Prediksi Potensi Kinerja Calon Karyawan Customer Service Call Center Menggunakan Model Machine Learning Berbasis Data Rekrutmen - Pratama, Andriyan Yoga; Ghozi, Wildanil (Forum Kerjasama Pendidikan Tinggi, 2025)
Employee selection process is a critical stage for companies in acquiring high-quality human resources (HR), particularly for customer service call center positions that demand excellent communication skills and strong work endurance. Data-driven recruitment methods have demonstrated improved accuracy compared to traditional, often subjective, approaches. This study aims to develop a predictive model to assess the potential performance of candidates during the HR interview stage, based on educational background, work experience, and other relevant factors, using machine learning algorithms. The dataset utilized includes demographic information, education levels, previous work experience, and other factors that may influence candidate performance in customer service roles. The models tested in this study include Decision Tree, Random Forest, and Artificial Neural Network algorithms. The analysis shows that GPA, prior work experience, and organizational involvement significantly correlate with the potential performance of candidates. The application of machine learning in the recruitment process can enhance selection effectiveness and improve HR efficiency. Through this approach, companies are expected to make more accurate hiring decisions and select the best candidates with greater precision.