Application of The Naïve Bayes Algorithm for Employee Performance Prediction Based on SIMPEG at TVRI East Kalimantan Station - Ishmah Hanani; Siti Lailiyah; Yulindawati (Forum Kerjasama Pendidikan Tinggi (FKPT), 2025)
Employee performance evaluation is a crucial aspect of public organizational management, including at the public broadcasting institution TVRI East Kalimantan Station. To date, attendance indicators obtained from the Employee Management Information System (SIMPEG) have often been used as the primary benchmark, as the data are objectively and structurally available. However, a single attendance-based approach risks overlooking more substantive aspects of work achievement. Therefore, this study integrates attendance data with the Employee Performance Targets (SKP) to construct a more representative performance label. The method employed is a classification approach using the Naïve Bayes (GaussianNB) algorithm. The research dataset consists of attendance records (normal attendance, leave, official duty, study assignment, early departure, absence, and total working days) and quantized SKP scores. Performance labels were generated using a composite score (0.30 × attendance percentage + 0.70 × normalized SKP), which was then categorized into three classes: Excellent, Good, and Needs Improvement. The model was trained using SIMPEG and SKP data that had undergone preprocessing, data partitioning, and class balancing. Experimental results show that the model achieved an accuracy of 0.83, with a precision of 0.86, recall of 0.84, and F1-score of 0.83 on the test data. These results indicate that the model can consistently recognize employee performance patterns across all categories. Practically, this study offers a simple, efficient, and easily implementable predictive framework to support more objective processes of coaching, monitoring, and reward allocation within TVRI East Kalimantan Station.