Klasifikasi Penyakit Pneumonia Menggunakan Regresi Logistik, SVM, dan Fitur Deep Learning - Ahmad Bagus Muzakki; Imam Yuadi (Bulletin of Computer Science Research, 2026)
This study evaluates the integration of deep learning-based feature extraction with conventional machine learning algorithms for pneumonia disease classification from chest X-ray images. Two pre-trained models, Inception V3 and SqueezeNet, are used as feature extractors due to their ability to generate effective feature representations from medical images. Inception V3 was chosen because it is able to capture complex visual patterns, while SqueezeNet offers computational efficiency with a smaller number of parameters. Meanwhile, Logistic Regression and Support Vector Machine (SVM) are used as classification algorithms due to their ability to handle high-dimensional data extracted from deep learning features. The dataset used consists of two categories, namely normal images and pneumonia images, with the entire analysis process carried out using Orange Data Mining. The experimental results show that the combination of Inception V3 and SVM provides the best performance with an Area Under Curve (AUC) of 0.731, Classification Accuracy (CA) of 0.835, F1-score of 0.805, Precision of 0.810, Recall of 0.835, and Matthews Correlation Coefficient (MCC) of 0.321. Meanwhile, the combination of SqueezeNet and Logistic Regression produces a CA of 0.771, an F1-score of 0.751, and an MCC of 0.118, showing quite competitive performance although still below Inception V3 and SVM. The results show that the quality of feature embedding generated by the deep learning model has a significant influence on classification performance. The integration of transfer learning and conventional machine learning has been proven to improve the accuracy of pneumonia detection and has the potential to support the development of an efficient and accurate artificial intelligence-based diagnostic system.