2 research outputs found
Deteksi Email Spam dengan Continuous Bag-Of-Words dan Random Forest
No full textSpam email poses a significant cyber threat, as scammers employ various tactics to deceive individuals into divulging sensitive information or downloading harmful content. For instance, in June 2023, Indonesia encountered approximately 6.51 thousand spam attacks, underscoring the widespread nature of this issue. These attacks frequently involve deceptive strategies, such as impersonation or false promises of rewards, to ensnare unsuspecting victims. Succumbing to spam can result in financial losses and other grave repercussions. To address this concern, this research addresses this pressing problem by focusing on email content classification to detect phishing attempts. The proposed solution leverages runtime platforms such as Google Colab and uses Continuous Bag of Words (CBOW) analysis and Random Forest methods. CBOW is selected for its effectiveness in capturing semantic relationships between words, allowing the model to extract meaningful features from the email content. Random Forest, on the other hand, is chosen for its ability to handle imbalanced datasets commonly encountered in email classification tasks, ensuring fair representation of both spam and ham emails during model training. By combining these two techniques, we aim to develop a robust classification model capable of accurately distinguishing between phishing (spam) and legitimate (ham) emails, thus enhancing email security measures. Through our approach, we aim to classify the SpamAssassin dataset into ham or spam categories, with an anticipated precision rate of 0.98, demonstrating the model's effectiveness in accurately identifying phishing emails
Deblurring Photos with Lucy-Richardson and Wiener Filter Algorithm in RGBA Color
No full textPhotographers and social media influencers encounter challenges with hand tremors during photo capture, leading to unintended blurriness in their posts, reducing visual impact and audience engagement. To mitigate this problem, the authors aim to effectively reduce the blurring caused by instability in handling, producing sharper and noise-free photos. The methodology involves implementing the Lucy-Richardson and Wiener Filter algorithms into a Python-based web application optimized for RGBA photo processing. Data requirements include sample photos affected by hand tremors to validate the efficacy of the solution. The outcome successfully eliminates blur in captured photos affected by hand tremors in RGBA color format
