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All Journal Jurnal Informatika SISFOTENIKA Indonesian Journal on Software Engineering (IJSE) Jurnal Teknik Komputer AMIK BSI Jurnal Pilar Nusa Mandiri Techno Nusa Mandiri : Journal of Computing and Information Technology INTI Nusa Mandiri JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Infortech Jurnal Sistem Informasi Computer Science (CO-SCIENCE) SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Teknologi Sistem Informasi Journal Medical Informatics Technology Jurnal Pengabdian Kepada Masyarakat International Journal of Computer Technology and Science
Taopik Hidayat
Universitas Nusa Mandiri
Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Public Health Social Sciences Other

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Studi Perbandingan Algoritma Random Forest dan K-Nearest Neighbors (KNN) dalam Klasifikasi Gangguan Tidur Khasanah, Nurul; Eka Saputri , Daniati Uki; Aziz, Faruq; Hidayat, Taopik
Computer Science (CO-SCIENCE) Vol. 5 No. 1 (2025): Januari 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i1.5522

Abstract

Sleep disorders such as insomnia and sleep apnea can significantly affect quality of life and increase the risk of chronic diseases. Early identification and classification of sleep disorders are crucial in preventing further impacts. This study aims to compare the performance of the Random Forest and K-Nearest Neighbors (KNN) algorithms in classifying sleep disorders using the Sleep Health and Lifestyle Dataset from Kaggle, which contains health and lifestyle data relevant to sleep patterns. The Random Forest and KNN algorithms were applied to classify sleep disorders into the categories 'None', 'Sleep Apnea', and 'Insomnia'. Based on the study results, the Random Forest algorithm achieved an accuracy of 89.69%, with the best performance in the 'None' category, reaching a recall of 96.08%. Meanwhile, KNN achieved an accuracy of 87.02% with K=5. Although Random Forest demonstrated superior results, challenges were still found in detecting the 'Sleep Apnea' category, where recall only reached 74.55%, likely due to data imbalance. This study shows that the Random Forest algorithm is more effective in classifying sleep disorders compared to KNN. Future research steps include data balancing and exploring other algorithms such as XGBoost to improve the performance of sleep disorder detection.
Co-Authors Abdul Latif Achmad Baroqah Pohan Achmad Bayhaqy Ade Christian Ade Suryadi Andi Saryoko Aziz, Faruq Budi Santoso, Satrio Daniati Uki Eka Saputri Dipo Era Ginanti Eka Saputra, Bagas Eka Saputri , Daniati Uki Faruq Aziz Faruq Aziz Fitra Septia Nugraha Gata, Windu Hafifah Bella Novitasari Ilmalia Hartina Khultsum, Umi M. Rangga Ramadhan Saelan Mulia Rahmayu Muzakki Ramadhan, Naufal Nurmalasari Nurmalasari Nurmalasari Nurmalasari Nurul Khasanah Nurul Khasanah Nurul Khasanah, Nurul Nurwijayanti Pratiwi, Risca Lusiana Rangga Pebrianto Retno Sari Rina Rina Risca Lusiana Pratiwi Risca Lusiana Pratiwi - Universitas Nusa Mandiri Salsabila, Unik Hanifah Seimahuira, Syarah Septian, Rendi Siti Masturoh Siti Masturoh siti nurlela Tri Rivanie Uki Eka Saputri, Daniati Umi Khultsum Veithzal Rivai Zainal Windu Gata YOANDA, YURIKA PRISILIA