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All Journal TEKNIK INFORMATIKA JSI: Jurnal Sistem Informasi (E-Journal) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Komputer Terapan Seminar Nasional Teknologi Informasi Komunikasi dan Industri JOURNAL OF APPLIED INFORMATICS AND COMPUTING Sebatik ILKOM Jurnal Ilmiah IJITEE (International Journal of Information Technology and Electrical Engineering) Jurnal Pengabdian Masyarakat Indonesia Caring : Jurnal Pengabdian Masyarakat Jurnal Nasional Teknik Elektro dan Teknologi Informasi Technologica Jurnal Sistem Informasi Jurnal Kecerdasan Buatan dan Teknologi Informasi Scientific Journal of Informatics
Warnia Nengsih
Politeknik Caltex Riau
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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KLASIFIKASI SUARA JANTUNG MENGGUNAKAN DEEP LEARNING INTEGRASI AI DALAM APLIKASI WEB UNTUK DETEKSI DINI GANGGUAN KARDIOVASKULAR nengsih, warnia; Elviyenti, Mona; Fadhli, Mardhiah; Aleanda, Galih; Utama, Nuradila
Jurnal Komputer Terapan Vol 11 No 2 (2025): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jkt.v11i2.6516

Abstract

Heart disease is one of the leading causes of death worldwide, making early detection crucial to prevent more serious complications. One of the methods that can be used is heart sound analysis, which contains important information related to the physiological and pathological conditions of the heart. However, the manual analysis process by healthcare professionals requires specialized skills and may result in interpretation errors. Therefore, this research aims to develop an artificial intelligence-based system using Convolutional Neural Networks (CNN) to automate heart sound classification. This system allows users to upload heart sound recordings, which will then be processed and classified as Normal or Abnormal. The research process consists of several main stages, including data collection and preprocessing of heart sounds, development and training of the CNN model, implementation of the model into a web application, and testing and evaluation of the system using metrics such as accuracy, precision, and recall. The outcome of this research includes a deep learning model for heart sound classification. The developed system is expected to enhance the accuracy and efficiency of heart disease detection, reduce reliance on manual analysis, and serve as an artificial intelligence-based solution that can be integrated into healthcare services.
Co-Authors Akbar, Memen Aleanda, Galih Ardiyanto Ardiyanto Aulia Gusri Pratama Ayu Putri Lestari Cici Angresta Cyntia Widiasari Dini Hidayatul Qudsi Dini Nurmala Sari Eka Ariefyanto Putra Eki Haiyal'ulya Elnovrian Purnama Saghita Elviyenti, Mona Felix Gary Hajijah Noor Hasna Fikriyah Dwinas Helmi Chazali Lubis Indra Agus Lukman Irwan Chandra Istianah Muslim Juni Nurma Sari Juni Nurma Sari M. Mahrus Zein Mardhiah Fadhli, Mardhiah Muhammad Saputra mutia sari zulvi Nazifa Hayati Nurmalasari, Dini Nurwijayanti Putri Madhona Rama Yoedha Satria Ria Anjelina Agustin Rizky Fitria Rizky Fitria Shinta Utiya Syah Syefrida Yulina Syefrida Yulina, Syefrida Syefrida, Yulina T.Marlina Cahyani Utama, Nuradila Widyasari, Cyntia Wiwin Styorini Yuli Fitrisia