Prediksi Employee Attrition Menggunakan Metode Decision Tree dan XGBoost dengan Seleksi Fitur ChiSquare
Abstract
Employee attrition adalah peristiwa di mana suatu
perusahaan kehilangan karyawan karena berbagai alasan.
Employee attrition dapat berdampak negatif terhadap
produktivitas dan stabilitas perusahaan, sehingga
perusahaan perlu mengambil langkah pencegahan yang
tepat terhadap terjadinya hal tersebut. Dalam penelitian
ini, metode klasifikasi yang digunakan adalah Decision
Tree dan XGBoost, dengan menerapkan seleksi fitur Chisquare. Metode Decision Tree dipilih karena kemudahan
interpretasi dan implementasinya, sementara XGBoost
dipilih karena memiliki kinerja prediksi yang sangat baik.
Seleksi fitur Chi-square digunakan untuk
mengidentifikasi fitur-fitur yang memiliki hubungan
signifikan dengan fitur target. Evaluasi performa antara
kedua metode dilakukan menggunakan metrik seperti
accuracy, precision, recall, dan f1-score. Hasil penelitian
menunjukkan bahwa metode Decision Tree mencapai
akurasi tertinggi sebesar 93.58% dengan memanfaatkan
20 fitur dengan nilai Chi-square tertinggi. Sementara itu,
metode XGBoost berhasil mencapai akurasi terbaik
sebesar 98.65% dengan memanfaatkan 25 fitur dengan
nilai Chi-square tertinggi. Penggunaan seleksi fitur Chisquare secara signifikan meningkatkan performa model
prediksi. Hal ini menunjukkan bahwa model dengan
metode XGBoost lebih unggul dalam memprediksi
kemungkinan terjadinya employee attrition dibandingkan
dengan metode Decision Tree.
Kata kunci: employee attrition, prediksi, decision tree, xgboost, chi-square
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