Fraud in digital transactions has become a serious issue threatening the security and integrity of the fintech and e-commerce sectors. To address this problem, machine learning technology has emerged as an effective solution for automatically detecting anomalies and fraudulent transactions. This study aims to analyze the application of machine learning algorithms, specifically Support Vector Machine (SVM), Random Forest, and Ensemble Learning, in detecting fraud in digital transactions. The research adopts a quantitative approach with experimentation, testing the effectiveness of the three algorithms using a digital transaction dataset consisting of both fraudulent and non-fraudulent transactions. The results show that the Random Forest algorithm performs the best in terms of accuracy and recall, followed by Ensemble Learning, which enhances fraud detection performance by combining multiple prediction models. Meanwhile, SVM demonstrates satisfactory performance but is prone to overfitting issues when handling large and complex datasets. The study also finds that the problem of imbalanced data can affect model accuracy, and data balancing techniques such as oversampling are required to improve fraud detection performance. Overall, the findings suggest that machine learning, particularly Random Forest and Ensemble Learning algorithms, can be relied upon to improve fraud detection in digital transactions. However, challenges such as model interpretability and the need for periodic algorithm updates still need to be addressed to enhance the effectiveness of fraud prevention systems in countering the ever-evolving nature of fraud.

In  recent  years,  air  quality  has  become  an  increasingly  important  issue  in  various  cities  around  the  world.  Real-time  air  quality  monitoring  is  essential  for  identifying  pollution  problems  and  taking  appropriate  actions.  This  article  discusses  the  design  of  an  Internet  of  Things  (IoT)-based  air  quality  monitoring  system  using  the  prototype  method.  The  system  is  designed  to  monitor  air  quality  parameters  such  as  PM2.5,  PM10,  CO2,  and  temperature  in  real-time  and  present  the  data  to  users  through  a  web-based  application.