Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Media Computer Science
Muhammad Rizki Akbar
Universitas Muhammadiyah Bengkulu
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Analysis Of Artificial Intelligence Deepfake Risks Regarding The Public's Ability To Distinguish Between Real And AI-Generated Content Using A Quantitative Descriptive Method Muhammad Rizki Akbar; Pahrizal Pahrizal; Yulia Darmi; Rozali Toyib
Jurnal Media Computer Science Vol 5 No 4 (2026): Oktober
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i4.12713

Abstract

The development of Artificial Intelligence (AI) technology has given rise to Deepfake technology capable of producing visual content resembling reality, creating a risk of disinformation and making it difficult for the public to distinguish real from AI-generated content. This study aims to analyze the public's ability to distinguish such content and the relationship between the public's knowledge of Deepfake AI (X1) and their ability to detect Deepfake content (Y1). This study uses a quantitative descriptive method with 100 social media user respondents aged 17-30 years, collected online via Google Forms from June 24 to July 24, 2026. The instrument consisted of five Likert-scale statements measuring X1 and ten pairs of real-Deepfake photos measuring Y1. Results show that detection ability is high (average 77.1%) and knowledge level is also high (average 3.87 on a 1-5 scale). Both instruments were valid and reliable (Cronbach's Alpha for X1 = 0.834; KR-20 for Y1 = 0.712). A Pearson correlation test showed no significant relationship between X1 and Y1 (r = -0.028; p = 0.784 > 0.05), confirmed by simple linear regression (R Square = 0.001), so the null hypothesis (H0) is accepted; conceptual knowledge was shown not to automatically improve actual detection ability. This study recommends improving digital literacy, AI content watermarking regulations, and refining the detection instrument in future research.
Co-Authors Pahrizal, Pahrizal Toyib, Rozali Yulia Darmi