Penerapan Metode Fuzzy TOPSIS untuk Perangkingan Aplikasi Streaming Film di Kalangan Mahasiswa Universitas Jember

Abstract

Determining the best alternative among multiple competing criteria is a fundamental challenge in Multi-Criteria Decision Making (MCDM). In the context of selecting movie streaming applications, user assessments are inherently subjective and expressed in linguistic terms, making them difficult to measure numerically using conventional methods. This research aims to rank movie streaming applications based on user preferences using the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) method. Eight streaming applications popular in Indonesia were evaluated as alternatives, namely Netflix, Disney+ Hotstar, Viu, WeTV, Vidio, MAXStream, Prime Video, and iQIYI. Five criteria were employed: price, broadcast quality, content diversity, ease of use, and feature completeness. Price was treated as a cost criterion, while the remaining four were treated as benefit criteria. Linguistic assessments were represented using triangular fuzzy numbers on a scale of low (1,3,5), medium (3,5,7), and high (5,7,9). Data were collected through a questionnaire completed by 102 students at the University of Jember who had used a streaming application within the past 12 months. All calculations were performed using MATLAB software implemented as a Graphical User Interface (GUI). The results indicate that price is the most heavily weighted criterion, followed by broadcast quality, content diversity, ease of use, and feature completeness. The final ranking from highest to lowest is: Netflix (0.8173), Vidio (0.7394), Disney+ Hotstar (0.6707), Viu (0.5372), Prime Video (0.4809), WeTV (0.4370), iQIYI (0.4078), and MAXStream (0.2326). Netflix ranks first due to its superior performance in broadcast quality and content diversity despite its higher price, while MAXStream ranks last as it is less competitive in almost all benefit criteria. This research demonstrates that the Fuzzy TOPSIS method is effective for ranking streaming applications based on users' subjective preferences.