
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijitee.56919

Abstract

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.
Co-Authors Selo Sulistyo, Selo Sigit Wibowo