<![CDATA[Pneumonia is an acute lung infection and remains one of the leading causes of mortality among children under five in Indonesia. Pneumonia is commonly diagnosed through the analysis of chest X-ray (CXR) images by radiologists; however, this process is still subject to human variability and the limited availability of medical specialists. This study aims to develop a Convolutional Neural Network (CNN)-based pneumonia detection system by comparing four model variants: CNN with Color Jitter (Model 1), CNN with Color Jitter and Layer Augmentation (Model 2), CNN with Batch Normalization and Color Jitter (Model 3), and EfficientNet-B0 transfer learning (Model 4). The experiments were conducted using the Chest X-Ray Images (Pneumonia) dataset from Kaggle, comprising 5,856 images, which were divided into 70% for training, 15% for validation, and 15% for testing. The experimental results demonstrate that the EfficientNet-B0 transfer learning model achieved the best performance, with an accuracy of 96.92%, precision of 97.67%, recall of 98.13%, and an F1-score of 97.90%. This model improved classification accuracy by 1.93% compared with the conventional CNN baseline (Model 1), which achieved an accuracy of 94.99%. Furthermore, the incorporation of Layer Augmentation in Model 2 effectively reduced the number of false negatives from 21 to 10 cases. These findings demonstrate the effectiveness of EfficientNet-B0 transfer learning for medical image classification and highlight its potential as a clinical decision support tool for pneumonia diagnosis in healthcare settings, rather than as a replacement for clinical diagnosis performed by qualified medical professionals.]]>
