| Autor: |
Kelvin, Kelvin, Cindy, Cindy, Charles, Charles, Leonardo, Denny Peter, Yennimar, Yennimar |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Jurnal Mantik; Vol. 4 No. 2 (2020): Augustus: Manajemen, Teknologi Informatika dan Komunikasi (Mantik); 1285-1290 |
| ISSN: |
2685-4236 |
| DOI: |
10.35335/mantik.Vol4.2020 |
| Popis: |
Nowadays the competition between companies is increasing. Companies need to predict their customers to find out the level of customer loyalty. One way is to analyze customer data by doing Customer Churn Prediction. In this study the method used is the FP-Growth Algorithm. The FP-Growth algorithm is an algorithm that uses the association rules technique to determine the data that appears most frequently. The data used in this study are secondary data and have 7,403 data from customers. The data has 21 variables. By using a minimum support of 1.2% and confidence at 80%, the associative rules generated are 60. The variable of the type of internet the customer has is strong enough to predict churn. It can be seen that of the 60 associative rules, there are 36 associative rules that have this variable. Testing associative rules on test data yields an accuracy of 71%. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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