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All Journal International Journal of Advances in Applied Sciences Jurnal Kedokteran dan Kesehatan : Publikasi Ilmiah Fakultas Kedokteran Universitas Sriwijaya International Journal of Artificial Intelligence Research Collaborative Medical Journal (CMJ) Klinikal Sains (Jurnal Analis Kesehatan) EDUKATIF : JURNAL ILMU PENDIDIKAN Health and Medical Journal Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan Jurnal Aisyah : Jurnal Ilmu Kesehatan Jurnal Pengabdian Masyarakat Multidisiplin Indonesia Berdaya Jurnal teknologi Kesehatan Borneo Scientific Journal Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat JKKI : Jurnal Kedokteran dan Kesehatan Indonesia Compromise Journal : Community Proffesional Jurnal Abdimas Kesehatan Karawo : Journal of Community Service (KJCS)
Valzon, May
Universitas Abdurrab
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Journal : International Journal of Advances in Applied Sciences

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Electrocardiogram signals classification using random forest method for web-based smart healthcare Juni Nurma Sari; Putri Madona; Hari Kusryanto; Muhammad Mahrus Zain; May Valzon
International Journal of Advances in Applied Sciences Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v12.i2.pp133-143

Abstract

Coronary heart is the highest cause of death in Indonesia reaching 26%. Therefore, to prevent the high mortality rate of coronary heart disease (CHD), early detection of CHD can be carried out. One way is to examine the electrocardiogram/electrocardiograph (ECG) recording. ECG hardware has been made in previous studies to record ECG signals. ECG research is an important study because it can detect cardiovascular disease. Cardiovascular diseases can be classified as arrhythmic diseases. Arrhythmia is a disorder that occurs in the heart rhythm. The method used to recognize and classify ECG signal patterns is the R-R interval (RRI) method. In this study, the ECG signal is classified as normal and abnormal. Abnormal means that a person has a heart rhythm disorder. The classification method used is random forest. The advantage of the random forest classifier is that it can handle noise and missing values and can handle large amounts of data. The accuracy of the ECG signal classification using the Random forest method is 96%. The contribution of this research is that early detection of heart rhythm disorders using an ECG can be monitored through the smart healthcare web.
Co-Authors Agus Jamal Agusmai, Riski Ahmad Naufal, Ahmad alif maulana Amelia Elvarani Aprilianti, Risa Arif Budianto Arzi Putra Persada Asiska Permata Dewi Asma Ul Husna Aulia Izzati, Aulia Ayu Permata ayuningtiyas, ratih Bagus Pramono, Kurnia Cheok Ng, Siew Deinike Marwan Deinike W Marwan Dewi Sartika Siagian Dheana Salsabiela Eddya Dinda Lestari Dona liazarti Dona Liazarti Donaliazarti Donaliazarti Else Chandra Else Chandra Farras, Aqil Fino Hidayat, Arya Fitrianti Hari Kusryanto Hasikin, Khairunnisa Huda Marlina Wati Huda Marlina Wati Imron Khoiri, Muhammad Ismaningsih ismaningsih, ismaningsih Isna Wardaniati Juni Nurma Sari Jusman, Yessi Kenepri Kenepri Kusuma Wardani, Kharisma Laila Indah Fitriya Larysa Fernenda Lasiah Susanti Liazarti, Dona Madona, Putri Maratis Solehah, Nurul Marwan, Deinike Wanita Muga Hapdy Daulay Muhammad Al Fatih Muhammad mahrus zain Muhammad Nur Faisal Rizky Muhammad Yazid Mukhammad Fathkhur Rozaq Nanda Utami, Putri Nova Relida Samosir Nurmaliza, Nurmaliza Oktariani, Eva Pratiwi, Denia Prista Indria Nawang Wulan Rahmatika Hasanah, Atin Raja Alya Zahra Reza Rizky Notisa Rini Hariani Ratih Riski Agusmai Rizka Prasetya Putri Dewi Saddam Muhdi Safardi, Hakim Salamun Salamun Santi Widyasari Santi Widyasari Siagian, Dewi Sartika Sinaga, Grace Krisdayanti Siregar, Uli Astuti Siti Juariah Siti Muawanah Sitimuawanah Sri Wahyuni Suhaila, Tasha Haviva Sukri Sukri Syilvia, Miranda Ulhusna, Nissa Uly Astuti Siregar Uly Astuti Siregar Ummi Azizah Nasution Utami, Putri Nanda Wati, Huda Marlina Widya Ade Putri Sinaga Widya Rizkika Islami Wiwi Sartika Zein, Renni Hidayati