Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Ratna Lestari Budiani Buana
Universitas Gadjah Mada
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Review: Analisis Fitur Deteksi Aritmia dan Metode Deep Learning untuk Wearable Devices Ratna Lestari Budiani Buana; Imroatul Hudati
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 11 No 1: Februari 2022
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1321.399 KB) | DOI: 10.22146/jnteti.v11i1.3381

Abstract

Arrhythmia is one of the heart abnormalities which probably not a life threat in a short time but could cause a long-term interference in electricity of the heart. Even so, it should be detected earlier to have proper treatment and suggest a better lifestyle. Arrhythmia diagnosis is usually made by performing a long recording ECG by using Holter monitoring then analyzing the rhythm. Nevertheless, the observation takes time, and using Holter in several days may affect the patient’s physiological condition. Previous research has been conducted to build an auto-detection of arrhythmia by using various datasets, different features, and detection methods. However, the biggest challenges faced by the researcher were the computation and the complex features used as the algorithm input. This study aims to review the latest research on the data used, features, and deep learning methods that can solve the time computation problem and be applied in wearable devices. The review method started by searching the related paper, then studied on the data used. The second step was to review the used ECG features and the deep learning method implemented to detect arrhythmia. The review shows that most researchers used the MIT-BIH database, even it requires a lot of effort on the pre-processing. The CNN is the most used deep learning method, but time computation is one of the considerations. The ECG interval features in the time domain are the best feature analysis for rhythm abnormality detection and have a low computation cost. These features will be the input of the deep learning process to reduce computation time, especially on wearable device applications.
Identification of plasmodium falciparum and plasmodium vivax on digital image of thin blood films gf Hanung Adi Nugroho; Made Satria Wibawa; Noor Akhmad Setiawan; E. Elsa Herdiana Murhandarwati; Ratna Lestari Budiani Buana
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 3: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i3.pp933-944

Abstract

Observing presence of Plasmodium parasite of stained thick or thin blood films through microscopic examination is a gold standard for malaria diagnosis.  Although the microscopic examination has been extensively used, misidentification might occur caused by human factors.  In order to overcome misidentification problem, several studies have been conducted to develop a computer-aided malaria diagnosis (CADx) to assist paramedics in decision-making.  This study proposes an approach to identify species and stage of Plasmodium falciparum and Plasmodium vivax on thin blood films collected from the Laboratory of Parasitology, Faculty of Medicine, Universitas Gadjah Mada.  Adaptive k-means clustering is applied to segment Plasmodium parasites.  A total of 39 features consisting of shape and texture features are extracted and then selected by using wrapper-based forward and backward directions.  Classification is evaluated in two schemes.  The first scheme is to classify the species of parasite into two classes. The second scheme is to classify the species and stage of parasite into six classes.  Three classifiers applied are k-nearest neighbour (KNN), support vector machine (SVM) and multi-layer perceptron (MLP).  Furthermore, to facilitate the multiclass classification, one-versus-one (OVO) and one-versus-all (OVA) methods are implemented.  The first scheme achieves the accuracy of 88.70% based on MLP classifier using three selected features.  While the accuracy gained by the second scheme is 95.16% based on OVO and MLP classifier using 29 selected features.  These results indicate that the proposed approach successfully identifies the species and stage of parasite on thin blood films and has potential to be implemented in the CADx system for assisting paramedics in diagnosing malaria.
Co-Authors E. Elsa Herdiana Murhandarwati Hanung Adi Nugroho Imroatul Hudati Made Satria Wibawa Noor Akhmad Setiawan