Article Per Year (5 Year)
p-Index From 2020 - 2025
7.382
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Informatics (IJEEI) JITK (Jurnal Ilmu Pengetahuan dan Komputer) International Journal of Computing Science and Applied Mathematics INTECOMS: Journal of Information Technology and Computer Science Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JSAI (Journal Scientific and Applied Informatics) Jurnal Ilmu Teknik dan Komputer Journal of Computer Science and Engineering (JCSE) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Sistem Informasi Dan E-Bisnis Jurnal Ilmu Komputer Dan Informatika Dedikasi Nusantara: Jurnal Pengabdian Masyarakat Pendidikan Dasar Sinergi: Jurnal Pengabdian Kepada Masyarakat JPMTT (Jurnal Pengabdian Masyarakat Teknologi Terbarukan)
Ayumi, Vina
Universitas Mercu Buana
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

Published : 57 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : International Journal of Electrical and Computer Engineering

 1 
Implementation of deep neural networks (DNN) with batch normalization for batik pattern recognition Ida Nurhaida; Vina Ayumi; Devi Fitrianah; Remmy A. M. Zen; Handrie Noprisson; Hong Wei
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (580.261 KB) | DOI: 10.11591/ijece.v10i2.pp2045-2053

Abstract

One of the most famous cultural heritages in Indonesia is batik. Batik is a specially made drawing cloth by writing Malam (wax) on the cloth, then processed in a certain way. The diversity of motifs both in Indonesia and the allied countries raises new research topics in the field of information technology, both for conservation, storage, publication and the creation of new batik motifs. In computer science research area, studies about Batik pattern have been done by researchers and some algorithms have been successfully applied in Batik pattern recognition. This study was focused on Batik motif recognition using texture fusion feature which is Gabor, Log-Gabor, and GLCM; and using PCA feature reduction to improve the classification accuracy and reduce the computational time. To improve the accuracy, we proposed a Deep Neural Network model to recognise batik pattern and used batch normalisation as a regularises to generalise the model and to reduce time complexity. From the experiments, the feature extraction, selection, and reduction gave better accuracy than the raw dataset. The feature selection and reduction also reduce time complexity. The DNN+BN significantly improve the accuracy of the classification model from 65.36% to 83.15%. BN as a regularization has successfully made the model more general, hence improve the accuracy of the model. The parameters tuning also improved accuracy from 83.15% to 85.57%.
Hospital quality classification based on quality indicator data during the COVID-19 pandemic Nurhaida, Ida; Dhamanti, Inge; Ayumi, Vina; Yakub, Fitri; Tjahjono, Benny
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4365-4375

Abstract

This research aim is to propose a machine learning approach to automatically evaluate or categories hospital quality status using quality indicator data. This research was divided into six stages: data collection, pre-processing, feature engineering, data training, data testing, and evaluation. In 2020, we collected 5,542 data values for quality indicators from 658 Indonesian hospitals. However, we analyzed data from only 275 hospitals due to inadequate submission. We employed methods of machine learning such as decision tree (DT), gaussian naïve Bayes (GNB), logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), linear discriminant analysis (LDA) and neural network (NN) for research archive purposes. Logistic regression achieved a 70% accuracy rate, SVM a 68% accuracy rate, and neural network a 59.34% of accuracy. Moreover, K-nearest neighbors achieved a 54% of accuracy and decision tree a 41% accuracy. Gaussian-NB achieved a 32% accuracy rate. The linear discriminant analysis achieved the highest accuracy with 71%. It can be concluded that linear discriminant analysis is the algorithm suitable for hospital quality data in this research.
Co-Authors Abd Rahman Abrar, Muhammad Fauzan Angga, Tyas Dwi Anita Ratnasari Anzalna, Akbarakah Bagus Priambodo Cahyo Pambudi Darmo Desi Ramayanti Desmon, Johanes Devi Fitrianah Dhamanti, Inge Dianing Asri, Sri ferdi Fitri Yakub Hadiguna Setiawan Hong Wei Ida Farida Ida Nurhaida Ida Nurhaida Ida Nurhaida Inge Handriani, Inge Iryani, Lemi Jumaryadi, Yuwan Laksono Trisnantoro Mailana, Siska Mariana Purba Mariana Purba Naufal, Muhamad Fahri Nengsiana, Nengsiana Noprisson, Handrie Nur Ani PAISAL PAISAL Purba, Mariana Putra, Erwin Dwika Remmy A. M. Zen Salamah, Umniy Sarwati Rahayu Sri Dianing Asri Tjahjono, Benny Uus Rusmawan, Uus Wachyu Hari Haji Wachyu Hari Haji Wina Firly, Tiara Yogga Herlambang, Helwy