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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References
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