Student Reading Interest Segmentation in the Digital Era Using the K-Means Algorithm

Le résumé fourni par la source

The rapid advancement of information technology in the digital era has triggered transformative changes in students’ reading behavior. Although access to information has become virtually unlimited, students are increasingly susceptible to digital distractions and tend to prefer instant content consumption over in-depth engagement with academic literature. This study aims to segment students’ reading interests at the Institut Sains dan Bisnis (ISB) Atma Luhur in order to obtain a more granular understanding of their literacy profiles through a data mining approach. The unsupervised learning algorithm K-Means Clustering was employed to group reading behavior patterns. Primary data were collected from 130 student respondents using a questionnaire instrument covering reading intensity, genre preferences, and motivation. The data preprocessing stage involved several steps, including data cleaning, data transformation, and feature selection, focusing on two quantitative attributes: the number of books read per month and daily reading duration. The determination of the optimal number of clusters was evaluated using the Elbow Method, which identified three as the optimal number of clusters. The clustering results revealed three dominant persona segments: incidental readers (34.62%), pragmatic academic readers (37.69%), and digital recreational readers (27.69%). These analytical findings provide strategic insights for institutional stakeholders and library managers in designing adaptive, personalized, and targeted literacy intervention programs aligned with the unique characteristics of each student segment within the digital ecosystem.

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