@phdthesis{eprintsunpak7849,
          school = {Universitas Pakuan},
           month = {July},
            year = {2023},
          author = {Moch. Aripin and Soewarto Hardhienata and Teguh Puja Negara},
           title = {J-INTECH (Journal of Information and Technology)
Terakreditasi Kemendikbud SK No. 204/E/KPT/2022
E-ISSN: 2580-720X {\ensuremath{|}}{\ensuremath{|}} P-ISSN: 2303-1425
Deteksi Gangguan Tidur Menggunakan Metode Jaringan Saraf Tiruan 
Berbasis Internet Of Things (IOT)
Moch Aripin1*
, Soewarto Hardhienata2, Teguh Puja Negara3
1,2,3Universitas Pakuan, Fakultas Matematika dan Ilmu Pengetahuan Alam, Program Studi Ilmu Komputer, 
Bogor, Indonesia
Informasi Artikel Abstrak
Diterima: 26-05-2024
Direvisi: 27-05-2024
Diterbitkan: 28-06-2024
Gangguan tidur seperti Central Sleep Apnea (CSA) dan 
Obstructive Sleep Apnea (OSA) dapat berdampak buruk bagi 
kesehatan jika tidak ditangani dengan baik. Penelitian ini 
bertujuan merancang alat pendeteksi gangguan tidur 
menggunakan metode jaringan saraf tiruan berbasis Internet 
of Things (IoT). Sistem ini menggunakan sensor AD8232 untuk 
mengakuisisi sinyal elektrokardiogram (EKG) yang kemudian 
diekstraksi fitur High Frequency dan Low Frequency. Ekstraksi 
fitur dilakukan dengan metode Fast Fourier Transform.
Klasifikasi kondisi normal, CSA, atau OSA dilakukan dengan 
metode jaringan saraf tiruan Multilayer Perceptron yang 
ditraining menggunakan data dari Physionet. Mikrokontroler 
ESP32 digunakan untuk memproses ekstraksi fitur dan 
klasifikasi. Hasil klasifikasi kemudian dikirimkan ke database 
melalui modul WiFi ESP32 dan ditampilkan pada antarmuka 
website. Dari pengujian kinerja sensor AD8232 diperoleh 
akurasi 96,85\%, akurasi klasifikasi menggunakan Jaringan 
Syaraf Tiruan sebesar 80\%, dan waktu komputasi rata-rata 7,6 
ms. Sistem ini berpotensi membantu deteksi dini gangguan 
tidur sehingga dapat ditangani lebih awal oleh tenaga medis.
Kata Kunci
Gangguan tidur; Central Sleep Apnea; 
obstructive Sleep Apnea; Jaringan Saraf 
Tiruan; Internet of Things
*Email Korespondensi:
mocharipin214@gmail.com
Abstract
Sleep disorders such as Central Sleep Apnea (CSA) and 
Obstructive Sleep Apnea (OSA) can have adverse health effects 
if not treated properly. This research aims to design a sleep 
disorder detection device using the Internet of Things (IoT)-
based artificial neural network method. This system uses 
AD8232 sensor to acquire electrocardiogram (ECG) signal 
which is then extracted High Frequency and Low Frequency 
features. Feature extraction is performed using the Fast 
Fourier Transform method. Classification of normal, CSA, or 
OSA conditions is performed using the Multilayer Perceptron 
artificial neural network method which is trained using data 
from Physionet. The ESP32 microcontroller is used to process 
the feature extraction and classification. The classification 
results are then sent to the database via the ESP32 WiFi 
module and displayed on the website interface. From testing 
the performance of the AD8232 sensor, an accuracy of 96.85\% 
was obtained, the classification accuracy using the Artificial 
Neural Network was 80\%, and the average computation time 
was 7.6 ms. This system has the potential to help early 
detection of sleep disorders so that they can be treated early by 
medical personnel},
        abstract = {J-INTECH (Journal of Information and Technology)
Terakreditasi Kemendikbud SK No. 204/E/KPT/2022
E-ISSN: 2580-720X {\ensuremath{|}}{\ensuremath{|}} P-ISSN: 2303-1425
J-INTECH (Journal of Information and Technology)
Terakreditasi Kemendikbud SK No. 204/E/KPT/2022
E-ISSN: 2580-720X {\ensuremath{|}}{\ensuremath{|}} P-ISSN: 2303-1425
Deteksi Gangguan Tidur Menggunakan Metode Jaringan Saraf Tiruan 
Berbasis Internet Of Things (IOT)
Moch Aripin1*
, Soewarto Hardhienata2, Teguh Puja Negara3
1,2,3Universitas Pakuan, Fakultas Matematika dan Ilmu Pengetahuan Alam, Program Studi Ilmu Komputer, 
Bogor, Indonesia
Informasi Artikel Abstrak
Diterima: 26-05-2024
Direvisi: 27-05-2024
Diterbitkan: 28-06-2024
Gangguan tidur seperti Central Sleep Apnea (CSA) dan 
Obstructive Sleep Apnea (OSA) dapat berdampak buruk bagi 
kesehatan jika tidak ditangani dengan baik. Penelitian ini 
bertujuan merancang alat pendeteksi gangguan tidur 
menggunakan metode jaringan saraf tiruan berbasis Internet 
of Things (IoT). Sistem ini menggunakan sensor AD8232 untuk 
mengakuisisi sinyal elektrokardiogram (EKG) yang kemudian 
diekstraksi fitur High Frequency dan Low Frequency. Ekstraksi 
fitur dilakukan dengan metode Fast Fourier Transform.
