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Top1. Introduction
Screening some data is necessary by mining large amounts of data to gain obtain quality knowledge. In a corporate organization, top management can use this quality knowledge to make good decisions to implement company business processes. one way to do data mining is by association rules by mining a lot of data to find useful information and knowledge. Association analysis is one of the four (4) core data mining tasks besides cluster analysis, predictive modeling and anomaly detection (Hale, 1981).
Data mining is the research area where the huge dataset in database and data repository are scoured and mined to find novel and useful pattern (Man et al., 2016). Chevron Pacific Indonesia (CPI) is a company engaged in the exploration of oil and gas (Development of H.R, 2019). CPI operates in the block area of Rokan Sumatera Riau province. The success of exploration activities is not freelancing work with the role of information technology that supports companies’ activities. Information technology (IT) as an instrumental to facilitate the workers in receiving information (KAWANO, 1997) (Development of H.R, 2019) and monitoring activities of the special oil and gas explorations in the area of Duri city (Riau Province).
Monitoring generally refers to the observation, regulation, control, and reporting of processes, procedures, team work, and persons (Nebeker & Tatum, 1993) (Bhave, 2014). Monitoring concerns about employee privacy and activities; therefore, employers must find a balance between monitoring gains and employee activities (Moussa, 2015) (Ding et al., 2019). One of IT’s roles is to carry out evergreen activities. The function of evergreen activities is for HMI personal computer (PC) or HMI treatment. HMI PC is working to facilitate the operator of monitoring directly from control room. HMI PC used in oil exploration field in the area of Rokan block amounted to approximately 180.
PC HMI is what is used in carrying out evergreen oil and gas exploration activities. It is in evergreen activities that data mining association rules are carried out to find itemsets or patterns that often appear. The FP Tree Transformation that was formed consisted of 180 evergreen PC HMI data indicating variables that often appear in these activities (See Figure 1). This discovery is a huge challenge and has a strong and enduring tradition in data mining. It is a fundamental part of many data mining applications including market basket analysis, web link analysis, genome analysis and molecular fragment mining (Man et al., 2016).
In this paper, the authors develop research from previous studies that only focused on 20 samples of PC HMI and did data mining manually according to the applicable formula. This paper will use 180 PC HMI data to demonstrate the overall improvement of the data mining process using the knowledge domain in several stages. The test will be carried out with the rapid miner application. The problem that occurs at Chevron Pacific Indonesia is the difficulty of the CPI in understanding the rules of knowledge and seeing what trends exist in evergreen activities carried out at PC HMI. So, in this study, we tested whether the Association Rule Algorithm FP-Growth method could be used to solve problems in evergreen activity data. The results of this study are expected to help leaders at PT. Chevron Pacific Indonesia on how to develop a more appropriate computer maintenance policy. This study is structured as follows: introduction, literature review, methods, results and analysis, and conclusions.