Comparative Analysis of Social Media Usage Duration Among Vocational High School Students Using A Data Analytics Approach


Wakhid Yunendar(1*); Anas Anas(2); Ryo Hartawan Sasolo(3);

(1) Institut Teknologi Bacharuddin Jusuf Habibie
(2) Institut Teknologi Bacharuddin Jusuf Habibie
(3) Institut Teknologi Bacharuddin Jusuf Habibie
(*) Corresponding Author

  

Abstract


This study aims to analyze and compare the duration of social media usage among Vocational High School (SMK) students in Parepare City, specifically at SMKN 2 and SMKN 3, using a quantitative analytical approach based on descriptive and inferential statistics. The study involved 100 students who were randomly selected from both schools. The primary data in the form of the duration of social media usage were obtained from screen time records on students' smartphones for three consecutive days, while a Likert-scale questionnaire was used as supporting data to describe patterns and tendencies of usage. Data analysis included descriptive statistics, an independent t-test to compare the duration of usage between schools, and a one-way ANOVA test to examine differences in duration among social media platforms. The results of the analysis showed that there was no significant difference in the duration of social media usage between students of SMKN 2 and SMKN 3 (p > 0.05). However, there was a significant difference among platforms (p < 0.001), with TikTok as the dominant platform with an average of 436.4 minutes over three days, followed by WhatsApp (345.4 minutes), Instagram (264.0 minutes), and YouTube (185.0 minutes). The average duration of students' social media usage reached approximately 6–7 hours per day, which is relatively higher compared to national reports. This comparison is indicative considering the differences in platform definitions and measurement methods. The findings of this study indicate the need for digital literacy assistance in the school environment so that social media usage can be managed more wisely and does not negatively affect the students' learning process

Keywords


Social Media; Usage Duration; Vocational High School Students; Data Analytics; ANOVA

  
  

Full Text:

PDF
  

Article Metrics

Abstract view: 88 times
PDF view: 13 times 		     

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v18i2.3122.363-377 		  

Cite

How to cite item

References


M. De Choudhury, S. Counts, E. J. Horvitz, and A. Hoff, “Characterizing and predicting postpartum depression from shared facebook data,” in Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing, New York, NY, USA: ACM, Feb. 2014, pp. 626–638. doi: 10.1145/2531602.2531675.

M. M. Tadesse, H. Lin, B. Xu, and L. Yang, “Detection of Depression-Related Posts in Reddit Social Media Forum,” IEEE Access, vol. 7, pp. 44883–44893, 2019, doi: 10.1109/ACCESS.2019.2909180.

I. Jun, S. Ser, J. Xu, J. Bian, and M. Prosperi, “Quantification of Racial Disparity on Urinary Tract Infection Recurrence and Treatment Resistance in Florida using Algorithmic Fairness Methods,” in 2023 IEEE 11th International Conference on Healthcare Informatics (ICHI), IEEE, Jun. 2023, pp. 261–267. doi: 10.1109/ICHI57859.2023.00043.

M. Boer et al., “Adolescents’ Intense and Problematic Social Media Use and Their Well-Being in 29 Countries,” J. Adolesc. Heal., vol. 66, no. 6, pp. S89–S99, Jun. 2020, doi: 10.1016/j.jadohealth.2020.02.014.

B. Brosnan et al., “From dusk to dawn: examining how adolescents engage with digital media using objective measures of screen time in a repeated measures study,” Int. J. Behav. Nutr. Phys. Act. , vol. 22, no. 1, 2025, doi: 10.1186/s12966-024-01698-0.

D. T. Browne et al., “From screen time to the digital level of analysis: a scoping review of measures for digital media use in children and adolescents,” BMJ Open, vol. 11, no. 5, p. e046367, May 2021, doi: 10.1136/bmjopen-2020-046367.

C. Marker, T. Gnambs, and M. Appel, “Active on Facebook and Failing at School? Meta-Analytic Findings on the Relationship Between Online Social Networking Activities and Academic Achievement,” Educ. Psychol. Rev., vol. 30, no. 3, pp. 651–677, Sep. 2018, doi: 10.1007/s10648-017-9430-6.

