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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING Jurnal Ilmiah FIFO MIND (Multimedia Artificial Intelligent Networking Database) Journal TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika JMECS (Journal of Measurements, Electronics, Communications, and Systems) Jurnal Teknik Informatika (JUTIF) Charity : Jurnal Pengabdian Masyarakat Prosiding SNAST Jurnal Abdi Masyarakat Indonesia eProceedings of Engineering Jurnal INFOTEL
R. Yunendah Nur Fu’adah
Telkom University
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences

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Journal : MIND (Multimedia Artificial Intelligent Networking Database) Journal

 1 
Sistem Otentikasi Biometrik Berbasis Sinyal EKG Menggunakan Convolutional Neural Network 1 Dimensi FAUZI FRAHMA TALININGSIH; YUNENDAH NUR FU’ADAH; SYAMSUL RIZAL; ACHMAD RIZAL; MUHAMMAD ADNAN PRAMUDITO
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 7, No 1 (2022): MIND Journal
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v7i1.1-10

Abstract

ABSTRAKBiometrik merupakan salah satu analisis karakteristik individu yang saat ini banyak digunakan, seperti sidik jari, pengenalan suara, dan pengenalan wajah. Metode biometrik tersebut masih memiliki kelemahan seperti mudah untuk dimanipulasi. Oleh karena itu, penelitian ini akan menggunakan sinyal Elektrokardiogram (EKG) sebagai salah satu metode biometrik. Sinyal EKG memiliki keunikan pada setiap individu sehingga sulit untuk dimanipulasi. Penelitian ini mengembangkan sistem otentikasi biometrik berbasis sinyal EKG. Data yang digunakan berasal dari ECG-ID database dengan jumlah 90 subjek. Sinyal EKG yang digunakan hanya menggunakan gelombang PQRST sebagai input model Convolutional Neural Network 1 Dimensi (CNN). Hasil akurasi yang diperoleh menunjukkan 92.2%. Dengan demikian, sistem yang dikembangkan memungkinkan digunakan sebagai otentikasi biometrik.Kata kunci: Biometrik, Sinyal EKG, Convolutional Neural NetworkABSTRACTBiometrics is analyses individual characteristics that are currently widely used, such as fingerprints, voice recognition, and face recognition. The biometric method still has weaknesses, such as being easy to manipulate. Therefore, this study will use an Electrocardiogram (ECG) signal as a biometric method. The ECG signal is unique to each individual, so it is not easy to manipulate. This study develops a biometric authentication system based on ECG signals. The data used comes from the ECG-ID database with a total of 90 subjects. The ECG signal used only PQRST waves as input for the 1-Dimensional Convolutional Neural Network (CNN) model. The accuracy results obtained show 92.2%. Thus, the developed system allows it to be used as biometric authentication.Keywords: Biometric, ECG Signal, Convolutional Neural Network
Identifikasi Sinyal Congestive Heart Failure dengan Metode Convolutional Neural Network 1D MUHAMMAD ADNAN PRAMUDITO; YUNENDAH NUR FU’ADAH; RITA MAGDALENA; ACHMAD RIZAL; FAUZI FRAHMA TALININGSIH
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 7, No 1 (2022): MIND Journal
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v7i1.11-20

