The week's pick
Random Articles
Reseach Article
Heart Rate Monitoring System using Max30102 Sensor and Gaussian Naive Bayes Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 47 |
| Year of Publication: 2023 |
| Authors: Beni Mustiko Aji, Wahyu Sri Utami |
10.5120/ijca2023923249
|
Beni Mustiko Aji, Wahyu Sri Utami . Heart Rate Monitoring System using Max30102 Sensor and Gaussian Naive Bayes Algorithm. International Journal of Computer Applications. 185, 47 ( Dec 2023), 7-12. DOI=10.5120/ijca2023923249
Abstract
Heart health is a crucial aspect for the elderly, as it is the leading cause of death in Indonesia. However, the elderly often do not receive sufficient monitoring on a regular basis at Ngudi Makmur Integrated Health Post. Therefore, a solution is proposed to overcome this problem by introducing a heart rate monitoring system using Max30102 sensor and Gaussian Naive Bayes algorithm. The system is designed to monitor the heart rate and display the heart health condition based on the average of the measurements taken. The calibration results of the device used showed an accuracy rate of 95.8%, while testing the Gaussian Naive Bayes algorithm with the k-fold cross-validation method resulted in an accuracy of 91%. The envisioned system aims to enhance the motivation of elderly individuals at Posyandu Ngudi Makmur, encouraging them to consistently undergo routine heart health checks.
References
- Kementrian Kesehatan Republik Indonesia, “Hasil Utama
- Riskesdas 2018,” Jakarta, 2018.
- L. Herawati et al., Personal Trainer Guide, 1st ed. Departemen Ilmu Faal Fakultas Kedokteran Universitas Airlangga, 2019.
- K. Hajer, A. Dhahir, and J. A. Al-tuwairijari, “Cancer Classification Using Gaussian Naive Bayes Algorithm,” in International Engineering Conference (IEC), Erbil, Iraq: IEEE, Jun. 2019, pp. 165–166.
- D. N. Ilham, Hardisal, and R. A. Candra, Monitoring dan Stimulasi Detak Jantung dengan Murotal Al-Qur’an Berbasis Internet of Things (IoT). CV Jejak, anggota IKAPI, 2020.
- Whiliams and S. C. Sumarta, “Simulasi Sistem Monitoring Kondisi Pasien Serangan Jantung Menggunakan Arduino Dan Sensor Detak Jantung,” Jurnal Tematika, vol. VOL. 10, NO. 1, pp. 46–49, Mar. 2022.
- A. R. Rinaldi, “Rancang Bangun Alat Deteksi Jantung Berbasis Mikrokontroler Arduino Dengan Pulse Sensor,” Forum Pendidikan Tinggi Teknik Elektro Indonesia Regional VII, pp. 374–376, 2021.
- J. Dian, F. D. Silalahi, and N. D. Setiawan, “Sistem Monitoring Detak Jantung Untuk Mendeteksi Tingkat Kesehatan Jantung Berbasis Internet Of Things Menggunakan Android,” Jurnal JUPITER, vol. Vol. 13 No. 2, pp. 69–72, Oct. 2021.
- R. Mayasari, N. Nafisah, and A. Natuzzuharryyah, “Klasifikasi Tingkat Kepuasan Mahasiswa Terhadap Pembelajaran Secara Daring Menggunakan Algoritma Naive Bayes,” JISka (Jurnal Informatika Sunan Kalijaga), vol. Vol. 6, No. 3, p. 163, Jun. 2021.
- H. Kamel, D. Abdulah, and J. M. Al-Tuwaijari, “Cancer Classification Using Gaussian Naive Bayes Algorithm,” in International Engineering Conference (IEC), Erbil, Iraq: IEEE, Jun. 2019, pp. 166–166.
- Y. Findawati and M. A. Rosid, Buku Ajar Text Mining, Pertama. Sidoarjo: UMSIDA PRESS, 2020.
- A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Second. O’Reilly Media, Inc., 2019.
- U. Indahyant and Y. Rahmawati, Algoritma dan Pemrograman Dalam bahasa c++, Pertama. Sidoarjo: UMSIDA Press, 2020.
- D. Indra Ava, Logika dan Algoritma Untuk Merancang Aplikasi Komputer , 1st ed. Semarang: Yayasan Prima Agus Teknik, 2021.
- F. Rouessac and A. Rouessac, Analysist Modern Intrumentation Method Techques. West Sussex: Willy & Sons, 2007.
- R. Kumar, Machine Learning Quick Reference. Packt Publishing, 2019.
Index Terms
Keywords