Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Riset Teknik Komputer
Oktaviana Putri Agung
Universitas Pamulang
Computer Science & IT Industrial & Manufacturing Engineering Other

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
PENERAPAN ALGORITMA RANDOM FOREST DALAM PREDIKSI CUSTOMER CHURN UNTUK MENDUKUNG STRATEGI RETENSI PELANGGAN Oktaviana Putri Agung; Fairuza Mayla Faizal; Irenia Mascharenhas
Jurnal Riset Teknik Komputer Vol. 3 No. 2 (2026): Juni : Jurnal Riset Teknik Komputer (JURTIKOM)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/84q65g10

Abstract

Customer churn is a condition when customers stop using company services within a certain period. A high churn rate can negatively impact company revenue and customer loyalty, especially in telecommunications companies. This study aims to implement the Random Forest algorithm to predict customer churn in order to support customer retention strategies. The dataset used in this study is Telco Customer Churn obtained from Kaggle with a total of 7043 customer records. This research applies the CRISP-DM methodology consisting of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The preprocessing stage includes handling missing values, transforming data types, and encoding categorical data. The modeling process uses the Random Forest Classifier algorithm with an 80:20 split between training and testing data. The results show that the Random Forest model achieved an accuracy of 77% in predicting customer churn. The most influential factors affecting churn based on feature importance are TotalCharges, tenure, MonthlyCharges, and customer contract types. Based on the research results, the Random Forest algorithm can help companies identify customers with churn potential and support more effective customer retention strategies.
Co-Authors Fairuza Mayla Faizal Irenia Mascharenhas