Driver Predicting Behavior Based on Accelerometer and Gyroscope Sensors with K-Means Algorithm Method

Michael Sitorus; Melisa Melisa; Desty Herninda

doi:10.31004/riggs.v2i1.29

Authors

Michael Sitorus BRI Institute
Melisa Melisa Information System Departement, BRI Institute
Desty Herninda Information System Departement, BRI Institute

DOI:

https://doi.org/10.31004/riggs.v2i1.29

Keywords:

Accelerometer, Gyroscope, Sensor, K-Means Algorithm, Prediction

Abstract

The transportation with an increasing population level in Indonesia will also affect the smoothness of traffic. However, with the existence of transportation that is owned by someone, it is often misused in driving. Aggressive driving behavior is a major factor in traffic accidents. As reported AAA Foundation for Traffic Safety, 106,727 dangerous accidents – 55.7 percent of the over past four year period involved drivers engaging in one or more acts of aggressive driving. Therefore, to predict driver behavior, the K-Means algorithm used the Orange Application. Using the driver behavior dataset that has been recorded with Accelerometer and Gyroscope Sensor-based applications, the results obtained where the k value is the number of cluster 1 there are 814 items, cluster 2 has 997 items, cluster 3 has 1273 items. From this test, it was found that proportion of predicted driver behavior with Aggressive qualifications was 96.4%, Normal was 92.2% and from driver behavior with Low qualifications was 74.3%. The accuracy rate of research using the K-Means algorithm to determine predictions is 0.861 or 86.1%. The results of predictions can help prevent accidents or other risks that will occur in the future.

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