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 (JITC)
Vol 1 No 2 (2025): Jurnal Informatika & Teknologi Cerdas (JITC)

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.