Data to Knowledge-Based Transformation

Abstract

The objectives of this study is to analyze a large amount of data that often appears to create a knowledge base that can be utilized by firm to enhance their decision support system. The authors used the association rules with rapid miner software, data mining approach, and predictive analysis that contains various data exploration scenarios. The study provides important evidence for adopting data mining methods in the industrial sector and their advantages and disadvantages. Chevron Pacific Indonesia (CPI) has a type of computer maintenance activity. Currently, a numerous errors often occur due to the accuracy in computer maintenance which has a major impact on production results. Therefore, this study focuses on association rules using growth patterns that often appear on variables that have been determined into the algorithm (FP-growth) which results in knowledge with a 100% confidence value and a 97% support value. The value results of this study has support and trust are expected to become knowledge for top management in deciding evergreen IT-business routines.