Analisis efisiensi tidur berdasarkan faktor demografi dan kebiasaan harian dengan metode random forest regression
DOI:
https://doi.org/10.31571/saintek.v14i1.9291Keywords:
Efisiensi Tidur, Random Forest Regression, Machine Learning, Kebiasaan Harian, Faktor DemografiAbstract
Efisiensi tidur berperan penting bagi kesehatan fisik dan mental, namun dapat menurun karena berbagai faktor demografi dan kebiasaan harian. Penelitian ini bertujuan menganalisis efisiensi tidur berdasarkan faktor-faktor tersebut menggunakan metode Random Forest Regression. Pendekatan kuantitatif diterapkan dengan dataset dari Kaggle melalui tahapan data, feature selection, dan pemodelan dengan 100 pohon keputusan. Untuk menilai performa secara menyeluruh, model dibandingkan dengan Linear Regression dan Support Vector Regression (SVR). Hasil penelitian menunjukkan bahwa Random Forest Regression memberikan performa yang sangat baik dengan nilai MAE sebesar 0,0327, MSE sebesar 0,0015, RMSE sebesar 0,0390, dan koefisien determinasi (R²) sebesar 0,91 yang lebih unggul dibanding dua model lainnya pada data pengujian. Model mampu mengikuti pola fluktuasi efisiensi tidur dan diterapkan dalam sistem prediksi interaktif yang menerima input seperti usia, waktu tidur, durasi tidur, jumlah terbangun, dan kebiasaan harian lainnya. Salah satu hasil prediksi menunjukkan nilai efisiensi tidur sebesar 0,528. Nilai ini merupakan output regresi dalam skala kontinu (0–1) dan tidak menghasilkan klasifikasi secara langsung. Kategorisasi kualitas tidur seperti “Sangat Baik”, “Baik”, dan “Kurang” dilakukan secara post-hoc (setelah hasil prediksi diperoleh) berdasarkan ambang batas tertentu. Kesimpulannya, faktor demografi dan kebiasaan harian memiliki pengaruh yang dapat diprediksi terhadap efisiensi tidur, dengan Random Forest Regression terbukti efektif untuk menganalisis hubungan kompleks ini. Penelitian selanjutnya disarankan untuk mengeksplorasi algoritma lain dan menambahkan variabel seperti tingkat stres dan faktor lingkungan untuk analisis yang lebih komprehensif.
Downloads
References
Altini, M., & Kinnunen, H. (2021). The promise of sleep: A multi-sensor approach for accurate sleep stage detection using the oura ring. Sensors, 21(13), 1–21. https://doi.org/10.3390/s21134302
Ardesfira, G., Zedha, H. F., Fazana, I., Rahmadhiyanti, J., Rahima, S., & Anwar, S. (2022). Peramalan nilai tukar Rupiah terhadap Dollar Amerika dengan menggunakan metode Autoregressive Integrated Moving Average (ARIMA). Jambura Journal of Probability and Statistics, 3(2), 71–84. https://doi.org/10.34312/jjps.v3i2.15469
Ardiansyah, D. (2023). Perbandingan model prediksi radiasi matahari berbasis mesin pembelajaran pada Stasiun Meteorologi Fatmawati Soekarno Bengkulu. Megasains, 14(1), 26–32. https://doi.org/10.46824/megasains.v14i1.129
Cici Haryati, M., Kusumaningsih, I., & Supardi, S. (2022). Faktor-faktor yang berhubungan dengan kualitas tidur lansia di RW 03 Kelurahan Kebon Manggis Jakarta. Jurnal Keperawatan Malang, 7(2), 76–93. https://jurnal.stikespantiwaluya.ac.id/index.php/JPW
Desjardins, S., Lapierre, S., Hudon, C., & Desgagné, A. (2019). Factors involved in sleep efficiency: A population-based study of community-dwelling elderly persons. Sleep, 42(5), 1–10. https://doi.org/10.1093/sleep/zsz038
