C45 Algorithm for Motorcycle Sales Prediction On CV Mokas Rawajitu

Jurnal Sisfotek Global

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Field Value
 
Title C45 Algorithm for Motorcycle Sales Prediction On CV Mokas Rawajitu
 
Creator Alita, Debby
Setiawansyah, Setiawansyah
Putra, Ade Dwi
 
Subject
C45 Algorithm, Cross Validation, Rapid Miner, Prediction, Motorcycle Sales

 
Description CV Mokas Rawajitu is a company that sells various types of used motorbikes both in cash and on credit. In sales, the problem that occurs is the frequent occurrence of ups and downs in motorcycle sales due to the mismatch of the available motorcycle variants with consumer interests so that motorcycle sales often do not reach the target. The role of data mining is needed to analyze consumer purchasing patterns at CV Mokas Rawajitu which can produce information, namely knowing what types of motorbikes most in-demand by consumers are and which are most in-demand in the market by predicting using the C4.5 algorithm based on the sales transaction data they have. from previous periods. The study used a dataset of motorcycle sales at CV Mokas Rawajitu from 2017-2019 with a total data volume of 1,411 data. The attributes used are the motorbike category, the motorbike brand, the motorbike price, and the year of production. The tools used in this research are Rapid Miner. The results of the application of the C4.5 Algorithm can be used as a prediction of sales at CV Mokas Rawajitu because the results of the accuracy of testing data and models using 9-Fold Cross Validation reach a value of 87.95% where the 9th fold reaches the highest accuracy value with a Sensitivity level of 97, 15%, 69.05% Specificity, 86.57% Precision, 12.05% Error (Error Rate) and 30.95% False Positive Rate.
 
Publisher STMIK Bina Sarana Global
 
Contributor
 
Date 2021-09-30
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article

 
Format application/pdf
 
Identifier https://journal.stmikglobal.ac.id/index.php/sisfotek/article/view/392
10.38101/sisfotek.v11i2.392
 
Source JURNAL SISFOTEK GLOBAL; Vol 11, No 2 (2021): JURNAL SISFOTEK GLOBAL; 127-134
2721-3161
2088-1762
10.38101/sisfotek.v11i2
 
Language eng
 
Relation https://journal.stmikglobal.ac.id/index.php/sisfotek/article/view/392/pdf
 
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Rights Copyright (c) 2021 JURNAL SISFOTEK GLOBAL
 

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