Prediksi Peningkatan Jumlah Nasabah Deposito Berjangka Menggunakan Algoritma KNN, Decision Tree, Random Forest Dan Xgboost
DOI:
https://doi.org/10.22441/incomtech.v13i2.16921Keywords:
Deposito Berjangka, KNN, Decision Tree, Random Forest, XGBoost,Abstract
Bank merupakan sebuah lembaga keuangan yang umumnya didirikan untuk menghimpun dana dari masyarakat dalam bentuk simpanan dan menyalurkan kepada masyarakat dalam bentuk kredit atau bentuk lainnya dengan rangka meningkatkan taraf hidup rakyat banyak. Pada penelitian ini, dilakukan pengujian empat algoritma machine learning yaitu K-Nearest Neighbor (K-NN), Decision Tree, Random Forest dan XGBoost, untuk mengetahui dan membandingkan tingkat akurasi dari masing-masing algoritma tersebut dalam melakukan prediksi terhadap peningkatan jumlah nasabah deposito berjangka bank. Pada penelitian ini dataset diperoleh dari UCI Machine Learning Repository. Data yang diperoleh kemudian diproses. Dari hasil pengujian didapatkan tingkat akurasi terbaik sebesar 92,36% dengan menggunakan algoritma XGBoost.
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