Pengembangan Sistem Pendukung Keputusan Cerdas untuk Seleksi Karyawan Berbasis Multi-Kriteria dan Machine Learning
Keywords:
Sistem Pendukung Keputusan, Seleksi Karyawan, AHP, Machine Learning, Explainable AIAbstract
Seleksi karyawan yang objektif dan akurat merupakan tantangan utama dalam manajemen sumber daya manusia modern. Penelitian ini mengembangkan sistem pendukung keputusan cerdas untuk seleksi karyawan dengan mengintegrasikan metode Analytic Hierarchy Process (AHP) sebagai mekanisme pembobotan kriteria berbasis pakar dengan tiga algoritma machine learning, yaitu Random Forest, XGBoost, dan LightGBM, sebagai mesin prediksi. Novelty penelitian ini terletak pada penggunaan bobot AHP yang divalidasi menggunakan data historis sebagai fitur tambahan dalam pelatihan model, serta penerapan analisis SHAP untuk memberikan interpretasi prediksi yang transparan. Dataset terdiri dari 750 rekaman historis kandidat dengan 13 atribut yang mencakup dimensi kognitif, teknis, dan interpersonal. Hasil eksperimen menunjukkan bahwa XGBoost mencapai akurasi tertinggi sebesar 91,3% dengan nilai F1-score 90,8% dan AUC-ROC 0,954, diikuti LightGBM dengan akurasi 89,7% dan Random Forest 87,4%. Analisis SHAP mengungkapkan bahwa skor kompetensi teknis, hasil wawancara, dan IPK merupakan tiga prediktor paling berpengaruh. Framework yang dikembangkan terbukti mampu meningkatkan objektivitas dan konsistensi proses seleksi, sekaligus menyediakan interpretasi yang dapat dikomunikasikan kepada pemangku kepentingan non-teknis
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