Article Per Year (5 Year)
p-Index From 2020 - 2025
0.888
P-Index
This Author published in this journals
All Journal Jurnal Sarjana Teknik Informatika Indonesian Journal of Electrical Engineering and Informatics (IJEEI) ELINVO (Electronics, Informatics, and Vocational Education) INTEK: Jurnal Penelitian
Winursito, Anggun
Unknown Affiliation
Computer Science & IT Education Electrical & Electronics Engineering Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Performance Analysis of EMG Signal Classification Methods for Hand Gesture Recognition in Stroke Rehabilitation Winursito, Anggun; Arifin, Fatchul; Muslikhin, Muslikhin; Artanto, Herjuna; Caryn, Femilia Hardina
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 8 No. 2 (2023): November 2023
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v8i2.76811

Abstract

This study evaluates the performance of different classification methods in classifying healthy individuals and stroke patients. The hand gesture variations of the subjects were also analyzed based on electromyography (EMG) signals. Several classification methods were tested in this analysis to find out which method had the most suitable performance. The results showed that Decision Tree and Naive Bayes classifiers achieved the highest performance in classifying EMG signals from healthy individuals and stroke patients, with both methods showing high accuracy, precision, recall, and F1 score. Specifically, Decision Tree excelled in overall accuracy and recall, while Naive Bayes showed superior precision. For hand gesture recognition, SVM, KNN, and Random Forest classifiers showed similarly high performance, achieving accuracy, precision, recall, and F1 score above 82%. Naive Bayes also performed well, especially in precision, while Decision Tree performed poorly compared to other methods. This insight can form the basis for the development of more effective and personalized rehabilitation systems for stroke patients, by utilizing reliable and accurate EMG signal classification
Advanced Multimodal Emotion Recognition for Javanese Language Using Deep Learning Arifin, Fatchul; Nasuha, Aris; Priambodo, Ardy Seto; Winursito, Anggun; Gunawan, Teddy Surya
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5662

Abstract

This research develops a robust emotion recognition system for the Javanese language using multimodal audio and video datasets, addressing the limited advancements in emotion recognition specific to this language. Three models were explored to enhance emotional feature extraction: the SpectrogramImage Model (Model 1), which converts audio inputs into spectrogram images and integrates them with facial images for emotion labeling; the Convolutional-MFCC Model (Model 2), which leverages convolutional techniques for image processing and Mel-frequency cepstral coefficients for audio; and the Multimodal Feature-Extraction Model (Model 3), which independently processes video and audio features before integrating them for emotion recognition. Comparative analysis shows that the Multimodal Feature-Extraction Model achieves the highest accuracy of 93%, surpassing the Convolutional-MFCC Model at 85% and the Spectrogram-Image Model at 71%. These findings demonstrate that effective multimodal integration, mainly through separate feature extraction, significantly enhances emotion recognition accuracy. This research improves communication systems and offers deeper insights into Javanese emotional expressions, with potential applications in human-computer interaction, healthcare, and cultural studies. Additionally, it contributes to the advancement of sophisticated emotion recognition technologies.
Pengembangan Sistem Monitoring Kesehatan Jantung Tahan Noise Berbasis Sinyal EKG Winursito, Anggun
Jurnal Sarjana Teknik Informatika Vol. 10 No. 2 (2022): Juni
Publisher : Program Studi Informatika, Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jstie.v10i2.24153

Abstract

Penelitian mengenai sistem monitoring kesehatan jantung secara otomatis banyak dilakukan, namun masih belum menghasilkan output yang maksimal. Permasalahan utama dari penelitian yang sudah ada adalah akurasi sistem monitoring yang masih rendah terutama pada kondisi sinyal EKG yang mengandung noise. Pada penelitian ini dirancang sistem deteksi yang tahan noise melalui pengembangan algoritma kombinasi, serta dirancang prototipe hardware dan software sistem pelayanan bagi pasien dalam memonitoring kesehatan jantung. Algortima kombinasi menggunakan Wavelet dan Artificial Neural Network (ANN). Output sinyal hasil proses denoising dimasukkan dalam proses klasifikasi menggunakan ANN dan output deteksi berupa kondisi sinyal EKG yang menggambarkan keadaan jantung normal atau abnormal. Proses denoising dirancang menggunakan Wavelet dengan mengujicobaan beberapa tipe Wavelet Daubechies, Symlet, serta Coiflet pada sinyal EKG yang mengandung noise. Hasil penelitian menunjukkan bahwa algoritma kombinasi mampu memperbaiki performa sistem deteksi konvensional pada proses monitoring kesehatan jantung. Software monitoring serta prosedur pelayanan pasien juga dirancang berbasis website dan menggunakan teknologi internet of thngs.
Improving Speed Performance of Select Random Query in SQL Database Utomo, Muhammad Nur Yasir; Bastian, Alvian; Winursito, Anggun
INTEK: Jurnal Penelitian Vol 7 No 1 (2020): In Press
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (868.105 KB) | DOI: 10.31963/intek.v7i1.1536

Abstract

Select random is a query in a SQL database that can retrieve data randomly from a table. Select random is often used to present data in various applications such as websites, data mining and others. Unfortunately, ordinary select random query is inefficient in terms of processing time if used in large table. This paper, tries to solve this problem by proposing two optimized methods of select random query, namely the Small Percentage Order by Rand (SPO-Rand) and the Filtered Column Order by Rand (FCO-Rand). The two proposed methods are then compared in terms of processing speed with a standard Select Random query or Normal Order by Rand (NO-Rand). The scenario of the experiment is to collect five random data from several data sets, ranging from 10.000 to 200.000 data. Based on the results of experiments that have been conducted, the proposed FCO-Rand method obtained the best process speed with 0.074 seconds at 200.000 data, followed by SPO-Rand with 0.265 seconds. These result are much faster than the standard random select method (NO-Rand) which takes up to 7,035 seconds for the same task.
Co-Authors Ardy Seto Priambodo Aris Nasuha BASTIAN, ALVIAN Caryn, Femilia Hardina Fatchul Arifin Gunawan, Teddy Surya Herjuna Artanto Muslikhin Utomo, Muhammad Nur Yasir