Analisis Faktor dan Prediksi Atrisi untuk Optimalisasi Retensi Karyawan Menggunakan Machine Learning
DOI:
https://doi.org/10.32877/bt.v7i3.2301Keywords:
Atrisi Karyawan, Feature Importance, K-nearest neighbors, Random Forest, Retensi Karyawan
Abstract
Atrisi karyawan merupakan fenomena penurunan jumlah tenaga kerja dalam sebuah organisasi yang disebabkan oleh faktor-faktor seperti pengunduran diri, pensiun, atau alasan lainnya. Fenomena ini dapat berdampak negatif pada perusahaan, termasuk penurunan produktivitas, gangguan operasional, dan meningkatnya biaya rekrutmen serta pelatihan. Penelitian ini bertujuan untuk menganalisis faktor-faktor yang mempengaruhi atrisi karyawan dan mengembangkan model prediksi menggunakan algoritma machine learning, yaitu Random Forest dan K-Nearest Neighbors (KNN). Adapun penelitian ini menggunakan dataset IBM HR Analytics Employee Attrition & Performance. Metode penelitian melibatkan tahap pengumpulan data, pemrosesan data, pelatihan model menggunakan algoritma Random Forest dan KNN, serta evaluasi kinerja model berdasarkan akurasi, precision, recall, F1-score, AUC, dan ROC curve. Hasil penelitian menunjukkan bahwa algoritma Random Forest memiliki akurasi 93% dan nilai AUC sebesar 0.98, lebih tinggi dibandingkan dengan KNN yang hanya mencapai akurasi 88% dan AUC 0.96. Selain itu, Random Forest menunjukkan kinerja yang lebih seimbang pada precision, recall, dan F1-score, serta lebih rendah dalam kesalahan prediksi pada kelas "Atrisi" dan "Tidak Atrisi". Pada analisis feature importance mengidentifikasi faktor utama yang mempengaruhi atrisi karyawan, seperti RelationshipSatisfaction, Work-Life Balance, Age, StockOptionLevel, dan NumberofCompaniesWorked. Temuan ini memberikan kontribusi penting bagi perusahaan dalam merancang strategi retensi yang lebih efektif dengan memanfaatkan data yang ada. Penelitian ini juga merekomendasikan penggunaan dataset yang lebih besar, serta penerapan algoritma dan teknik lain seperti SMOTE untuk meningkatkan akurasi model dalam prediksi atrisi di masa depan.
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