Driller, M. W., Dunican, I. C., Omond, S. E. T., Boukhris, O., Stevenson, S., Lambing, K., & Bender, A. M. (2023). Pyjamas, polysomnography and professional athletes: The role of sleep tracking technology in sport. Sports, 11(1), 1–17. https://doi.org/10.3390/sports11010014
Fadillah, A. R., & Fauzan, M. N. (2024). Analisis perbandingan Linear Regression dan Random Forest Regression untuk prediksi batas kredit: Pendekatan optimasi hyperparameter. Jurnal Informatika Polinema, 10(4), 543–550. https://doi.org/10.33795/jip.v10i4.5700
Faoziyah, N., & Suharjana, S. (2020). Kualitas dan efisiensi tidur yang buruk pada mahasiswa olahraga tahun pertama srata-1 terhadap pembelajaran gerak. Jurnal SPORTIF : Jurnal Penelitian Pembelajaran, 6(2), 514–525. https://doi.org/10.29407/js_unpgri.v6i2.14545
Farhanuddin, Sihombing, S. E. K., & Yahfizham. (2024). Komparasi multiple Linear Regression dan Random Forest Regression dalam memprediksi anggaran biaya manajemen proyek sistem informasi. Journal of Computers and Digital Business, 3(2), 86–97. https://doi.org/10.56427/jcbd.v3i2.408
Hafizh, V., Putra, C., Al-husaini, M., Wahyu, A. P., & Raharja, A. R. (2025). Design of an intelligent monitoring system based on Internet of Things (IoT) with Random Forest Regression algorithm for height detection in cherry tomato plants. Indonesian Journal of Machine Learning and Computer Science, 5(January), 10–25. https://doi.org/10.57152/malcom.v5i1.1612
Halson, S. L., Johnston, R. D., Piromalli, L., Lalor, B. J., Cormack, S., Roach, G. D., & Sargent, C. (2022). Sleep regularity and predictors of sleep efficiency and sleep duration in elite team sport athletes. Sports Medicine - Open, 8(1). https://doi.org/10.1186/s40798-022-00470-7
He, Y. (2023). Effect of indicators of exercise on sleep efficiency. Highlights in Science, Engineering and Technology, 54(2), 342–350. https://doi.org/10.1016/j.csm.2004.12.003
Li, L., Yu, Q., Zhao, W., Herold, F., Cheval, B., Kong, Z., Li, J., Mueller, N., Kramer, A. F., Cui, J., Pan, H., Zhan, Z., Hui, M., & Zou, L. (2021). Physical activity and inhibitory control: The mediating role of sleep quality and sleep efficiency. Brain Sciences, 11(5), 1–10. https://doi.org/10.3390/brainsci11050664
Mazzotti, D. R., Guindalini, C., Moraes, W. A. dos S., Andersen, M. L., Cendoroglo, M. S., Ramos, L. R., & Tufik, S. (2014). Human longevity is associated with regular sleep patterns, maintenance of slow wave sleep, and favorable lipid profile. Frontiers in Aging Neuroscience, 6(JUN), 1–9. https://doi.org/10.3389/fnagi.2014.00134
Meiranny, A., & Chabibah, A. M. (2022). Pengaruh konsumsi minuman berkafein terhadap pola dan kualitas tidur mahasiswa: A literatur review. Media Publikasi Promosi Kesehatan Indonesia (MPPKI), 5(2), 117–122. https://doi.org/10.56338/mppki.v5i2.1910
Nugraha, Y. E. N., Ariawan, I., & Arifin, W. A. (2023). Weather forecast from time series data using LSTM algorithm. Jurnal Teknologi Informasi Dan Komunikasi, 14(1), 144–152. https://doi.org/10.51903/jtikp.v14i1.531
Nur, A., Pudjianto, M., & Hidayat, E. Y. (2024). Perbandingan prediksi depresi mahasiswa dengan Linear Regression , Random Forest , dan Gradient Boosting. SINTECH Journal, 7(3), 180–189. https://doi.org/10.31598/sintechjournal.v7i3.1729
