Combination of Decision Tree and K-Means Clustering Methods for Decision Making of BLT Recipients in the Covid-19 Period

Abstract

The economic conditions during the Covid-19 outbreak had an impact on society globally. The number of people who have experienced layoffs has an impact on the economic conditions of the family. The economic impact that helps the community encourages the government to increase efforts to increase social assistance in the form of BLT. However, the distribution of BLT was not right on target, there were still many people who really could not afford not to receive BLT, while those who were still able to get BLT assistance. Therefore, it is important in this study to use a combination of the K-Means Cluster and Decision Tree methods to be used in BLT recipient decision making, with the aim of increasing BLT recipients as expected. The calculation results were obtained using a combination of the K-Means Cluster and Decision Tree methods referring to the criteria for the community who has the right to receive data with an error level of -2.476190476 <from error tolerance 6.84.

Evaluation of ATM Location Placement Using the K-Means Clustering in BNI Denpasar Regional Office

“…Based on the problems described previously, the purpose of this study is the strategic ATM placement evaluation system by implementing the K-Means method. Several previous studies related to the implementation of K-Means Clustering have also been successfully carried out, including for grouping sales data, decision-making of BLT recipients, beef production clustering, and clustering poverty data field (Halawa & Hamdani, 2019;Kusanti & Sutanto, 2021;Pandiangan, 2019;Sugianto & Bokings, 2021). Furthermore, this study also aims to test K-Means' effectiveness in increasing business potential and business benefits in evaluating BNI ATM placements.…”