Clustering Korean Drama Viewers’ Preferences for Marketing Strategy Optimization Using the K-Means Algorithm on the MyDramaList Platform - Nabilla, Sulistina; Nugroho, Agung; Mahaerni Putri, Isria Miharti (Journal of Applied Informatics and Computing, 2025)
Korean dramas are highly popular in Indonesia, but it is a challenge for marketers to understand the diverse tastes of the audience. This study aims to identify audience preference segments by applying the K-Means algorithm to perform clustering. The analysis was conducted on 500 Korean dramas from the MyDramaList platform released in the period 2020 to 2023. Features used in the clustering process include rating, number of viewers (no_of_viewers), year of release (year), and genre. To overcome the multi-label genre, this research uses one-hot encoding technique to convert categorical data into numerical format. The optimal number of clusters was determined as four (4) based on analysis using the Elbow Method. The analysis successfully identified four distinct audience segments. Cluster 1 is the largest market segment which includes 323 dramas with a dominant genre of “Drama and Romance” and an average rating of 7.64. In contrast, Cluster 2 is a high-quality niche segment consisting of only 16 dramas but has the highest average rating (8.29) as well as the highest average viewership (25,739), with the dominant genre of “Drama and Mystery”. The other two segments are Cluster 0 (58 dramas) which focuses on the “Thriller” and ‘Mystery’ genres, and Cluster 3 (147 dramas) which features the “Comedy, fantasy and romance” genres. These data-driven findings enable the development of more specific and targeted marketing strategies for each audience profile, thereby improving promotional effectiveness as well as the viewing experience.