Hammad, Jehad A.H
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Journal : Bulletin of Social Informatics Theory and Application

Blocking pornography sites on the internet private and university access ; Irawan, Ninon Oktaviani; Nurfadila, Piska Dwi; Ristanti, Putri Yuni; Hammad, Jehad A.H
Bulletin of Social Informatics Theory and Application Vol. 3 No. 1 (2019)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v3i1.161

Abstract

Promoting Covid-19 vaccination with Instagram Fahmi, Ahmad Maulanal; Azizah, Hanifah Nur; Mahendra , I Putu Arda; Fatkhiatin , Irma; Hammad, Jehad A.H
Bulletin of Social Informatics Theory and Application Vol. 5 No. 1 (2021)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v5i1.408

Abstract

The COVID-19 Vaccination Program is essential information during this pandemic. Information about COVID-19 vaccination is disseminated through social media, one of which is Instagram. During the COVID-19 pandemic, the Ministry of Health's Instagram accounts in various countries provided much information about COVID-19, including vaccinations. This research was made to determine the role of Instagram as a media to promote the invitation to vaccinate for COVID-19 by using the calculation of engagement rates in each post category on the Instagram account of the Ministry of Health of Indonesia, Malaysia, the United States, and Australia. The results obtained in this study are that these accounts have implemented the right strategy in COVID-19 vaccination, and several strategic refinements need to be done.
Ransomware detection: patterns, algorithms, and defense strategies Amro, Manar Y; Dwieb, Mohamed; Hammad, Jehad A.H; Wibawa, Aji Prasetya
Bulletin of Social Informatics Theory and Application Vol. 8 No. 1 (2024)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v8i1.689

Abstract

In the contemporary digital landscape, rapid technological advancements present unprecedented challenges for developers in the hardware and software realms. The ubiquitous presence of the Internet, the Internet of Things (IoT), and widespread digital solutions bring numerous benefits and escalating risks. This study investigates the pervasive threat of ransomware attacks, a daily menace that imperils the operational and security dimensions of the digital sphere for enterprises and individuals. The research objective is to identify the most effective algorithm for detecting ransomware viruses, a persistent and evolving threat that significantly challenges institutions, companies, and governmental organizations. The dynamic nature of ransomware necessitates robust detection mechanisms to safeguard sensitive data. To achieve this goal, we conducted a comparative analysis of four prominent algorithms recognized for their efficacy in combating and detecting viruses. Emphasis was placed on the algorithm exhibiting the most promising results. A detailed examination of its impact on existing data involved comprehensive analysis and a comparative assessment against previous studies. Results, derived from extensive studies and experiments on a diverse dataset, illuminate the critical role of ransomware detection algorithms and underscore their effectiveness. The findings contribute valuable insights to the ongoing discourse on cybersecurity strategies, providing a foundation for enhanced ransomware defense measures.