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All Journal Jurnal Masyarakat Informatika Jurnal Simetris JAIS (Journal of Applied Intelligent System) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Journal of Soft Computing Exploration Jurnal Informatika Polinema (JIP) Jurnal Tata Rias
Agustina, Feri
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Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Journal : JAIS (Journal of Applied Intelligent System)

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Prediction of Sleep Disorders Based on Occupation and Lifestyle: Performance Comparison of Decision Tree, Random Forest, and Naïve Bayes Classifier Lestiawan, Heru; Jatmoko, Cahaya; Agustina, Feri; Sinaga, Daurat; Erawan, Lalang
(JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i3.8987

Abstract

Health is a very important thing in life. Therefore, to maintain health, we need adequate rest. Without adequate rest, the body will not be healthy and fit. In this study, a person's sleep disorder prediction will be made based on their lifestyle and work. The predictions made will classify sleep disorders that are absent, sleep apnea and insomnia from certain lifestyles and work. The methods used to make predictions are decision tree classifier, random forest classifier and naïve Bayes classifier. The test was carried out using a total of 375 data which was broken down into 70% training data and 30% testing data. The results obtained after testing with test data are by using the decision tree classifier algorithm to get an accuracy of 89.431%, using the random forest classifier algorithm to get an accuracy of 90.244% and by using the naïve Bayes classifier algorithm to get an accuracy of 86.992%.
Co-Authors ALI MUQODDAS Award Widya Laksana, Deddy Cahaya Jatmoko Candra Irawan Christy Atika Sari De Rosal Ignatius Moses Setiadi Doheir, Mohamed Dwi Puji Prabowo, Dwi Puji Eko Hari Rachmawanto Erlin Dolphina Erna Zuni Astuti Henry Bastian, Henry Heru Lestiawan Lalang Erawan Mulyono, Ibnu Utomo Wahyu Ningrum, Novita Kurnia Putri, Ni Kadek Devi Adnyaswari Ramadhan, Daniswara Tegar Ricardus Anggi Pramunendar Saputro, Fakhri Rasyid Sinaga, Daurat Solichul Huda, Solichul suprayogi Suprayogi Suprayogi Syafira, Zahra Ghina Syahputra, Zulfikar Adi Utomo, Danang Wahyu