IDENTIFIKASI POLA TIDUR GENERASI Z (GEN-Z) MENGGUNAKAN ALGORITMA KLUSTERISASI K-MEANS
Abstract
This study uses the K-Means clustering algorithm to identify the sleep patterns of generation Z (Gen-Z) and the factors that affect their sleep quality. Data were collected from a number of Gen-Z participants, including sleep duration, frequency of difficulty sleeping, feeling refreshed after waking up, caffeine consumption, use of electronic devices, frequency of exercise, as well as symptoms such as restlessness, daytime sleepiness, and difficulty concentrating. The clustering results showed that there were several different sleep patterns among Gen-Z, which were influenced by the use of electronic devices before bedtime, caffeine consumption, and exercise habits. It was found that excessive use of technology, caffeine consumption, and lack of physical activity were associated with poorer sleep quality among Gen-Z. These results emphasize the importance of lifestyle factors in influencing sleep quality and provide a basis for more effective interventions. This research contributes to the development of more relevant health policies to improve the sleep quality and well-being of Gen-Z.
References
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[3] A. J. Krause et al., “The sleep-deprived human brain,” Jul. 01, 2017, Nature Publishing Group. doi: 10.1038/nrn.2017.55.
[4] M. Hysing, S. Haugland, T. Bøe, K. M. Stormark, and B. Sivertsen, “Sleep and school attendance in adolescence: Results from a large population-based study,” Scand J Public Health, vol. 43, no. 1, pp. 2–9, 2015, doi: 10.1177/1403494814556647.
[5] E. Harbard, N. B. Allen, J. Trinder, and B. Bei, “What’s Keeping Teenagers Up? Prebedtime Behaviors and Actigraphy-Assessed Sleep over School and Vacation,” Journal of Adolescent Health, vol. 58, no. 4, pp. 426–432, Apr. 2016, doi: 10.1016/j.jadohealth.2015.12.011.
[6] A. A. Borbély, S. Daan, A. Wirz-Justice, and T. Deboer, “The two-process model of sleep regulation: A reappraisal,” J Sleep Res, vol. 25, no. 2, pp. 131–143, Apr. 2016, doi: 10.1111/jsr.12371.
[7] A. A. Schlarb, D. Kulessa, and M. D. Gulewitsch, “Sleep characteristics, sleep problems, and associations of self-efficacy among German university students,” Nat Sci Sleep, vol. 4, pp. 1–7, 2012, doi: 10.2147/NSS.S27971.
[8] D. J. Maume, “Social Ties and Adolescent Sleep Disruption,” J Health Soc Behav, vol. 54, no. 4, pp. 498–515, Dec. 2013, doi: 10.1177/0022146513498512.
[9] J. P. Chaput, C. Dutil, and H. Sampasa-Kanyinga, “Sleeping hours: What is the ideal number and how does age impact this?,” 2018, Dove Medical Press Ltd. doi: 10.2147/NSS.S163071.
[10] B. Bei, J. F. Wiley, J. Trinder, and R. Manber, “Beyond the mean: A systematic review on the correlates of daily intraindividual variability of sleep/wake patterns,” Aug. 01, 2016, W.B. Saunders Ltd. doi: 10.1016/j.smrv.2015.06.003.
[11] W. Chang et al., “Analysis of university students’ behavior based on a fusion K-means clustering algorithm,” Applied Sciences (Switzerland), vol. 10, no. 18, Sep. 2020, doi: 10.3390/APP10186566.
[12] J. A. Hartigan and M. A. Wong, “Algorithm AS 136: A K-Means Clustering Algorithm,” 1979.
[13] W. W. Kristianto and C. Rudianto, “Penerapan Data Mining Pada Penjualan Produk Menggunakan Metode K-Means Clustering (Studi Kasus Toko Sepatu Kakikaki),” Nov. 2022.
[14] T. Amalina, D. Bima, A. Pramana, and B. N. Sari, “Metode K-Means Clustering Dalam Pengelompokan Penjualan Produk Frozen Food,” Jurnal Ilmiah Wahana Pendidikan, vol. 8, no. 15, pp. 574–583, 2022, doi: 10.5281/zenodo.7052276.
[15] E. A. Saputra and Y. Nataliani, “Analisis Pengelompokan Data Nilai Siswa untuk Menentukan Siswa Berprestasi Menggunakan Metode Clustering K-Means,” Journal of Information Systems and Informatics, vol. 3, no. 3, 2021, [Online]. Available: http://journal-isi.org/index.php/isi
[16] S. Alelyani, J. Tang, and H. Liu, “Feature Selection for Clustering: A Review.”
[17] A. Saxena et al., “A review of clustering techniques and developments,” Neurocomputing, vol. 267, pp. 664–681, Dec. 2017, doi: 10.1016/j.neucom.2017.06.053.
[18] J. Hämäläinen, S. Jauhiainen, and T. Kärkkäinen, “Comparison of internal clustering validation indices for prototype-based clustering,” Algorithms, vol. 10, no. 3, 2017, doi: 10.3390/a10030105.
[19] A. Kassambara, “Multivariate Analysis I Practical Guide To Cluster Analysis in R Unsupervised Machine Learning.” [Online]. Available: http://www.sthda.com
[20] D. Xu and Y. Tian, “A Comprehensive Survey of Clustering Algorithms,” Annals of Data Science, vol. 2, no. 2, pp. 165–193, Jun. 2015, doi: 10.1007/s40745-015-0040-1.
[21] I. A. S. Silvanasari and W. Rosalini, “Perilaku Penggunaan Smartphone, Gaya Hidup, Dan Lingkungan Fisik Berhubungan Dengan Kualitas Tidur Yang Buruk Pada Remaja”.
[22] W. Angkatavanich, N. Chaiburanont, and S. Barrameesangpet, “International Journal of Current Science Research and Review Associations between Duration and Type of Electronic Device Use and Sleep Quality among Bangkok’s High School Students”, doi: 10.47191/ijcsrr/V6-i9-34.
[23] S. Martini, S. Roshifanni, and F. Marzela, “Pola Tidur yang Buruk Meningkatkan Risiko Hipertensi,” Media Kesehatan Masyarakat Indonesia, vol. 14, no. 3, p. 297, Sep. 2018, doi: 10.30597/mkmi.v14i3.4181.
[24] D. Fahturosi, “Dampak Kebiasaan Begadang Terhadap Pola Tidur Dan Kesehatan.”
Published
2024-12-31
How to Cite
ANNISA, Riski; RAHAYUNINGSIH, Panny Agustia; ANNA, Anna.
IDENTIFIKASI POLA TIDUR GENERASI Z (GEN-Z) MENGGUNAKAN ALGORITMA KLUSTERISASI K-MEANS.
Jurnal Tekinkom (Teknik Informasi dan Komputer), [S.l.], v. 7, n. 2, dec. 2024.
ISSN 2621-3079.
Available at: <https://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/1740>. Date accessed: 17 jan. 2025.
doi: https://doi.org/10.37600/tekinkom.v7i2.1740.
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