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

Ryo Nicholas Reinaldo, Saruni Dwiasnati
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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.
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使用决策树算法按公司类别对客户数据进行分类预测(案例研究:PT.)
以 PT Teknik Kreasi Solusindo 公司为例,使用决策树对客户进行分组分类是该公司存在的一个问题。以前,PT Teknik Kreasi Solusindo 公司的客户数据分组分类没有依据,导致分类结果不尽如人意。为了克服这一问题,本研究使用了数据挖掘过程中的一种分类方法,即决策树算法。本研究使用决策树是因为所使用的数据类型离散,而且分类过程简单快捷。研究中使用的数据是 PT Teknik Kreasi Solusindo 2018-2023 年的产品供应属性。获得数据的来源是对 PT Teknik Kreasi Solusindo 公司代表的访谈结果,以及该公司的几份投标文件。根据这些数据,将使用谷歌 Colabolatory 服务进行分类。通过这种方法,决策树方法的准确性将作为理想分类的参考。
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