Analisis Perbandingan Metode Edas Dan Aras Dalam Pemilihan Platform Freelance Terbaik Untuk Pekerja Jarak Jauh (Remote Worker)

Rexlicky Verdhika Sagatha; Zaenal Abidin

doi:10.47065/bits.v8i1.9554

Rexlicky Verdhika Sagatha * Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia
Zaenal Abidin Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/bits.v8i1.9554

Keywords: Remote Worker; Decision Support System; EDAS; ARAS; Freelance Platform

Abstract

The trend of remote workers has increased significantly, driving the high adoption of global freelance platforms. However, the diversity of policies regarding service fees, withdrawal limits, and levels of competition across platforms often makes it difficult for beginner remote workers to determine the most optimal choice. This study aims to analyze and compare the recommendation results of a Decision Support System (DSS) using the Evaluation based on Distance from Average Solution (EDAS) method and the Additive Ratio Assessment (ARAS) method in selecting freelance platforms. The study evaluates five platform alternatives (Upwork, Fiverr, Fastwork, Freelancer, and Projects.co.id) using a mixed-methods approach that combines factual platform policy data (Administrative Fee Deduction and Minimum Withdrawal) with user perception data (UI/UX, Security, and Level of Competition). The analysis results show a high level of consistency between the two methods for the best alternative, where Upwork (A1) ranks first with an Appraisal Score (AS) of 0.965 in EDAS and a Utility Degree (Ki) of 0.958 in ARAS. However, the comparative analysis reveals differences in rankings at the 4th and 5th positions, caused by the extreme value (outlier) sensitivity of the EDAS algorithm on cost attributes and the more tolerant stability of the ARAS algorithm in providing proportional value compensation. This study concludes that a comparative method not only provides validated recommendations but also reveals the characteristics of each algorithm in handling anomalies in cost attribute data. The main contribution of this study is to provide a valid comparative decision-making framework for remote workers in optimizing platform selection, while also enriching the academic literature regarding the disclosure of algorithmic sensitivity in the ARAS and EDAS methods when handling cost data anomalies.

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