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lubis, ihsan
Universitas Harapan Medan
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Journal : Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer

 1 
Penerapan Sistem HRMS Berbasis Web untuk Seleksi dan Rekomendasi Karyawan dengan Metode K – Nearest Neighbor (KNN) Anisya Avishtya Indra; Ihsan Lubis
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 5 No. 3 (2025): November: Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v5i3.1521

Abstract

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.
Co-Authors Ahmad Daffa Ahmad Zakir Ahmad Zakir Ainil Arbie Akbar Fachrezi Andi Marwan Elhanafi Andi Marwan Elhanafi, Andi Marwan Anisya Avishtya Indra Arie Rafika Dewi Boni Oktaviana Sembiring Daffa , Ahmad Daffa, Mdh. Al Hilmi Dalimunthe, Yulia Agustina Danar A. Rumbiarmytha Dede Abdillah Dedy Irwan Denny Walady Utama Devianita Zulkirahmadhani Edy Rahman Syahputra Edy Rahman Syahputra, Edy Rahman EKA RAHAYU Fachrul Rozi Lubis Fahri Effendy Fatma Wani Silitonga febri Dhea Mita Ferdy Riza Hadinata, Edrian Halimatussa’diah Halimatussa’diah Harahap, Herlina Hasdiana, Hasdiana Hediningtias Wulaningrum Herlina Andriani Simamora Husni Lubis Idzani Muttaqien Nasution Imran Lubis IRWAN, DEDY Jimmi Akbar Khairani, Mufida L Hutahaean, Maxi. lubis, husni Lubis, Husni M Hutahaean, Marcho M. Imam Maulana Marcho M Hutahaean Maulana Maxi L Hutahaean Mhd Alfazri Mhd Fauzul Rizky Muhammad Hafiz Muhammmad Fahri Effendy Mutia Iswandari Putri Mutiah Dwi Amaliah Nazmy, T. Said Hazid An Nst, Risa Rahma Sari Nur Syahfitri Frastika Nasution Nurjamiyah, Nurjamiyah Purwoko, Agus Rafika Dewi, Arie Rahman, Sayuti Rahmi, Fadilla Aulia Rehulina Aslamiyah Reza Agustina Riri Ferial Salsabila Salsabila Sembiring, Boni Oktaviana Senti Harahap, Vira Yuanda Harahap Septiana Dewi Andriana, Septiana Dewi Siagian, Yenny Julanti Sartika Silvani Yolanda Siregar, Fitri Rizky Wahyuni Siregar, Rosyidah Suci Rahmadani Sumi Khairani, Sumi Syahputri, Nenna Irsa Tommy, Tommy Ummul Khair Yermia Ndruru Yuli Artika Yuli Artika, Yuli