Penerapan Sistem HRMS Berbasis Web untuk Seleksi dan Rekomendasi Karyawan dengan Metode K – Nearest Neighbor (KNN)
DOI:
https://doi.org/10.55606/juisik.v5i1.1521Keywords:
Employee Recommendation, Employee Selection, HRMS System, K-Nearestn Neighbor (KNN), WebAbstract
This research develops a website-based Human Resource Management System (HRMS) that applies the K-Nearest Neighbor (KNN) method for the selection and recommendation of permanent employees. The background to the development of this system is the challenges in the manual employee selection process, such as the large number of applicants, the difficulty of objective assessment, and the time required, which affects company productivity. The main objective of this research is to accelerate and simplify the selection process, reduce unfair assessments, and improve the accuracy of recruitment decisions. This system is designed to automatically analyze applicant data (including education level, work experience, and psychological test results) based on patterns from previous employee data. The results of this study indicate that the developed system is able to analyze applicant data and provide more accurate recommendations, significantly saving time and effort for the HR department, and producing more objective selection decisions that are in line with company needs. Thus, this system contributes to increasing efficiency, fairness, and quality in the recruitment process. In addition, the use of the K-Nearest Neighbor (KNN) method in this system provides advantages because this algorithm is able to classify data based on the level of similarity with previous data. This ensures more accurate and consistent recommendations, as they are based on historical employee patterns that have proven successful within the company. This helps HR reduce subjectivity in assessments, as selection decisions are not based solely on intuition or individual preferences, but rather on measurable, data-driven analysis. This HRMS system also has the potential for broader development, such as integration with other machine learning technologies capable of predicting long-term employee performance, or linking it to the company's performance management system.
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