Commit Journal

📃 Featured Article, CommIT (Communication and Information Technology) Journal 📃

Classification of Deepfake Images Using a Novel Explanatory Hybrid Model

Abstract
In court, criminal investigations and identity management tools, like check-in and payment logins, face videos, and photos, are used as evidence more frequently. Although deeply falsified information may be found using deep learning classifiers, block-box decisionmaking makes forensic investigation in criminal trials more challenging. Therefore, the research suggests a three-step classification technique to classify the deceptive deepfake image content. The research examines the visual assessments of an EfficientNet and Shifted Window Transformer (SWinT) hybrid model based on Convolutional Neural Network (CNN) and Transformer architectures. The classifier generality is improved in the first stage using a different augmentation. Then, the hybrid model is developed in the second step by combining the EfficientNet and Shifted Window Transformer architectures. Next, the GradCAM approach for assessing human understanding demonstrates deepfake visual interpretation. In 14,204 images for the validation set, there are 7,096 fake photos and 7,108 real images. In contrast to focusing only on a few discrete face parts, the research shows that the entire deepfake image should be investigated. On a custom dataset of real, Generative Adversarial Networks (GAN)-generated, and human-altered web photos, the proposed method achieves an accuracy of 98.45%, a recall of 99.12%, and a loss of 0.11125. The proposed method successfully distinguishes between real and manipulated images. Moreover, the presented approach can assist investigators in clarifying the composition of the artificially produced material.

Keywords: Image Classification, Deepfake Images, Explanatory Artificial Intelligence, Hybrid Model

Read full article: https://journal.binus.ac.id/index.php/commit/article/view/8761

📝Featured Article, CommIT (Communication and Information Technology) Journal 📝

Yohan Adhi Styoutomo, Yova Ruldeviyani
University of Indonesia

Abstract
XYZ financial institution is a government institution that receives and processes transaction reports from banks and remittances, so its data classification is very confidential. However, during the Work from Home (WFH) policy in the Covid-19 pandemic, XYZ financial institution has received many spam/phishing attacks. Hence, this incident shows that some employees need an awareness of information security. The research offers a different Information Security Awareness (ISA) questionnaire using the Human Aspects of the Information Security Questionnaire (HAIS-Q) and ISO/IEC 27001:2013 as focus areas. The research uses the theory of Knowledge, Attitude, and Behavior (KAB) to determine the dimensions that need improvement and priority ranking using Fuzzy Analytical Hierarchy Process (FAHP). Furthermore, the research conducts a Focus Group Discussion (FGD) to explore the root causes of employee behavior. The FGD results show that there are still employees who do not know about information security, such as password combinations and length, so limited knowledge affects employees’ attitudes and behaviors. The research results from 34 respondents show that the employees’ information security awareness level is in the moderate category (78.8%). They still need to increase their awareness of information security, especially in managing passwords, using email and the Internet, and reporting incidents. Recommendations have been prepared to improve the dimensions and areas that have yet to be categorized as good. In the future, the ISA questionnaire is expected to be used in other organizations.

Keywords: Information Security Awareness, , Fuzzy Analytical Hierarchy Process (FAHP), Human Aspects of the Information Security Questionnaire (HAIS-Q), ISO/IEC 27001:2013

Read full article: https://journal.binus.ac.id/index.php/commit/article/view/8272

The Determinant Factors of Shopping Cart Abandonment Among E-commerce Customers in Indonesia

Abstract
Predicting the non-purchase behavior of potential customers, such as the abandonment of online shopping carts, is a pivotal factor in determining the success of companies. Despite several conducted studies, further investigation is still required to gain a profound understanding of the underlying causes of these phenomena. The research aims to analyze the motivating factors behind shopping cart abandonment among ecommerce customers in Indonesia using a quantitative method. Furthermore, the population size is undefined, and the sample consists of 200 respondents selected through purposive sampling. The sample size is determined by five times the indicator number. The data analysis is conducted using Structural Equation Modeling (SEM) through SmartPLS 4.0.8.5, and the Coefficient of determination (R2) value for shopping cart abandonment is found to be 37.5%. The results show that complicated checkout, information overload, complicated policies, and limited shipping options positively impact shopping cart abandonment. Complicated checkout emerges as the most significant variable. Meanwhile, perceived cost and emotional ambivalence have no impact. The research also provides theoretical contributions and suggests future research for e-commerce companies and merchants. The theoretical contribution is how user emotions, user experience, merchant policies, and e-commerce regulation affect shopping cart abandonment. From the practical implications, e-commerce companies should focus on the user experience during checkout to reduce shopping cart abandonment.

Keyword: Shopping Cart Abandonment, E-commerce Customers, Indonesia

Read full article: https://journal.binus.ac.id/index.php/commit/article/view/9308
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