Data Exploration Using Tableau and Principal Component Analysis

Hanna Parhusip - Universitas Kristen Satya Wacana, Salatiga, Indonesia
Suryasatriya Trihandaru - Universitas Kristen Satya Wacana, Salatiga, Indonesia
Adrianus Heriadi - Universitas Kristen Satya Wacana, Salatiga, Indonesia
Petrus Santosa - Universitas Kristen Satya Wacana, Salatiga, Indonesia
Magdalena Puspasari - Universitas Kristen Satya Wacana, Salatiga, Indonesia

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This study aims to determine the dominant chemical elements that may improve the monitoring of the productivity and efficiency of heavy engines in 2015-2021 in the company. The method used is usually Scheduled Oil Sampling. This article proposes a new approach. The research problems are analyzing the recorded chemical elements that are produced by heavy engines and visualizing them through the Tableau program. The basic design of the study is learning the given data after visualization and using the Principal Component Analysis. This method is to obtain chemical elements that affect engine wear during each engine's use in the 2015-2021 period. Because there are three categories in each element in the oil sample, namely wear metals, contaminants, and oil additives, a technique is needed to obtain these elements using Principal Component Analysis. Therefore, Oil Sampling Analysis through data exploration using Tableau resulted in a new approach to data analysis of elements recorded by heavy vehicles. The main findings as a result of the analysis are given by the visualization of Tableau, in which there are five machines analyzed to obtain the main components that cause engine wear. From the visualization results, it is shown that there is one engine coded MSD 012 that experienced wear and tear in 2018 and 2019. This shows where two main components, Ca and Mg, dominate engine wear. These results have been confirmed with the related companies. The company then carried out further studies on the machine to get special treatment because of these results.


Oil; heavy equipment; Tableau; principal component analysis.

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