Penerapan Random Forest untuk Klasifikasi Standar Gaji Industri Berdasarkan Level Pendidikan dan Pengalaman Kerja

Abstract

Determining salary compensation standards in industrial environments is often hindered by the high complexity of mapping multiple worker variables simultaneously, such as education level, work experience, age, and job title. Conventional determination processes that are not based on historical data correlations are prone to producing inconsistent and subjective decisions, triggering internal inequity. This study aims to design an objective, data-driven predictive model to automatically classify workers' salary standards into three target class categories (Low, Mid, High). The proposed computational solution is the application of Machine Learning methods using the Random Forest algorithm. The quantitative analysis phase was executed on 1,000 industrial worker profile records. To prevent data leakage phenomena that trigger pseudo-accuracy, the actual nominal salary attribute was strictly removed during the preprocessing phase. The system's performance evaluation was then validated using a 10-Fold Cross-Validation approach. The research results prove that the algorithm successfully extracted formal education and work experience variables as the most crucial primary splitting parameters. Based on parameter variation experiments, the model successfully reached its optimal learning convergence point at a configuration of 100 decision trees. At this point, the system recorded peak performance with an accuracy rate of 76.80%, a precision value of 0.767, and an F1-Score of 0.767. Modifying the computational structure by increasing it to 150 trees provided no further performance escalation. In conclusion, this analytical architecture is proven to be valid and reliable for implementation as a decision support instrument to establish an objective and transparent payroll system.