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All Journal IJID (International Journal on Informatics for Development) Proceedings of The ICECRS
Lestari, Lusiana
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Computer Science & IT Education Languange, Linguistic, Communication & Media Mathematics

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Protecting Cyber Crime Victims: Trends and Imperatives in Legal Safeguards Lestari, Lusiana; Rosnawati, Emy; Fidayanti, Anita Rohma
Proceedings of The ICECRS Vol 12 No 1 (2023): Proceedings of Data in Education, Culture, and Interdisciplinary Studies
Publisher : International Consortium of Education and Culture Research Studies

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/icecrs.v12i2.1701

Abstract

This data article presents an analysis of 10,446 scholarly articles on Crime Victims within Cyber Law sourced from Lens.org. Using rigorous filtering, the dataset unveils key contributors and trends, notably a surge in publications in 2017 and the dominance of the Law field. The findings emphasize the pressing need for enhanced legal safeguards to protect Crime Victims in cyberspace, serving as a foundation for policy reform and further academic inquiry. Highlights : Surge in Publications: Sharp increase in articles in 2017 highlights growing attention to cyber law and crime victimization. Dominance of Law Field: Emphasis on legal aspects underscores the need for legal frameworks in addressing cybercrime against victims. Call for Action: Findings stress the urgency for improved safeguards and policy reforms to protect victims in cyberspace. Keywords: Crime Victims, Cyber Law, Legal Safeguards, Policy Reform, Academic Inquiry
Veil and Hijab: Twitter Sentiment Analysis Perspective Lestari, Lusiana; R Wahyudi, M Didik; Kiftiyani, Usfita
IJID (International Journal on Informatics for Development) Vol. 9 No. 1 (2020): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2020.09108

Abstract

Controversies about veil and hijab are often occur in society. Especially in today’s digital era, public opinion expressed through social media can greatly influence the others opinions, regardless of whether it is positive or negative. Therefore, this research was aiming to conduct an approach through analysis sentiment of public opinion about the veil and hijab to know how much accurate the sentiment analysis predict the positive, negative, or other sentiments with using Twitter data as the research object. The algorithm used in this study is Support Vector Machine (SVM) because of its fairly good classification model though it trained using small set of data. The SVM on this research was combined with Radial Base Function (RBF) kernel because of its numerical difficulties that are fewer than linear and polynomial kernel and also because this research doesn’t have a large feature.  The amount of data used is 3556 tweets data. Tweets data, which is numbered 1056, is classified manually for the learning process. The remaining 2500 data will be classified automatically with the classifier model that has been created. A total of 1056 tweets data that have been classified manually is separated into training and testing data with a ratio of 8: 2. The result of the sentiment analysis process using Support Vector Machine algorithm RBF kernel with C=1 and γ=1  has an accuracy score of 73.6% with precision to negative opinions are 62%, positive opinions are 83%, neutral opinions reach 53% and irrelevant opinions that talk about hijab and veil reach 98%. It shows that sentiment analysis can be used for predicting the negative, positive or other sentiments of a sentence based on a certain topic, in this case veil and hijab.
Co-Authors Emy Rosnawati Fidayanti, Anita Rohma Kiftiyani, Usfita R Wahyudi, M Didik