Prediction of Customer Data Classification by Company Category Using Decision Tree Algorithm (Case Study: PT. Teknik Kreasi Solusindo)

Authors

  • Ryo Nicholas Reinaldo Universitas Mercu Buana, Jakarta, Indonesia
  • Saruni Dwiasnati Universitas Mercu Buana, Jakarta, Indonesia

DOI:

https://doi.org/10.38035/ijam.v2i2.285

Keywords:

Classification, Company Data, Data Mining, Decision tree

Abstract

Classification using a decision tree for grouping customers with a case study of PT Teknik Kreasi Solusindo is a problem that exists in the company. Where the classification of customer data grouping in the company PT Teknik Kreasi Solusindo previously had no basis which resulted in the classification results not being entirely good. To overcome this problem, this study uses a classification method that exists in the data mining process, namely the decision tree algorithm. This study uses a Decision tree because the data used has a discrete type and the classification process is simple and fast. The data in the research used are product offering attributes of PT Teknik Kreasi Solusindo from 2018-2023. The source of the data obtained is the results of interviews with representatives from PT Teknik Kreasi Solusindo and also several bidding files in the company. Based on this data, a classification will be carried out with the Google Colabolatory service. And with this method, the accuracy of the decision tree method will be seen as a reference for the desired classification.

 

 

Author Biographies

Ryo Nicholas Reinaldo, Universitas Mercu Buana, Jakarta, Indonesia

 

 

Saruni Dwiasnati, Universitas Mercu Buana, Jakarta, Indonesia

 

 

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Published

2023-07-21

How to Cite

Nicholas Reinaldo, R., & Dwiasnati, S. . (2023). Prediction of Customer Data Classification by Company Category Using Decision Tree Algorithm (Case Study: PT. Teknik Kreasi Solusindo). International Journal of Advanced Multidisciplinary, 2(2), 229–238. https://doi.org/10.38035/ijam.v2i2.285

Issue

Vol. 2 No. 2 (2023): International Journal of Advanced Multidisciplinary (July-September 2023)

Section

Articles

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