Improving the Classification Model of Smart Indonesia  Program Recipients in Koorwilbidikcam Sumber Using   the C4.5 Algorithm

Sigit Saputra; Nana Suarna; Irfan Ali; Dodi Solihudin

doi:10.59934/jaiea.v4i3.1000

Authors

Sigit Saputra STMIK IKMI Cirebon
Nana Suarna STMIK IKMI Cirebon
Irfan Ali STMIK IKMI Cirebon
Dodi Solihudin STMIK IKMI Cirebon

DOI:

https://doi.org/10.59934/jaiea.v4i3.1000

Keywords:

C4.5 Algorithm, Smart Indonesia Program (PIP), Classification

Abstract

This study aims to implement the C4.5 classification algorithm in determining the recipients of the Smart Indonesia Program (PIP) in the Koorwilbidikcam Sumber area. The main problem faced is the inaccuracy in identifying recipients of assistance which causes suboptimal distribution. This study uses a quantitative approach with data mining techniques, analyzing students' social and economic data. The dataset used consists of 550 student data with variables such as parental occupation, income, means of transportation, and ownership of KIP or SKTM. The classification process is carried out using the C4.5 algorithm with the Knowledge Discovery in Databases (KDD) stages which include data selection, preprocessing, data transformation, and data mining using RapidMiner. The results of the study show that the C4.5 algorithm is able to identify significant patterns in the data and produce decision trees that can be used to support decision making. The implementation of this algorithm improves the accuracy and efficiency of the PIP recipient selection process, as well as reducing subjective bias in the determination. Thus, this study contributes to the development of a fairer and more targeted educational assistance distribution system. The use of data mining-based technology such as the C4.5 algorithm also opens up opportunities for technology integration in decision making in the education sector, so that it can be a model for other areas with similar problems. In this study, it produced an accuracy rate of 85.45% from 550 data and from 110 testing data that had been tested.

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