Implementation Of the C4.5 Algorithm With The Backward Elimination Feature Selection For MSME Product Sales Strategy

M. Rudi Fanani; Elvinda Bendra Agustina

doi:10.59934/jaiea.v3i3.491

Authors

M. Rudi Fanani Program Studi Teknologi Informasi, Fakultas Sains dan Teknologi, Institut Teknologi dan Sains Nahdlatul Ulama Pekalongan
Elvinda Bendra Agustina Program Studi Fisika, Fakultas Sains dan Teknologi, Institut Teknologi dan Sains Nahdlatul Ulama Pekalongan

DOI:

https://doi.org/10.59934/jaiea.v3i3.491

Keywords:

C4.5 Algorithm, Forward Selection, Data Mining, Product Sales.

Abstract

The development of Micro, Small and Medium Enterprises (MSMEs) in Indonesia has become a major focus in advancing the economy, reducing poverty and improving community welfare. MSMEs not only play a strategic role in creating jobs and driving the local economy, but are also a source of inspiration for innovation and creativity. Kedungwuni Timur sub-district is an area in Kedungwuni sub-district, Pekalongan Regency, Central Java. The profession of the majority of Kedungwuni Timur Village residents is operates in the MSME Fashion sector. The current problem is that consumers can easily compare products. Therefore, it is necessary to carry out a sales strategy by utilizing Information Technology concepts, one of which is using Data Mining techniques. The C4.5 algorithm is a data mining algorithm that is used to predict the sales strategy for MSME products with an accuracy value of 82.78%. To increase the accuracy value, the backward elimination feature was used to produce an accuracy of 85.00%.

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