Analisa Prediksi Turnover Karyawan menggunakan Machine Learning
DOI:
https://doi.org/10.32877/bt.v7i2.1999
Keywords:
Data Imbalance, Employee Attrition, Machine Learning, Random Forest, SVM
Abstract
Penelitian ini membahas penerapan machine learning untuk memprediksi turnover karyawan, yang merupakan tantangan utama dalam manajemen Sumber Daya Manusia (SDM). Turnover karyawan sering kali disebabkan oleh berbagai faktor, termasuk ketidakseimbangan kehidupan kerja, ketidakpuasan kerja, dan minimnya peluang pengembangan karier. Dalam penelitian ini, digunakan dataset IBM HR Analytics untuk menganalisis faktor-faktor yang memengaruhi turnover karyawan. Algoritma yang diterapkan meliputi Support Vector Machine (SVM) dan Random Forest. Proses penelitian dimulai dengan pengumpulan data, eksplorasi awal, praproses data, seleksi fitur, dan penyeimbangan data menggunakan teknik Synthetic Minority Over-sampling Technique (SMOTE). Evaluasi kinerja model dilakukan menggunakan confusion matrix untuk mengukur akurasi, presisi, recall, dan f1-score. Hasil analisis menunjukkan bahwa algoritma Random Forest memberikan kinerja yang lebih baik dibandingkan SVM. Random Forest mencapai akurasi 97,72%, sedangkan SVM memperoleh akurasi 92,51%. Setelah menerapkan SMOTE, akurasi meningkat menjadi 97% untuk Random Forest dan 93% untuk SVM. Selain akurasi, Random Forest juga unggul dalam metrik presisi, recall, dan f1-score, membuktikan keandalannya dalam memprediksi turnover karyawan. Temuan ini menegaskan bahwa pendekatan machine learning dapat digunakan untuk memahami pola turnover secara lebih mendalam. Dengan prediksi yang lebih akurat, perusahaan dapat merancang strategi retensi karyawan yang lebih efektif dan berbasis data, menciptakan lingkungan kerja yang mendukung produktivitas serta meningkatkan stabilitas tenaga kerja secara keseluruhan.
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