Klasifikasi kondisi normal, CSA, atau OSA dilakukan dengan 
metode jaringan saraf tiruan Multilayer Perceptron yang 
ditraining menggunakan data dari Physionet. Mikrokontroler 
ESP32 digunakan untuk memproses ekstraksi fitur dan 
klasifikasi. Hasil klasifikasi kemudian dikirimkan ke database 
melalui modul WiFi ESP32 dan ditampilkan pada antarmuka 
website. Dari pengujian kinerja sensor AD8232 diperoleh 
akurasi 96,85\%, akurasi klasifikasi menggunakan Jaringan 
Syaraf Tiruan sebesar 80\%, dan waktu komputasi rata-rata 7,6 
ms. Sistem ini berpotensi membantu deteksi dini gangguan 
tidur sehingga dapat ditangani lebih awal oleh tenaga medis.
Kata Kunci
Gangguan tidur; Central Sleep Apnea; 
obstructive Sleep Apnea; Jaringan Saraf 
Tiruan; Internet of Things
*Email Korespondensi:
mocharipin214@gmail.com
Abstract
Sleep disorders such as Central Sleep Apnea (CSA) and 
Obstructive Sleep Apnea (OSA) can have adverse health effects 
if not treated properly. This research aims to design a sleep 
disorder detection device using the Internet of Things (IoT)-
based artificial neural network method. This system uses 
AD8232 sensor to acquire electrocardiogram (ECG) signal 
which is then extracted High Frequency and Low Frequency 
features. Feature extraction is performed using the Fast 
Fourier Transform method. Classification of normal, CSA, or 
OSA conditions is performed using the Multilayer Perceptron 
artificial neural network method which is trained using data 
from Physionet. The ESP32 microcontroller is used to process 
the feature extraction and classification. The classification 
results are then sent to the database via the ESP32 WiFi 
module and displayed on the website interface. From testing 
the performance of the AD8232 sensor, an accuracy of 96.85\% 
was obtained, the classification accuracy using the Artificial 
Neural Network was 80\%, and the average computation time 
was 7.6 ms. This system has the potential to help early 
detection of sleep disorders so that they can be treated early by 
medical personnel
Moch Aripin1*
, Soewarto Hardhienata2, Teguh Puja Negara3
1,2,3Universitas Pakuan, Fakultas Matematika dan Ilmu Pengetahuan Alam, Program Studi Ilmu Komputer, 
Bogor, Indonesia
Informasi Artikel Abstrak
Diterima: 26-05-2024
Direvisi: 27-05-2024
Diterbitkan: 28-06-2024
Gangguan tidur seperti Central Sleep Apnea (CSA) dan 
Obstructive Sleep Apnea (OSA) dapat berdampak buruk bagi 
kesehatan jika tidak ditangani dengan baik. Penelitian ini 
bertujuan merancang alat pendeteksi gangguan tidur 
menggunakan metode jaringan saraf tiruan berbasis Internet 
of Things (IoT). Sistem ini menggunakan sensor AD8232 untuk 
mengakuisisi sinyal elektrokardiogram (EKG) yang kemudian 
diekstraksi fitur High Frequency dan Low Frequency. Ekstraksi 
fitur dilakukan dengan metode Fast Fourier Transform.
Klasifikasi kondisi normal, CSA, atau OSA dilakukan dengan 
metode jaringan saraf tiruan Multilayer Perceptron yang 
ditraining menggunakan data dari Physionet. Mikrokontroler 
ESP32 digunakan untuk memproses ekstraksi fitur dan 
klasifikasi. Hasil klasifikasi kemudian dikirimkan ke database 
melalui modul WiFi ESP32 dan ditampilkan pada antarmuka 
website. Dari pengujian kinerja sensor AD8232 diperoleh 
akurasi 96,85\%, akurasi klasifikasi menggunakan Jaringan 
Syaraf Tiruan sebesar 80\%, dan waktu komputasi rata-rata 7,6 
ms. Sistem ini berpotensi membantu deteksi dini gangguan 
tidur sehingga dapat ditangani lebih awal oleh tenaga medis.
Kata Kunci
Gangguan tidur; Central Sleep Apnea; 
obstructive Sleep Apnea; Jaringan Saraf 
Tiruan; Internet of Things
*Email Korespondensi:
mocharipin214@gmail.com
Abstract
Sleep disorders such as Central Sleep Apnea (CSA) and 
Obstructive Sleep Apnea (OSA) can have adverse health effects 
if not treated properly. This research aims to design a sleep 
disorder detection device using the Internet of Things (IoT)-
based artificial neural network method. This system uses 
AD8232 sensor to acquire electrocardiogram (ECG) signal 
which is then extracted High Frequency and Low Frequency 
features. Feature extraction is performed using the Fast 
Fourier Transform method. Classification of normal, CSA, or 
OSA conditions is performed using the Multilayer Perceptron 
artificial neural network method which is trained using data 
from Physionet. The ESP32 microcontroller is used to process 
the feature extraction and classification. The classification 
results are then sent to the database via the ESP32 WiFi 
module and displayed on the website interface. From testing 
the performance of the AD8232 sensor, an accuracy of 96.85\% 
was obtained, the classification accuracy using the Artificial 
Neural Network was 80\%, and the average computation time 
was 7.6 ms. This system has the potential to help early 
detection of sleep disorders so that they can be treated early by 
medical personnel},
             url = {http://eprints.unpak.ac.id/7849/}
}