<![CDATA[Abstrak – Dalam lingkungan bisnis yang kompetitif, pemilihan karyawan terbaik merupakan proses penting untuk mendukung pengembangan sumber daya manusia yang optimal. Namun, proses seleksi sering kali menghadapi kendala seperti subjektivitas, ketidakteraturan data, dan kurangnya transparansi. Penelitian ini bertujuan untuk membangun Sistem Pendukung Keputusan (SPK) menggunakan metode Simple Additive Weighting (SAW) guna membantu proses seleksi karyawan terbaik secara objektif dan terukur. Metode SAW dipilih karena kemampuannya dalam menangani pengambilan keputusan multikriteria melalui penjumlahan nilai preferensi hasil dari proses normalisasi dan pembobotan. Kriteria penilaian yang digunakan meliputi kedisiplinan, kualitas kerja, tanggung jawab, kerja sama tim, serta loyalitas dan integritas. Data dikumpulkan melalui observasi, wawancara, dan studi pustaka terhadap 23 karyawan Divisi Teknik PT Lekom Maras. Hasil implementasi menunjukkan bahwa sistem berhasil mengidentifikasi karyawan terbaik (A6) dengan nilai preferensi tertinggi sebesar 18. Selain itu, sistem juga mengelompokkan performa karyawan ke dalam lima kategori, di antaranya terdapat 4 karyawan dengan nilai “Cukup” dan 9 karyawan dengan nilai “Sangat Kurang”. Dengan demikian, penerapan Sistem Pendukung Keputusan (SPK) berbasis Simple Additive Weighting (SAW) memberikan solusi yang efektif dalam proses seleksi yang lebih transparan, efisien, dan akuntabel. Kata kunci : Sistem Pendukung Keputusan; Simple Additive Weighting; Seleksi Karyawan; Evaluasi Kinerja; Multikriteria; Abstract – In a competitive business environment, selecting the best employees is a crucial process to support optimal human resource development. However, the selection process often faces challenges such as subjectivity, data inconsistency, and lack of transparency. This study aims to develop a Decision Support System (DSS) using the Simple Additive Weighting (SAW) method to assist in objectively and measurably selecting the best employees. SAW is chosen for its ability to handle multi-criteria decision-making by summing preference values derived from normalization and weighting processes. The evaluation criteria include discipline, work quality, responsibility, teamwork, loyalty, and integrity. Data were collected through observation, interviews, and a literature study involving 23 employees from the Technical Division of PT Lekom Maras. The implementation results showed that the system successfully identified the best employee (A6) with the highest preference score of 18. Additionally, the system categorized employee performance into five groups, including 4 employees rated as “Fair” and 9 employees as “Very Poor”. Therefore, the application of the SAW-based DSS provides an effective solution for a more transparent, efficient, and accountable selection process. Keywords: Decision Support System; Simple Additive Weighting; Employee Selection; Performance Evaluation; Multi-Criteria;]]>
