Application of Data Mining Using the Random Forest Algorithm to Classify High-Performing Employees at Maju Jaya Computer

Abstract

Increasing competition in the business sector requires companies to implement employee performance evaluation systems that are objective, efficient, and accurate. Maju Jaya Komputer, a company engaged in the retail of computer products and related peripherals, requires a performance assessment mechanism capable of processing employee data effectively. The existing conventional evaluation process is time-consuming and prone to subjectivity in identifying outstanding employees. This study aims to implement the Random Forest algorithm to classify outstanding employees based on historical sales records and predefined performance indicators. The research methodology consists of data collection, data preprocessing, Random Forest model development, and performance evaluation using a confusion matrix with accuracy, precision, recall, and F1-score as evaluation metrics. The results demonstrate that the Random Forest model successfully classified employee performance using three key predictor variables, namely the number of laptops sold, attendance rate, and the number of sales prospects. For a new employee record with 26 laptops sold, 94% attendance, and 41 sales prospects, all decision trees consistently predicted the employee as Outstanding, resulting in a majority voting decision of the Random Forest classifier that identified the employee as an outstanding performer. In this prediction example, the model achieved an Accuracy of 100%, Precision of 100%, Recall of 100%, and an F1-score of 100% in classifying outstanding employees. The implementation of this model is expected to enhance the effectiveness of employee performance evaluation while supporting more data-driven human resource management practices.