Reader Segmentation on Top 5 Online News Media in Indonesia Using K-Means Clustering Algorithm
DOI:
https://doi.org/10.59188/devotion.v7i6.25708Keywords:
Online Media, Reader Segmentation, K-Means Clustering, Online Value Proposition, Consumer Behavior, Digital JournalismAbstract
The rapid growth of digital news consumption in Indonesia has not been matched by accurate audience mapping, leading to suboptimal content personalization and monetization. This research aims to identify reader segmentation across the top five online news media outlets in Indonesia Detik.com, Kompas.com, Tribunnews.com, CNN Indonesia, and TVOneNews.com in order to formulate appropriate Online Value Proposition (OVP) strategies. Using a quantitative approach with K-Means Clustering algorithm on behavioral data from 100 respondents, the optimal number of clusters was determined using the Elbow method. Research variables encompass demographics, content preferences, access frequency, reading duration, and device usage. Three primary clusters were identified: Cluster 1 (Working Women Entertainment Reader) dominated by established professional women favoring entertainment video content; Cluster 2 (Executive Millennial National News Interest) male professional "heavy readers" accessing news over 20 times per day; and Cluster 3 (The Lifestyle Trend-Seeker) Gen Z active TikTok users preferring lifestyle content. Cluster 2 provides the greatest economic potential via traffic volume, while Cluster 1 offers economic stability through household purchasing power. Media outlets are recommended to adopt differentiated OVPs including rapid news summary features (News Bites) for professionals and aesthetic visual content for the younger generation.
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Copyright (c) 2026 Tyas Cahya Larasati, Jerry Heikal
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