Open access Jurnal SINTA 2
Penerapan Decision Tree dan Random Forest dalam Deteksi Tingkat Stres Manusia Berdasarkan Kondisi Tidur
Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 11 No. 5 (2024) · pp. 1043–1050
- ISSN
- 2528-6579, 2355-7699
- Published
- 2024-10-31
- Publisher
- Fakultas Ilmu Komputer, Universitas Brawijaya
- Language
- id
Abstract
Bahasa Indonesia
Masalah kesehatan mental menjadi isu global yang sangat umum terjadi, termasuk perubahan suasana hati, perbedaan kepribadian, ketidakmampuan mengatasi masalah, serta mengisolasi diri dari keramaian. Berdasarkan data dari World Health Organization (WHO), gangguan kecemasan dan stres menjadi gangguan mental yang paling sering terjadi dari 970 juta kasus yang dilaporkan sepanjang tahun 2019. Stres telah banyak dikaitkan dengan tidur. Penelitian ini akan mengungkap hubungan kondisi tidur pada manusia dengan tingkat stres yang sedang diderita dengan 5 tingkatan: normal, stres ringan, stres sedang, stres tinggi, stres sangat tinggi. Data yang digunakan merupakan data kontinyu dengan 8 fitur: ‘sr’ (snoring rate), ‘rr’ (respiration rate), ‘t’ (body temperature), ‘lm’ (limb movement), ‘bo’ (blood oxygen), ‘rem’ (rapid eye movement), ‘sh’ (sleeping hours), dan ‘hr’ (heart rate). Setiap fitur memiliki rentang nilai yang tidak sama, sehingga dilakukan normalisasi untuk menyeragamkan rentang tersebut. Hyperparameter tuning dilakukan dengan teknik k-fold cross validation dan model dirancang dengan algoritma klasifikasi Decision Tree serta Random Forest. Hasilnya, 5 fitur: tingkat mendengkur, laju respirasi, pergerakan anggota tubuh termasuk bola mata, serta detak jantung saat tidur berbanding lurus dengan tingkat stres. Semakin tinggi nilai kelima fitur tersebut mengindikasikan tingkat stres yang lebih tinggi. Sedangkan dengan 3 fitur lainnya: suhu tubuh, kadar oksigen, dan waktu tidur memberikan hasil sebaliknya. Dengan kata lain, ketiga nilai tersebut berbanding terbalik dengan tingkat stres yang diderita. Model Decision Tree memiliki akurasi 0,99 dan Random Forest memiliki akurasi 1,0. Hasil penelitian ini diharapkan dapat memberikan insight bagi peneliti lain pada bidang yang sama dan dapat menjadi acuan dalam mendeteksi stres yang sedang diderita. Abstract Stress is often associated with sleep. This research aims to uncover the relationship between human sleep conditions and the level of stress experienced, categorized into five levels: not stressed, very mildly stressed, mildly stressed, highly stressed, and very highly stressed. The data used consists of continuous data with eight features: 'snoring rate' (snoring rate), 'respiration rate' (respiration rate), 'body temperature' (body temperature), 'limb movement' (limb movement), 'blood oxygen' (blood oxygen), 'rapid eye movement' (rapid eye movement), 'sleep hours' (sleep hours), and 'heart rate' (heart rate). Each feature has a different value range, so normalization is performed to standardize these ranges. Hyperparameter tuning is done using k-fold cross-validation, and the model is designed using the Decision Tree and Random Forest classification algorithms. The results show that five features: snoring rate, respiration rate, limb movement including eye movement, and heart rate during sleep are directly proportional to the level of stress. Higher values for these five features indicate higher levels of stress. On the other hand, the other three features: body temperature, blood oxygen level, and sleep hours yield the opposite results. In other words, the values of these three features are inversely proportional to the level of stress experienced. The Decision Tree model has an accuracy of 0.99, and the Random Forest model has an accuracy of 1.0. The results of this research are expected to provide insights for other researchers in the same field and can serve as a reference for detecting ongoing stress.