W. W. F. Lau, “Effects of social media usage and social media multitasking on the academic performance of university students,” Comput. Human Behav., vol. 68, pp. 286–291, Mar. 2017, doi: 10.1016/j.chb.2016.11.043.

D. J. Lemay, P. Bazelais, and T. Doleck, “Patterns of social networking use and academic performance: Examining the link between quality and frequency of social networking use and academic performance among college-level students,” Educ. Inf. Technol., vol. 25, no. 3, pp. 2261–2273, May 2020, doi: 10.1007/s10639-019-10065-7.

A. Orben, “Teenagers, screens and social media: a narrative review of reviews and key studies,” Soc. Psychiatry Psychiatr. Epidemiol., vol. 55, no. 4, pp. 407–414, Apr. 2020, doi: 10.1007/s00127-019-01825-4.

A. Li, D. Jiao, and T. Zhu, “Detecting depression stigma on social media: A linguistic analysis,” J. Affect. Disord., vol. 232, pp. 358–362, May 2018, doi: 10.1016/j.jad.2018.02.087.

V. A. Goodyear et al., “School phone policies and their association with mental wellbeing, phone use, and social media use (SMART Schools): a cross-sectional observational study,” Lancet Reg. Heal. - Eur., vol. 51, p. 101211, 2025, doi: 10.1016/j.lanepe.2025.101211.

E. J. Choi, E. Christiaans, and E. G. Duerden, “Screen time woes: Social media posting, scrolling, externalizing behaviors, and anxiety in adolescents,” Comput. Human Behav., vol. 170, no. April, p. 108688, 2025, doi: 10.1016/j.chb.2025.108688.

W. Tafesse, “Social networking sites use and college students’ academic performance: testing for an inverted U-shaped relationship using automated mobile app usage data,” Int. J. Educ. Technol. High. Educ., vol. 19, no. 1, 2022, doi: 10.1186/s41239-022-00322-0.

S. Samad, M. Nilashi, and O. Ibrahim, “The impact of social networking sites on students’ social wellbeing and academic performance,” Educ. Inf. Technol., vol. 24, no. 3, pp. 2081–2094, May 2019, doi: 10.1007/s10639-019-09867-6.

I. A. Ibrahim, M. H. M. Mohamed, and A. Alenezi, “Exploring the linkages between social media use, self-esteem, and academic performance among nursing students in Saudi Arabia: A descriptive correlational study,” Belitung Nurs. J., vol. 10, no. 2, pp. 152–159, 2024, doi: 10.33546/BNJ.3188.

M. Romero Jeldres, E. Díaz Costa, and T. Faouzi Nadim, “A review of Lawshe’s method for calculating content validity in the social sciences,” Front. Educ., vol. 8, Nov. 2023, doi: 10.3389/feduc.2023.1271335.

K. Mikkonen, M. Tomietto, and R. Watson, “Instrument development and psychometric testing in nursing education research,” Nurse Educ. Today, vol. 119, p. 105603, Dec. 2022, doi: 10.1016/j.nedt.2022.105603.

P. Mishra, C. Pandey, U. Singh, A. Gupta, C. Sahu, and A. Keshri, “Descriptive statistics and normality tests for statistical data,” Ann. Card. Anaesth., vol. 22, no. 1, p. 67, 2019, doi: 10.4103/aca.ACA_157_18.

A. Ghasemi and S. Zahediasl, “Normality Tests for Statistical Analysis: A Guide for Non-Statisticians,” Int. J. Endocrinol. Metab., vol. 10, no. 2, pp. 486–489, Dec. 2012, doi: 10.5812/ijem.3505.

J. I. Vélez, J. C. Correa, and F. Marmolejo-Ramos, “A new approach to the Box–Cox transformation,” Front. Appl. Math. Stat., vol. 1, Oct. 2015, doi: 10.3389/fams.2015.00012.

