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Penelitian berfokus pada perbandingan algoritma klasifikasi dengan kombinasi tahapan preprocessing, khususnya dimensionality reduction dan feature selection menggunakan Variance threshold serta Correlation coefficient. Model yang diuji adalah Logistic Regression, Decision Tree, dan Naïve Bayes. Decision Tree menghasilkan performa tertinggi dengan F1 Score 96% dan akurasi 93%, sedangkan Logistic Regression dan Naïve Bayes berada di peringkat berikutnya. Tahapan preprocessing tidak menunjukkan pengaruh signifikan terhadap F1 Score dan akurasi.","Analisis Perbandingan Algoritma Machine Learning untuk Prediksi Potensi Hilangnya Nasabah Bank  \nApplication of Machine Learning to Predict Potential Loss of Bank Customer  \nMohammad Farid Naufal 1, Subrata2, Alvin Fernando Susanto3, Christian Nathaneil Kansil4  \nSolichul Huda5  \n1,2,3,4 Program Studi Teknik Informatika, Universitas Surabaya, Surabaya, Jawa Timur  \n5Program Studi Teknik Informatika, Universitas Dian Nuswantoro [E-mail:](E-mail:1faridnaufal@staff.ubaya.ac.id)[1](E-mail:1faridnaufal@staff.ubaya.ac.id)[faridnaufal@staff.ubaya.ac.id](E-mail:1faridnaufal@staff.ubaya.ac.id), [2](2s160420002@student.ubaya.ac.id)[s160420002@student.ubaya.ac.id](2s160420002@student.ubaya.ac.id), [3](3s160420013@student.ubaya.ac.id)[s160420013@student.ubaya.ac.id](3s160420013@student.ubaya.ac.id), [4](4s160420069@student.ubaya.ac.id)[s160420069@student.ubaya.ac.id](4s160420069@student.ubaya.ac.id), [5](5solichul.huda@dsn.dinus.ac.id)[solichul.huda@dsn.dinus.ac.id](5solichul.huda@dsn.dinus.ac.id)  \nAbstrak  \nNasabah adalah salah satu aset paling berharga dari sebuah bisnis perbankan. Mereka adalahujung tombak pengguna produk yang nantinya memberikan keuntungan bagi bank, terutamapadaproduk kartu kredit. Penelitian ini bertujuan untuk mengetahui nasabah mana sajakah yang berpotensi untuk meninggalkan layanan kartu kredit dari sebuah bank. Pada penelitian sebelumnya belum ada yang melakukan analisis perbandingan algoritma machine learning dengan berbagai macam tahapan preprocessing untuk memprediksi potensi hilangnya nasabah bank. Penelitian ini melakukan analisis perbandingan algoritma machine learning dengan kombinasi tahapan preprocessing untuk memprediksi potensi hilangnya nasabah bank. Analisis ini penting untuk pemilihan algoritma yang paling cocok untuk prediksi potensi hilangnyanasabah bank. Pada tahapan preprocessing diterapkan dimensionality reduction dan featureselection menggunakan metode Variance threshold dan Correlation coefficient. Metode klasifikasi yang digunakan adalah Logistic regression (LR), Decision tree (DT), dan Naïve Bayes (NB). Hasil tertinggi dari ketiga metode tersebut adalah Decision tree yang mampu memiliki nilai F1 Score sebesar 96% dan nilai akurasi mencapai 93% . Logistic regression dan Naïve Bayes berada pada urutan kedua dan ketiga setelah decision tree. Tahapan data preprocessing tidak memberikan pengaruh yang signifikan pada nilai F1 Score dan akurasi.  \nKata kunci: Klasifikasi, Bank, Nasabah, Hilang  \nAbstract  \nCustomers are one of the most valuable assets of a banking business. They are the spearhead of product users who will provide benefits for banks, especially in credit card products. This study aims to find out which customers have the potential to leave credit card services from a bank. In previous studies, no one has conducted a comparative analysis of machine learning algorithms with various preprocessing stages to predict the potential loss of bank customers. This study performs a comparative analysis of machine learning algorithms with a combination of preprocessing stages to predict the potential loss of bank customers. This analysis is important for selecting the most suitable algorithm for predicting potential loss of bank customers. At the preprocessing stage, dimensionality reduction and feature selection are applied using the Variance threshold and Correlation coefficient methods. The classification methods used are Logistic regression (LR), Decision tree (DT), and Naïve Bayes (NB) algorithms. The highest result of the three methods is the Decision Tree which is able to have an F1 score of 96% and an accuracy value of 93%. Logistic regression and Naïve Bayes are second and third after the decision tree. It was also found that the presence or absence of data preprocessing stages did not have a significant effect on the F1 score and accuracy.  \nKeywords: Classification, Banks, Customers, Loss  \n1. PENDAHULUAN  \nBerkembangnya zaman yang begitu cepat menjadikan manusia memiliki kebutuhan","cbCaimpQgPmDuOV5","https://ap.wps.com/l/cbCaimpQgPmDuOV5","pdf",2050865,4,1,20,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang churn nasabah dan kebutuhan analisis berbasis machine learning\n# Metodologi\n## Preprocessing: dimensionality reduction dan feature selection\n## Metode klasifikasi: Logistic Regression, Decision Tree, Naïve Bayes\n# Hasil dan Pembahasan\n## Perbandingan performa berdasarkan F1 Score dan akurasi\n## Dampak tahapan preprocessing terhadap performa","[{\"question\":\"Tujuan utama penelitian ini apa?\",\"answer\":\"Menentukan nasabah mana yang berpotensi meninggalkan layanan kartu kredit bank melalui analisis perbandingan algoritma machine learning dengan tahapan preprocessing.\"},{\"question\":\"Tahapan preprocessing apa yang digunakan dalam penelitian ini?\",\"answer\":\"Dimensionality reduction dan feature selection menggunakan Variance threshold dan Correlation coefficient.\"},{\"question\":\"Algoritma mana yang memberikan hasil terbaik dan berapa nilainya?\",\"answer\":\"Decision Tree memberikan hasil tertinggi dengan F1 Score 96% dan akurasi 93%.\"}]","Analisis Perbandingan Algoritma Machine Learning untuk Prediksi Potensi Hilangnya Nasabah Bank | 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utama penelitian ini apa?","Question",{"text":75,"@type":76},"Menentukan nasabah mana yang berpotensi meninggalkan layanan kartu kredit bank melalui analisis perbandingan algoritma machine learning dengan tahapan preprocessing.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Tahapan preprocessing apa yang digunakan dalam penelitian ini?",{"text":80,"@type":76},"Dimensionality reduction dan feature selection menggunakan Variance threshold dan Correlation coefficient.",{"name":82,"@type":73,"acceptedAnswer":83},"Algoritma mana yang memberikan hasil terbaik dan berapa nilainya?",{"text":84,"@type":76},"Decision Tree memberikan hasil tertinggi dengan F1 Score 96% dan akurasi 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