Zakah Management System Using Approach Classification

Hasanuddin, Zulfajri Basri; Syarif, Syafruddin; Darniati, Darniati

doi:10.12928/telkomnika.v15i4.5640

Cited by 1 publication

(1 citation statement)

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“…The Naive Bayes experiments in the study give performances as: the recall is 83.70%, the precision is 83.60% and the accuracy is 83.67%. Another interesting of naïve Bayes application for classification purpose is presented in [10]. In the study author present the result of the Zakah receiver classification experiment that utilizes the naïve Bayes classifier.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Classification Method on Customer Candidate Data to Predict its Potential Risk

Sadikin¹,

Alfiandi²

2018

IJECE

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Leasing vehicles are a company engaged in the field of vehicle loans. Purchase by way of credit becomes a mainstay because it can attract potential customers to generate more profit. But if there is a mistake in approving a customer candidate, the risk of stalled credit payments can happen. To minimize the risk, it can be applied the certain data mining technique to predict the future behavior of the customers. In this study, it is explored in some data mining techniques such as C4.5 and Naive Bayes for this purpose. The customer attributes used in this study are: salary, age, marital status, other installments and worthiness. The experiments are performed by using the Weka software. Based on evaluation criteria, i.e. accuracy, C4.5 algorithm outperforms compared to Naive Bayes. The percentage split experiment scenarios provide the precision value of 89.16% and the accuracy value of 83.33% wheres the cross validation experiment scenarios give the higher accuracy values of all used k-fold. The C4.5 experiment results also confirm that the most influential instant data attribute in this research is the salary.

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Section: Introductionmentioning

confidence: 99%