Özen, F. (2024). Random forest regression for prediction of Covid-19 daily cases and deaths in Turkey. Heliyon, 10(4), 1–19. https://doi.org/10.1016/j.heliyon.2024.e25746
Patlisan, P. (2023). Optimasi akurasi model Decision Tree menggunakan Random Forest Regression untuk prediksi kuantitas pembelian barang pada perusahaan manufaktur. Simetris: Jurnal Teknik Mesin, Elektro Dan Ilmu Komputer, 14(2), 217–228. https://doi.org/10.24176/simet.v14i2.9494
Penalun, F. E., Hermawan, A., & Avianto, D. (2023). Perbandingan Random Forest Regression dan Support Vector Regression pada prediksi laju penguapan. Jurnal Fasilkom, 13(02), 104–111. https://doi.org/10.37859/jf.v13i02.4976
Prasani, M., Mutmainnah, M., & Mawarti, I. (2023). Gambaran kualitas tidur pada perempuan lansia di Puskesmas Simpang IV Sipin Kota Jambi. Jurnal Ners, 7(2), 1567–1575. https://doi.org/10.31004/jn.v7i2.16687
Prastiyo, I. W., & Febriandirza, A. (2024). Analisis perbandingan prediksi tingkat kemiskinan menggunakan metode XGBoost dan Random Forest Regression. Jurnal Media Informatika Budidarma, 8(3), 1694. https://doi.org/10.30865/mib.v8i3.7892
Reed, D. L., & Sacco, W. P. (2016). Measuring sleep efficiency: what should the denominator be? Journal of Clinical Sleep Medicine, 12(2), 263–266. https://doi.org/10.5664/jcsm.5498
Ricky Verdiyanto, Dwi Hartanti, E. P. (2025). Pengembangan aplikasi point of sales untuk prediksi penjualan harian usaha minuman menggunakan algoritma Random Forest Regression. Infotek : Jurnal Informatika Dan Teknologi, 8(1), 128–139. https://doi.org/10.29408/jit.v8i1.28386
Saragih, V. R., Arnita, A., Indra, Z., Taufik, I., & Sinaga, M. S. (2024). Comparison of supervised machine learning methods in predicting the prevalence of stunting in north sumatra province. Journal of Soft Computing Exploration, 5(4), 370–379. https://doi.org/10.52465/joscex.v5i4.498
Sehangunaung, G. A., Mandey, S. L., & Roring, F. (2023). Analisis pengaruh harga, promosi dan kualitas pelayanan terhadap kepuasan konsumen pengguna aplikasi Lazada di Kota Manado. Jurnal EMBA, 11(3), 1–11. https://iprice.co.id/insights/mapofecommerce/
Tjahjadi, M. E., Agustina, F. D., & Agnesta, R. (2022). Uji akurasi koordinat dari konfigurasi jaringan pemotretan menggunakan kamera non metrik: Studi kasus di Pandansari, Ngantang, Kabupaten Malang. PROSIDING SEMSINA 2022, 13(1), 81–89. https://doi.org/10.36040/semsina.v3i1
Wulandari, S., & Pranata, R. (2024). Deskripsi kualitas tidur dan pengaruhnya terhadap konsentrasi belajar mahasiswa. Jurnal Pendidikan Kesehatan Rekreasi, 10(1), 101–108. https://doi.org/10.59672/jpkr.v10i1.3414
Downloads
View: 130
Download: 123
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Neternomia Maria Ausiladora Da Costa, Anief Fauzan Rozi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal. Please also carefully read Jurnal Pendidikan Informatika dan Sains Posting Your Article Policy at http://journal.ikippgriptk.ac.id/index.php/saintek/about/submissions#onlineSubmissions
- That it is not under consideration for publication elsewhere,
- That its publication has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Copyright
Authors who publish with Jurnal Pendidikan Informatika dan Sains agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
