Klasterisasi Tingkat Kecanduan Penggunaan Tiktok Terhadap Minat Belajar Menggunakan Algoritma K-Means Clustering - Ciptandini, Madyantari Ipmas; Prathivi, Rastri (Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen), 2025)
The decline in interest in learning among students is one of the significant challenges in the digital era, especially due to the excessive use of social media such as TikTok. TikTok, with its engaging and interactive short video content, often distracts learners from studying. These negative impacts include decreased focus, sleep disturbances and less time allocated to study, which ultimately affects academic achievement. Therefore, this study aims to cluster data related to the level of TikTok addiction and decreased interest in learning using the K-Means Clustering algorithm. The K-Means method was used to cluster a dataset of 137 samples into two groups based on the pattern of TikTok usage frequency and study interest level. The model evaluation process shows good performance, with an accuracy value of 96%, recall 98%, precision 91%, and F1 Score 94%. These results support the effectiveness of K-Means in identifying groups at high risk of declining interest in learning. This research proves the potential of clustering techniques in identifying distractions and offering solutions to deal with them