YUSTRAINI, YESSY ARYE (2025) Implementasi Machine Learning untuk Prediksi Gaji Menggunakan Algoritma Random Forest pada Website SmartSalary. Project Report (Praktek Kerja Lapang dan Magang). UPN Veteran Jawa Timur.
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Abstract
The implementation of machine learning in human resource management presents new opportunities to enhance efficiency and transparency in strategic decision-making, particularly regarding compensation policies. One resulting innovation is the development of the SmartSalary website, a data-driven employee salary prediction platform that utilizes the Random Forest algorithm to provide accurate estimates. The platform integrates Java Spring Boot technology for robust backend management and Python Flask to support machine learning-based data analysis. SmartSalary generates salary predictions based on various attributes—such as work experience, education level, age, and job position—thereby delivering relevant and context-aware recommendations. The project encompasses not only technological development but also model performance analysis to ensure prediction quality. Evaluation results demonstrate that the Random Forest algorithm achieves a high level of accuracy, with Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared values that are suitable for practical workplace applications. A key advantage of the SmartSalary website is its ability to offer broad benefits to various stakeholders. For companies, the platform facilitates the design of data-driven compensation policies, fostering transparency and fairness within salary structures. Meanwhile, for job seekers, SmartSalary serves as a valuable tool for better career planning based on salary estimates that reflect market conditions. By combining modern technology with a focus on practical solutions, the SmartSalary website demonstrates significant potential to support more effective and future-oriented human resource management. This project serves as a concrete example of how machine learning technology can be implemented to innovatively address challenges in workforce management.
| Item Type: | Monograph (Project Report (Praktek Kerja Lapang dan Magang)) | ||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Computer software | ||||||||
| Divisions: | Faculty of Computer Science > Departemen of Information Systems | ||||||||
| Depositing User: | Yessy Arye Yustraini | ||||||||
| Date Deposited: | 20 Jul 2026 04:21 | ||||||||
| Last Modified: | 20 Jul 2026 06:40 | ||||||||
| URI: | https://repository.upnjatim.ac.id/id/eprint/56131 |
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