H. Yu, P. Sang, and T. Huan, “Adaptive Box–Cox Transformation: A Highly Flexible Feature-Specific Data Transformation to Improve Metabolomic Data Normality for Better Statistical Analysis,” Anal. Chem., vol. 94, no. 23, pp. 8267–8276, Jun. 2022, doi: 10.1021/acs.analchem.2c00503.

E. E. Gongnet, R. Vihotogbé, C. E. Agbangba, T. Affossogbé, K. Djondang, and R. G. Kakaï, “Impact Of Box-Cox Transformation Technique On The Bayesian Maximum Entropy (BME) Prediction Accuracy,” JP J. Biostat., pp. 127–144, Dec. 2024, doi: 10.17654/0973514325006.

D. Soave and L. Sun, “A Generalized Levene’s Scale Test for Variance Heterogeneity in the Presence of Sample Correlation and Group Uncertainty,” Biometrics, vol. 73, no. 3, pp. 960–971, Sep. 2017, doi: 10.1111/biom.12651.

Y. Wang, M. Tang, P. Wang, B. Liu, and R. Tian, “The Levene test based-leakage assessment,” Integration, vol. 87, pp. 182–193, Nov. 2022, doi: 10.1016/j.vlsi.2022.06.013.

Y. Skaik, “The bread and butter of statistical analysis ‘t-test’: Uses and misuses,” Pakistan J. Med. Sci., vol. 31, no. 6, pp. 1558–1559, 2015, doi: 10.12669/pjms.316.8984.

K. DeJarnette and M. Mamidala, “Analysis of variance,” in Translational Surgery, Elsevier, 2023, pp. 177–181. doi: 10.1016/B978-0-323-90300-4.00101-4.

P. T. Munroe, “ANOVA (Analysis of Variance),” in Reference Module in Social Sciences, Elsevier, 2026. doi: 10.1016/B978-0-443-26629-4.00198-2.

J. Arredondo Montero, “A Structured Guide to Univariate Test Selection Based on Normality, Variance Homogeneity, and Graphical Data Exploration,” J. Surg. Res., vol. 318, pp. 230–240, Feb. 2026, doi: 10.1016/j.jss.2025.10.053.

A. M. M. Ahmed and F. M. A. Hamarai, “Using Analysis of Variance in the Academic Achievement to Compare Three Learning Patterns for University Students,” Creat. Educ., vol. 13, no. 06, pp. 2104–2118, 2022, doi: 10.4236/ce.2022.136131.

C. E. Agbangba, E. Sacla Aide, H. Honfo, and R. Glèlè Kakai, “On the use of post-hoc tests in environmental and biological sciences: A critical review,” Heliyon, vol. 10, no. 3, p. e25131, Feb. 2024, doi: 10.1016/j.heliyon.2024.e25131.

D. Chmiel, S. Wallan, and M. Haberland, “tukey_hsd: An Accurate Implementation of the Tukey Honestly Significant Difference Test in Python,” J. Open Source Softw., vol. 7, no. 75, p. 4383, Jul. 2022, doi: 10.21105/joss.04383.

A. Nanda, D. B. B. Mohapatra, A. P. K. Mahapatra, A. P. K. Mahapatra, and A. P. K. Mahapatra, “Multiple comparison test by Tukey’s honestly significant difference (HSD): Do the confident level control type I error,” Int. J. Stat. Appl. Math., vol. 6, no. 1, pp. 59–65, Jan. 2021, doi: 10.22271/maths.2021.v6.i1a.636.

W. A. Social and Hootsuite, “Digital 2024: Indonesia – Local country headlines report,” 2024.

S. P. Becker et al., “Remote Learning During COVID-19: Examining School Practices, Service Continuation, and Difficulties for Adolescents With and Without Attention-Deficit/Hyperactivity Disorder,” J. Adolesc. Heal., vol. 67, no. 6, pp. 769–777, Dec. 2020, doi: 10.1016/j.jadohealth.2020.09.002.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2026 Wakhid Yunendar, Anas Anas, Ryo Hartawan Sasolo

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.