Abstract

ABSTRAKPenyakit jantung merupakan salah satu penyebab utama kematian di dunia. Salah satu penyakit jantung yang perlu diperhatikan adalah congestive heart failure (CHF). CHF adalah suatu kondisi di mana jantung tidak mampu memompa darah ke seluruh tubuh. Penyakit ini dapat didiagnosis dengan EKG. Oleh karena itu, pada penelitian ini dibuat sebuah sistem yang dapat mengidentifikasi penyakit CHF secara otomatis menggunakan metode convolutional neural network (CNN) dengan 4 hidden layer dan 16 output channel, fully connected layer, dan aktivasi Softmax. Data yang digunakan dalam penelitian ini diambil dari MITBIH dan BIDMC. Penlitian ini memberikan akurasi 100%, sehingga deteksi penyakit CHF otomatis membantu staf medis mendiagnosis pasien untuk menerima perawatan yang tepat.Kata kunci: Elektrokardiogram (EKG), Convolutional Neural Network (CNN), Normal Sinus Rhythm (NSR), Congestive Heart Failure (CHF)ABSTRACTHeart disease is one of the leading causes of death in the world. One of the heart diseases that need to be considered is congestive heart failure (CHF). CHF is a condition in which the heart is unable to pump blood throughout the body. ECG can diagnose this disease. Therefore, this study created a system that can automatically identify CHF disease using the convolutional neural network (CNN) method with four hidden layers and 16 output channels, a fully connected layer, and Softmax activation. The data used in this study were taken from MIT-BIH and BIDMC. In this study provides 100% accuracy. Automated CHF disease detection helps medical staff diagnose patients to receive appropriate treatment.Keywords: Electrocardiogram (ECG), Convolutional Neural Network (CNN), Normal Sinus Rhythm (NSR), Congestive Heart Failure (CHF) 
Co-Authors Achmad Rizal Adam Agus Kurniawan Adinda Maulida Agung Aditama Putra Ahmad Fauzan Fauzan Ahmad Zendhaf Allisha Septariani Ahmad Alva Rischa Qhisthana Pratika Ardhi Fibrianto Avon Budiono Azis Ansori Wahid Daulay, Muhammad Agil Syaifullah Dian Ayu Nurlitasari Dyah Retno Mutia Edwar Efri Suhartono FAUZI FRAHMA TALININGSIH Febriani Ruming Sari Firdaus, Muhammad Naufal Firos Fathul Alam Gelar Budiman Hurianti Vidya Hurianti Vidyaningtyas Ibnu Da'wan Salim Ibnu Da’wan Salim Ubaidah Ihsan Budi Purwono Ilma Rahma Dewi Imanuel Boyke Nainggolan Inung Wijayanto Irdin Arjulian Irham Bani Alfafa Jangkung Raharjo Koredianto Usman Ledya Novamizanti Lugina Perceka Putri M Teguh Kurniawan Maghfira Rifki Hariadi Miftahul Fawaz Muhamad Reinaldi Kurniawan Muhamad Rokhmat Isnaini MUHAMMAD ADNAN PRAMUDITO Muhammad Akhyar Ghifari Muhammad Ardhi Prakasa Muhammad Dwi Cahyo Muhammad Yuqdha Faza Mulyantini, Agustien N Kumalasari Caecar Pratiwi Nabila Herman Naufal Adi Gifran Nidaan Khofiya Nivadirrokhman, Dhanendra Nor Kumalasari Nor Kumalasari Caecar Pratiwi Nur Alyyu Nur Ibrahim Ocky Tiaramukti Pandu Jati Utomo PRAMUDITHO, MUHAMMAD ADNAN PRATIWI, NOR KUMALASARI CAESAR Putra, Rafly Fasha Purnomo Raditiana Patmasari Rafid Fakhri Rahmad Hidayatullah Salam Rahmiati Aulia Ramadhan, Ardiansyah Ratna Sari Ratri Dwi Atmaja Razief Moch Diar Rd. Rohmat Saedudin Rezki Ariz Rahadian Rifky Abdul Khafid Rifqi Muhammad Fikri Rita Magdalena Rita Purnamasari Rizki Muhammad Iqbal Rizky Gilang Gumilar Saiful Azis Santosa, Atharizky Ade Sari, Febriani Ruming Siti Hajar Komariah SOFIA SAIDAH Sony Sumaryo Steven Palondongan Sugondo Hadiyoso SY, NIDAAN KHOFIYA Syafiq Hilmi Abdullah Syamsul Rizal Syamsul Rizal TALININGSING, FAUZI FRAHMA Teguh Dian Arifandi Teguh Musaharpa Gunawan UBAIDULLAH, IBNU DAWAN Vidya, Hurianti Wawan Tripiawan Yoga Yuniadi Zuhri, Hamdan Syaifuddin