Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
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This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics Engineering, Mathematics and Computer Science Journal (EMACS)
Nadia Nadia
Bina Nusantara University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Gamification for Increasing Learning Motivation of College Student Agnes Kurniati; Francisco Maruli Panggabean; Nadia Nadia; Thomas Galih Satria
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 3 No. 1 (2021): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v3i1.6843

Abstract

The purpose of this research is to build an attractive gamification system that could motivate college students as the user target by customized challenges with adjustable difficulty-scaled reward systems. The methods used for the research consists of problem identification, collecting and analyzing the data, problem formulation, solution and design creation, product implementation, and evaluation. StudyGO is a mobile application that has 2 main features that have gamification aspects which are focused and scheduled study. The application evaluation is done with questionnaire evaluation based on 5 measurable human factors. The majority results from the application feels very motivated and rewarded enough from this gamification system.
Identifying clickbait in online news using deep learning Andry Chowanda; Nadia Nadia; Lie Maximilianus Maria Kolbe
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4444

Abstract

Several industries use clickbait techniques as their strategy to increase the number of readers for their news. Some news companies implement catchy headlines and images in their news article links, with the expectation that the readers will be interested in reading the news and click the provided link. The majority of the news is not hoax news. However, the content might not be as grand as the catchy headlines and images provided to the readers. This research aims to explore the classification model using machine learning to identify if the headlines are classified as clickbait in online news. This research explores several machine learning techniques to classify clickbait in online news and comprehensively explain the results. Several popular machine learning techniques were implemented and explored in this research. The results demonstrate that the model trained with fast large margin provides the best accuracy and classification error (90% and 10%, respectively). Moreover, to improve the performance, bidirectional encoder representations from transformers architecture was used to model clickbait in online news. The best BERT model achieved 98.86% in the test accuracy. BERT model requires more time to train (0.9 hour) compared to machine learning (0.4 hour).
Phishing Detection Applications for Website and Domain at Browser using Virustototal API Nadia Nadia; Wellson Leewando; Javier Paulus; Valentino Nooril
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 5 No. 2 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i2.9998

Abstract

The purpose of this research is to create a browser extension-based application that can detect malicious sites to minimize phishing attacks. The research method used is to conduct a literature study and collect data from the questionnaire results. Research testing methods are blackbox testing, performance testing using 100 URL with precision and recall method, and comparison between two other simillar applications. The results of this study indicate that this application has good functionality and can reduce phishing attacks on users. The conclusion that can be drawn from this research is that the malicious site detection feature in browser extensions can enhance user protection from phishing attacks
Co-Authors Agnes Kurniati Andry Chowanda Francisco Maruli Panggabean Javier Paulus Lie Maximilianus Maria Kolbe Thomas Galih Satria Valentino Nooril Wellson Leewando