Article Per Year (5 Year)
p-Index From 2020 - 2025
1.909
P-Index
This Author published in this journals
All Journal JOIV : International Journal on Informatics Visualization Jurnal Perangkat Lunak
Eva Khudzaeva
UIN Syarif Hidayatullah
Arts Computer Science & IT

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

Found 2 Documents
Search

 1 
ANALISIS USABILITY APLIKASI BRIMO DENGAN MENGGUNAKAN METODE KUESIONER DAN MODEL DELONE & MCLEAN Muhammad Asnadi, Nur; Khudzaeva, Eva
JURNAL PERANGKAT LUNAK Vol 5 No 2 (2023): Jurnal Perangkat Lunak
Publisher : Indragiri Islamic University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/jupel.v5i2.2625

Abstract

The usage of internet and mobile banking in Indonesia has experienced significant growth and provided positive impacts for users. One popular mobile banking application is BRImo from Bank BRI. The increase in application usage is driven by the COVID-19 pandemic and the extensive development of digital banking services. The objective of this research is to evaluate customer satisfaction levels with the BRImo application and analyze the usability factors that influence satisfaction. The NAU (Nielsen Attributes of Usability) questionnaire method and the DeLone and McLean model are employed to achieve these objectives. A g-form questionnaire consisting of 20 statement items is distributed to collect data. Additionally, 7 hypotheses are formulated to test the relationships among the factors influencing customer satisfaction. After the calculation, the researcher found that the BRImo application has a usability level of 76.57%. This result indicates that the application has a fairly good level of usability. In addition, based on the analysis, all hypotheses in this study were accepted, marked by a very strong correlation between the variables. This indicates that the factors investigated have a significant impact on the usability level of the application.
Economic Impact due Covid-19 Pandemic: Sentiment Analysis on Twitter Using Naïve Bayes Classifier and Support Vector Machine Aini, Qurrotul; Fauzi, Raffie Rizky; Khudzaeva, Eva
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1474

Abstract

Covid-19 is an outbreak caused by severe acute respiratory syndrome. Covid-19 first appeared in Indonesia on March 2, 2020, with two confirmed cases and increased to 1285 cases in 30 provinces. One of the impacts of the Covid-19 pandemic is on the economic aspect, which has experienced a drastic decline in income. This study aims to classify public opinion to determine the level of public sentiment on the economic impact of the Covid-19 pandemic and to identify parameters that influence the accuracy of the sentiment analysis classification model. The methods used in this current research are Lexicon, Support Vector Machine (SVM), and Naive Bayes Classifier (NBC). First, Lexicon is used for scoring and labeling the preprocessed data. Second, SVM is used to classify the sentiment, then find the best accuracy using linear, radial, polynomial, and sigmoid kernels. Third, NBC is used to classify sentiment as a comparison method. The results indicated that 255 tweet data consisted of 44 positive tweets (17.25%), 46 neutral tweets (18.04%), and 165 negative tweets (64.71%). Therefore, it can be inferred that the economic impact on the Indonesian people due to the Covid-19 pandemic has a high negative sentiment value. In the performance, SVM yielded a better accuracy of 100%, precision, recall, and F-measure are 1. This study proves that selecting the kernel type and applying underfitting can improve the accuracy of SVM. Also, SVM can perform well on a small amount of training data.
Co-Authors Fauzi, Raffie Rizky Muhammad Asnadi, Nur Qurrotul Aini