Development of a Decision Support System for Movie Recommendations Using the Evaluation Based on Distance from Average Solution

M. Raiyan Firdaus (Universitas Malikussaleh)
Mukhlis Abdul Muthalib (Universitas Malikussaleh)
Rini Meiyanti (Universitas Malikussaleh)

Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)

Digital transformation has changed how people enjoy media content, including films, through digital platforms like YouTube. Recommendation systems play a vital role in helping viewers find movies that match their preferences, utilizing methods such as Simple Additive Weighting and Collaborative Filtering to enhance recommendation accuracy and relevance. In this study, the Evaluation Based on Distance From Average Solution (EDAS) method is applied to provide more independent and user-focused movie recommendations. EDAS works by analyzing user profiles, which contain keywords or features related to films of interest. Based on an analysis of 300 film alternatives, the results show that Dune: Part Two (A199) ranks highest with a qualitative utility score of 1, followed by Spider-Man: Across the Spider-Verse (A182) with a score of 0.932194, and Furiosa: A Mad Max Saga (A201) with a score of 0.853523. The lowest-ranked alternative is Cobweb (A158) with a qualitative utility score of 0. Through the EDAS approach, this movie recommendation system offers a more relevant and satisfying viewing experience for users.