International Journal of Informatics and Computing

Custom Concept Text-to-Image Using Stable Diffusion Model in Generative Artificial Intelligence

2024-06-26T11:33:42+00:00

The ability of algorithms to produce content that closely mimics human work has revolutionized several fields thanks to generative artificial intelligence, or Gen AI. However, these developments also raise questions about generative models' transparency, predictability, and behavior. Considering the relevance of this topic and the expanding influence of AI on society, research into it is imperative. This paper aims to empirically explore the nuances of behavior in the setting of discriminative generative AI, using the stable diffusion model as an example. We will be better equipped to handle obstacles and guarantee the ethical and responsible application of generative AI in a world that is changing quickly if we have a deeper grasp of this phenomenon. The research method is carried out in several stages, such as dataset collection, modeling, testing, and analysis of results. The research results show that generative artificial intelligence can create realistic images like the original. However, there are still several challenges, including the availability of a reasonably large dataset for training data and high and long computing times. Likewise, the results of the Fréchet Inception Distance (FID) test were still quite large, namely 1284.4430, which shows that the quality of this model is still not good.

Sentiment Analysis of Application X on The Impact of Social Media Content on Adolescent Mental Well-Being using Naïve Bayes Algorithm

2024-07-24T15:53:36+00:00

Since the pandemic, the use of social media has increased significantly. However, its presence has raised significant concerns about its impact on the mental well-being of teenagers. The pervasive influence of social media has led to substantial changes in the social system within society. Despite this influence, there is currently no comprehensive understanding of the specific impact of social media on mental health. To address this gap, this research proposes the use of sentiment analysis of social media posts with the Naive Bayes algorithm as an approach to identify and classify positive and negative sentiments in these posts related to the mental well-being of teenagers. This solution aims to provide a deeper understanding of the impact of social media content on this vulnerable demographic. In this study, a total of 555,361 social media posts were successfully collected and analyzed using the Naive Bayes algorithm, which was trained with a sample of 27,977 test data. The research results demonstrate that sentiment analysis with the Naive Bayes algorithm is effective in classifying social media sentiment, with 50.55% of the posts classified as positive and 46.97% classified as negative. The identified sentiment patterns have provided valuable insights into the positive and negative impact of social media content on the mental well-being of teenagers.

Advanced Phishing Attack Detection Through Network Forensic Methods and Incident Response Planning Based on Machine Learning

2024-02-21T23:25:19+00:00

The widespread use of smartphones has led to an increase in cybercrimes, particularly phishing attacks. Phishing attacks are commonly propagated through email, WhatsApp groups, and other communication channels. The stolen data is then used to commit further crimes, exploiting the victims' personal information. This study addresses the detection of phishing attacks using network forensic methods and incident response planning. Unlike previous approaches that relied solely on Incident Response Plans (IRPs) and Incident Handling methods to react to phishing attacks, this research emphasizes proactive detection. By employing network forensics, suspicious websites can be identified and differentiated from legitimate ones, enabling early detection and prevention of phishing attacks. The results demonstrate that network forensics can significantly enhance the ability to detect phishing sites before they can harm users. In our experiments, we analyzed a dataset of 10,000 websites, identifying 95% of phishing sites with a false positive rate of only 2%. Utilizing the Random Forest machine learning algorithm, we achieved high performance metrics with an accuracy of 96.5%, precision of 97.1%, recall of 95.8%, and an F1-score of 96.4%. This proactive approach not only mitigates the risk of phishing but also provides a robust framework for incident response, ensuring that potential threats are identified and neutralized promptly.

Optimizing Data Management in Web Applications through Google Drive API Integration and Synchronization

2024-09-17T05:43:43+00:00

The rise of Web-based applications has created a demand for streamlined data management and automatic data synchronization. Even manually stored local data is often insufficient to meet these requirements, necessitating a solution that can efficiently manage data access and storage through Cloud technology. This study advocates for utilizing the Google Drive API to resolve these issues. By leveraging the benefits of Google Drive's Cloud storage, Web applications can seamlessly synchronize user-uploaded data to the Cloud. To initiate this integration, a Google account is required to authenticate the process and serve as a mediator for data exchange. This approach employs secure authentication and authorization mechanisms to ensure data privacy. The system is developed using an iteration-based approach starting with user requirements analysis, followed by interface design and API integration. Pilot tests were then conducted to validate system performance under various usage scenarios. The findings revealed a noteworthy advancement in the synchronization and administration of data through the Web-based application with a data transmission duration of under 60 seconds, contingent on internet speed. Google Drive's API integration enables users to access files and manage them in real-time, surpassing prior limitations. To meet the demands of progressively intricate Web-based applications, future research can concentrate on enhancing data security and optimizing performance.