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All Journal Jurnal Informatika & Teknologi Cerdas (JITC)
Nuur Bachtiar, Adnan
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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KNNDT Analisis Perbandingan Kinerja Model K-Nearest Neighbors dan Decision Tree untuk Prediksi Pengeluaran Nasabah Shindy Yuliyatini; Olga Pangaribuan, Via; Nuur Bachtiar, Adnan
Jurnal Informatika & Teknologi Cerdas Vol 1 No 2 (2025): Jurnal Informatika & Teknologi Cerdas (JITC)
Publisher : Program Studi Teknik Informatika Universitas Paramadina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51353/sy3myf10

Abstract

Customer expenditure prediction is a crucial aspect of financial data analysis, helping banking institutions better understand consumer behavior. This study compares the performance of two machine learning algorithms, K-Nearest Neighbors (KNN) and Decision Tree, in predicting customer expenditures. The dataset used consists of 2,567 transaction records from a single customer at Bank BCA. The performance of both models is evaluated using three key metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results show that the KNN algorithm outperforms the Decision Tree by producing lower prediction errors across all evaluation metrics, making it more effective for this predictive task.
Co-Authors Olga Pangaribuan, Via Shindy Yuliyatini