A comparative study on Whole Body Vibration (WBV) comfort towards compact car model through data mining approach

Nowadays people of Malaysian spend a significant amount of time traveling by the vehicle to travel from one location to another location, and this could be the main reason to decrease minimal vibration for the comfort level in transportation. The vibration that generated while driving can influence pressure and eliminate the focus to the driver and passenger, and this is one of the main causes that can lead accidents on the roads. In this study, we investigate the effect of the vibration caused by the tire interaction with the road surface. The methodology focuses on the trends which occur on the vibration exposure that has been generated throughout the engine operating rpm range in both stationary and non-stationary conditions. An equation will be approached through the analysis to find the significant data that can be used in the process which is K-Means algorithm. Based on the trends of the experienced and exposed vibration, the model is able to differentiate the level of comfort between the clusters by grouping the level of vibration into five categories. To review the accuracy of classification data cluster, the K-Nearest Neighbor method and Analysis Linear Discriminant is used for shows the percentage accuracy of classification data have been a cluster. Later, the vibration for the three cars in this study which has analyzed, compared using the approach of analysis of variations (ANOVA).