English
Masalah kesehatan mental menjadi isu global yang sangat umum terjadi, termasuk perubahan suasana hati, perbedaan kepribadian, ketidakmampuan mengatasi masalah, serta mengisolasi diri dari keramaian. Berdasarkan data dari World Health Organization (WHO), gangguan kecemasan dan stres menjadi gangguan mental yang paling sering terjadi dari 970 juta kasus yang dilaporkan sepanjang tahun 2019. Stres telah banyak dikaitkan dengan tidur. Penelitian ini akan mengungkap hubungan kondisi tidur pada manusia dengan tingkat stres yang sedang diderita dengan 5 tingkatan: normal, stres ringan, stres sedang, stres tinggi, stres sangat tinggi. Data yang digunakan merupakan data kontinyu dengan 8 fitur: ‘sr’ (snoring rate), ‘rr’ (respiration rate), ‘t’ (body temperature), ‘lm’ (limb movement), ‘bo’ (blood oxygen), ‘rem’ (rapid eye movement), ‘sh’ (sleeping hours), dan ‘hr’ (heart rate). Setiap fitur memiliki rentang nilai yang tidak sama, sehingga dilakukan normalisasi untuk menyeragamkan rentang tersebut. Hyperparameter tuning dilakukan dengan teknik k-fold cross validation dan model dirancang dengan algoritma klasifikasi Decision Tree serta Random Forest. Hasilnya, 5 fitur: tingkat mendengkur, laju respirasi, pergerakan anggota tubuh termasuk bola mata, serta detak jantung saat tidur berbanding lurus dengan tingkat stres. Semakin tinggi nilai kelima fitur tersebut mengindikasikan tingkat stres yang lebih tinggi. Sedangkan dengan 3 fitur lainnya: suhu tubuh, kadar oksigen, dan waktu tidur memberikan hasil sebaliknya. Dengan kata lain, ketiga nilai tersebut berbanding terbalik dengan tingkat stres yang diderita. Model Decision Tree memiliki akurasi 0,99 dan Random Forest memiliki akurasi 1,0. Hasil penelitian ini diharapkan dapat memberikan insight bagi peneliti lain pada bidang yang sama dan dapat menjadi acuan dalam mendeteksi stres yang sedang diderita. Abstract Stress is often associated with sleep. This research aims to uncover the relationship between human sleep conditions and the level of stress experienced, categorized into five levels: not stressed, very mildly stressed, mildly stressed, highly stressed, and very highly stressed. The data used consists of continuous data with eight features: 'snoring rate' (snoring rate), 'respiration rate' (respiration rate), 'body temperature' (body temperature), 'limb movement' (limb movement), 'blood oxygen' (blood oxygen), 'rapid eye movement' (rapid eye movement), 'sleep hours' (sleep hours), and 'heart rate' (heart rate). Each feature has a different value range, so normalization is performed to standardize these ranges. Hyperparameter tuning is done using k-fold cross-validation, and the model is designed using the Decision Tree and Random Forest classification algorithms. The results show that five features: snoring rate, respiration rate, limb movement including eye movement, and heart rate during sleep are directly proportional to the level of stress. Higher values for these five features indicate higher levels of stress. On the other hand, the other three features: body temperature, blood oxygen level, and sleep hours yield the opposite results. In other words, the values of these three features are inversely proportional to the level of stress experienced. The Decision Tree model has an accuracy of 0.99, and the Random Forest model has an accuracy of 1.0. The results of this research are expected to provide insights for other researchers in the same field and can serve as a reference for detecting ongoing stress.
Keywords
Collaboration network of the authors
40 authors · 72 co-authorship links · 1 across different organisations · showing the 40 most connected
Hover or tap a name to highlight its co-authors. Double-click to open the author's page.
- University of Brawijaya
- Akademi Komunitas Negeri Putra Sang Fajar Blitar
- Affiliation unknown
- Different organisations
- Same organisation
- Affiliation unknown
- Thicker line = more joint articles · bigger circle = more articles
Show as table
| Co-author | Organisation | Joint articles |
|---|---|---|
| Bayu Priyambadha | University of Brawijaya | 7 |
| Retno Indah Rokhmawati | University of Brawijaya | 5 |
| Satrio Agung Wicaksono | University of Brawijaya | 2 |
| Achmad Basuki | University of Brawijaya | 2 |
| Imam Cholissodin | University of Brawijaya | 2 |
| Fitri Utaminingrum | University of Brawijaya | 2 |
| Novanto Yudistira | University of Brawijaya | 2 |
| Herman Tolle | University of Brawijaya | 2 |
| Aisyah Awalina | University of Brawijaya | 2 |
| Aditya Rachmadi | University of Brawijaya | 2 |
| Mochammad Dearifaldi Al Ikhsan | University of Brawijaya | 2 |
| Rona Salsabila | University of Brawijaya | 2 |
| Muhammad Zulfikarrahman | University of Brawijaya | 2 |
| Diva Fardiana Risa | University of Brawijaya | 2 |
| Intan Yusuf Habibie | University of Brawijaya | 2 |
| Joseph Ananda Sugihdharma | University of Brawijaya | 1 |
| Rizal Setya Perdana | University of Brawijaya | 1 |
| Nathanael Victor Darenoh | University of Brawijaya | 1 |
| Ripto Mukti Wibowo | Akademi Komunitas Negeri Putra Sang Fajar Blitar | 1 |
| Aulia Akhrian Syahidi | University of Brawijaya | 1 |
| Fajerin Biabdillah | University of Brawijaya | 1 |
| Mochammad Adhy | University of Brawijaya | 1 |
| Elke Cahya Putri | University of Brawijaya | 1 |
| Nanang Yudi Setiawan | University of Brawijaya | 1 |
| Iqbal Taufiq Ahmad Nur | University of Brawijaya | 1 |
| Indra Syahputra | University of Brawijaya | 1 |
| Bayu Sutawijaya | University of Brawijaya | 1 |
| Sulandri Sulandri | University of Brawijaya | 1 |
| Aisyatul Maulidah | University of Brawijaya | 1 |
| Adam Syarif Hidayatullah | University of Brawijaya | 1 |
| Muhammad Wafi | University of Brawijaya | 1 |
| Ovy Rochmawanti | University of Brawijaya | 1 |
| M. Sofyan Irwanto | University of Brawijaya | 1 |
| Budi Darma Setiawan | University of Brawijaya | 1 |
| Muhammad Nugraha Delta Revanza | Affiliation unknown | 1 |
Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.