Perkembangan teknologi informasi berbasis web telah mendorong perubahan
signifikan dalam sistem pengambilan keputusan, termasuk dalam proses seleksi
karyawan. 234 Garage, sebuah perusahaan di bidang otomotif, menghadapi
tantangan dalam menyeleksi kandidat secara objektif dan efisien karena belum
adanya sistem yang terstruktur. Penelitian ini bertujuan untuk mengembangkan
sistem pendukung keputusan (SPK) berbasis web dalam proses seleksi karyawan
dengan menggunakan metode Weighted Aggregated Sum Product Assessment
(WASPAS), yang menggabungkan pendekatan Weighted Sum Model (WSM) dan
Weighted Product Model (WPM) untuk memberikan evaluasi multi-kriteria yang
lebih akurat. Hasil pengujian menunjukkan bahwa sistem mampu mempercepat dan
mempermudah proses seleksi serta menghasilkan pemeringkatan kandidat
berdasarkan kriteria seperti pengalaman bekerja, hasil training, nilai interview,
disiplin dan tanggungjawab. Penerapan metode WASPAS menghasilkan nilai akhir
tertinggi dengan alternatif A2 sebesar 0,88 dan nilai terendah alternatif A8 sebesar
0,50 yang mencerminkan efektivitas sistem dalam memberikan hasil seleksi yang
objektif, terstruktur, dan sesuai kebutuhan perusahaan. Sistem ini secara signifikan
meningkatkan efisiensi, akurasi, dan transparansi dalam proses rekrutmen di 234
Garage.

The development of web-based information technology has driven significant changes in decision-making systems, including in employee selection processes. 234 Garage, an automotive company, faces challenges in selecting candidates objectively and efficiently due to the absence of a structured system. This study aims to develop a web-based Decision Support System (DSS) for employee selection using the Weighted Aggregated Sum Product Assessment (WASPAS) method, which combines the Weighted Sum Model (WSM) and Weighted Product Model (WPM) approaches to provide more accurate multi-criteria evaluations. The testing results show that the system is capable of accelerating and simplifying the selection
process while generating candidate rankings based on criteria such as work experience, training results, interview scores, discipline, and responsibility. The application of the WASPAS method produced the highest final score for alternative A2 at 0.88 and the lowest for alternative A8 at 0.50, demonstrating the system’s effectiveness in delivering objective, structured, and company-aligned selection outcomes. This system significantly improves efficiency, accuracy, and transparency in the recruitment process at 234 Garage.