Klasifikasi Arritmia pada Sinyal EKG menggunakan Deep Neural Network

Abstrak Penelitian yang dikembangkan saat ini memfokuskan klasifikasi sinyal Electrokardiogram (EKG) pada gangguan arritmia detak jantung. Monitoring ini bertujuan agar dapat menjadi penanganan dini terhadap berbagai jenis gangguan arritmia. Klasifikasi yang diajukan dapat mengklasifikasi 9 jenis gangguan arritmia dengan menggunakan metode Deep Neural Network (DNN). Teknik preprosessing data pada sinyal EKG sebelum proses klasifikasi, yaitu segmentasi, normalisasi menggunakan normalize bound, dan fitur extraction dengan menggunakan autoencoder. Hasil menunjukkan bahwa metode yang digunakan mendapatkan nilai akurasi yang sangat baik sebesar 99.62% dan sensitivity about 97.18%. Kata kunci—EKG, Arritmia, Klasifikasi, Deep Neural Network  Abstract The research developed today focuses the classification of Electrocardiogram (ECG) signals on heart rate arritmia disorders. This monitoring aims to be an early treatment of various types of arritmia disorders. Using the Deep Neural Network (DNN) process, the proposed classification will identify 9 kinds of arrhythmia disorders. Preprocessing of the ECG signal data technique before the classification process, namely segmentation, normalization using bound normalization, and autoencoder extraction function. Results showed that the system used gained an outstanding 99.62 percent precision value and about 97.18 percent sensitivity. Kata kunci—ECG, Arrhytmia, Classifikation, Deep Neural Networ

Penerapan Metode System Usability Scale (Sus) Perangkat Lunak Daftar Hadir di Pondok Pesantren Miftahul Jannah Berbasis Website

The application of technology from time to time develops rapidly, because with the current application of technology it is easier for human work, one of which is by using software, at the Miftahul Jannah Islamic boarding school located in the village of observation, Kec. In the observation of OKU district, in South Sumatra, there has been no application of technology, namely software, one of which is a website-based attendance list software. The purpose of this research is to build a software attendance list at the Miftahul Jannah Islamic boarding school for teachers, students and female students by applying the System Usability Scale (SUS) method. This research method uses observation, interviews, literature study, needs analysis as well as building and implementing attendance list software using the application of the System Usability Scale (SUS) method, where the criteria for implementing the SUS method are if the value is greater than 80.3 then the criteria are very good, while the value from 68 to 80.3 means software with the application criteria is good, the value of 68 application of the SUS method is sufficient, while the value of 51 to 68 with the criteria for applying the SUS method is less, the value is below 51 then the criteria for applying the SUS method is very lacking, As for the results of the application using the method System Usability Scale (SUS), get the results of the respondents for k teachers where the average value of the conversion results obtained was 79.54, while for student respondents showed the average value conversion result using the application of the SUS method the result was 79.33 where the conversion results The application of the SUS method in this study has been accepted in accordance with the criteria for the distribution of the system usability scale (SUS) method

Klasifikasi Arritmia pada Sinyal EKG Menggunakan Deep Neural Network

Penelitian yang dikembangkan saat ini memfokuskan klasifikasi sinyal Electrokardiogram (EKG) pada gangguan arritmia detak jantung. Monitoring ini bertujuan agar dapat menjadi penanganan dini terhadap berbagai jenis gangguan arritmia. Klasifikasi yang diajukan dapat mengklasifikasi 9 jenis gangguan arritmia dengan menggunakan metode Deep Neural Network (DNN). Teknik preprosessing data pada sinyal EKG sebelum proses klasifikasi, yaitu segmentasi, normalisasi menggunakan normalize bound, dan fitur extraction dengan menggunakan autoencoder. Hasil menunjukkan bahwa metode yang digunakan mendapatkan nilai akurasi yang sangat baik sebesar 99.62% dan sensitivity about 97.18%. Kata kunci—EKG, Arritmia, Klasifikasi, Deep Neural Networ