Optimasi Hyperparameter Algoritma Random Forest dan Gradient Boosting untuk Evaluasi Kinerja Karyawan Berbasis Rekam Jejak Digital
DOI:
https://doi.org/10.22303/infosys.10.2.2026.145-161Keywords:
Kinerja Karyawan, Random Forest, Gradient Boosting, Hyperparameter Tuning, Rekam Jejak DigitalAbstract
Evaluasi kinerja karyawan berbasis rekam jejak digital telah banyak dikembangkan menggunakan algoritma machine learning, namun optimalisasi melalui hyperparameter tuning pada algoritma ensemble seperti Random Forest dan Gradient Boosting masih jarang dieksplorasi secara khusus pada domain ini. Penelitian ini bertujuan untuk menganalisis pengaruh hyperparameter tuning menggunakan RandomizedSearchCV terhadap performa algoritma Random Forest dan Gradient Boosting dalam mengklasifikasikan tingkat kinerja karyawan (Low, Average, Good, Excellent) berdasarkan 2.435 data rekam jejak digital yang meliputi aktivitas kerja, data wearable, kepuasan kerja, kompetensi, dan data organisasi. Metodologi penelitian meliputi praproses data dengan RobustScaler, penanganan ketidakseimbangan kelas menggunakan SMOTE, pembagian data latih-uji 80:20, serta optimasi hyperparameter dengan RandomizedSearchCV dan validasi silang stratified k-fold. Hasil penelitian menunjukkan bahwa model Random Forest baseline mencapai akurasi tertinggi sebesar 76,58% dengan F1-score 0,7632, sementara hyperparameter tuning terbukti meningkatkan performa Gradient Boosting sebesar 3,51% pada akurasi dan 3,99% pada F1-score, meskipun tidak memberikan peningkatan konsisten pada Random Forest. Penelitian ini juga mengungkap bahwa penerapan SMOTE sebelum pembagian data (data-splitting) menghasilkan estimasi performa yang jauh lebih optimistis (akurasi 76,58%) dibandingkan skema SMOTE yang diterapkan secara ketat hanya pada data latih (akurasi 49,28%), yang mengindikasikan potensi data leakage pada metodologi yang umum digunakan pada penelitian-penelitian sebelumnya. Fitur Overtime_Hours_Monthly, Physical_Activity_Steps, dan Organization_Support_Score teridentifikasi sebagai prediktor kinerja karyawan yang paling berpengaruh. Penelitian ini memberikan kontribusi metodologis berupa evaluasi transparan terhadap risiko data leakage dalam penerapan teknik balancing data pada klasifikasi kinerja karyawan.
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