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Analisis Efektivitas Edukasi Gizi Dengan Video Tiktok Terhadap Pengetahuan Dan Sikap Konsumsi Gula Pada Siswa Man 1 Kota Palu Nur Fadillah; Nikmah Utami Dewi
Jurnal Bina Ilmu Cendekia Vol. 6 No. 2 (2025): Jurnal Bina Ilmu Cendekia
Publisher : MAN Insan Cendekia Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46838/jbic.v6i2.951

Abstract

Diabetes mellitus (DM) is a non-communicable disease characterized by chronic increase in blood sugar levels due to impaired insulin secretion or function. One of the main risk factors for type 2 DM is excessive consumption of foods and drinks high in added sugar. High sugar consumption patterns among adolescents are of concern due to increasing diabetes prevalence at young age. This study aimed to analyze the effectiveness of nutrition education using TikTok video media on knowledge and attitudes based on Transtheoretical Model theory regarding consumption of sweet foods and drinks among students at MAN 1 Palu City. The research design was quasi-experimental with pre-test post-test control group. A sample of 40 students was selected through purposive sampling, consisting of 20 students in experimental group receiving education through TikTok and 20 students in control group with instructional videos. Data were analyzed using paired sample t-test for within-group differences and independent sample t-test for between-group differences. Results showed significant differences in students' knowledge scores in both groups (p=0.000), but attitude scores did not show significant differences (p>0.05). Based on Transtheoretical Model theory, students' attitudes changed from contemplation stage to preparation stage. Gain Score indicated instructional videos were slightly superior to TikTok. TikTok-based nutrition education and instructional videos were effective in improving adolescents' knowledge towards sugar consumption.
K-Means-Based Behavioral Segmentation of Social Media Users for Digital Communication Analysis in Indonesia Nurhidayat, Nurhidayat; Nur Fadillah; Dilla, Salsa; Syahrul, Syahrul; Surianto, Dewi Fatmarani
International Journal of Electronics and Communications Systems Vol. 5 No. 2 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v5i2.28749

Abstract

Social media has become a central part of daily life in Indonesia, yet the rapid growth of digital platforms presents challenges in analyzing large, unstructured user data. Responding to this issue, this study aims to cluster the behavioral patterns of Indonesian social media users using the K-Means Clustering algorithm, a data mining technique for unsupervised segmentation. Employing a quantitative approach, data were collected through an online questionnaire from 553 respondents aged 15–80 years. After data cleaning, normalization, and feature encoding, the optimal number of clusters was determined using the Elbow method and Silhouette Coefficient, resulting in two clusters with a Silhouette score of 0.177. Cluster 0 (303 respondents) represents highly interactive multi-platform users active on Instagram, TikTok, and YouTube for 3–6 hours daily, showing strong interest in entertainment and motivational content. Cluster 1 (250 respondents) includes more passive users, mainly on Instagram and TikTok, spending 3–4 hours per day with moderate engagement and a preference for motivational and self-development content. The findings demonstrate that K-Means Clustering effectively maps user behavior based on platform use, content preferences, motivations, and interaction patterns. The implications of these findings suggest that digital communication strategies need to be tailored to the characteristics of each user cluster so that the messages and content conveyed are more effective.