Contribution Principal Component Analysis to Optimizing Data by Reducing Product Data on Transaction

Abstract

Principal component analysis is to analyze the observation data table into a new data table that has the same correlation. And the aim is to simplify the previously complex observation data so that it is easier to process or analyze. The dataset used is transaction data which is often used by the association method in sales analysis, where the data taken consists of 1397 types of products sold in 1200 transactions. In this data, there are products that have very small sales, which means that the percentage of these products has very little effect on the future process, namely sales analysis with the association method. Therefore the authors try to optimize the data to become ready to use data by reducing products that have a small percentage value that affects research for the dataset. And on this occasion the author uses the main component analysis method to reduce products or form products that can represent the entire dataset without reducing the quality of the data for analysis. From the results of research conducted on transaction data, there was a product decline of 65.21%, where the products totaling 1397 were reduced to 486 products that could represent without reducing their value.