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Muhammad Muharrom Al Haromainy
Universitas Pembangunan Nasional "Veteran" Jawa Timur
Computer Science & IT

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Classification Tuberculosis on Chest X-Ray Images Using Backpropagation Neural Network Ananda Ayu Puspitaningrum; Anggraini Puspita Sari; Muhammad Muharrom Al Haromainy
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3197

Abstract

Tuberculosis is an infectious disease that primarily affects the lungs and remains a major health concern due to the difficulty of diagnosis through manual interpretation of chest X-ray images. This study aims to develop an automatic tuberculosis classification system using the Backpropagation Neural Network (BPNN) method to improve diagnostic accuracy. The dataset used in this study was obtained from the Kaggle Tuberculosis (TB) Chest X-ray Dataset, consisting of 7.000 images divided into two classes normal and tuberculosis. The research stages include image preprocessing such as conversion to grayscale, resizing to 256×256 pixels, contrast enhancement using histogram equalization, and noise reduction using a median filter. Experiments were conducted by varying the number of hidden layers 2, 3, and 4 to analyze the effect of network architecture complexity on classification performance. The results showed that the configuration with 2 hidden layers and [100 50] neurons achieved the best performance with an accuracy of 93.57%. The findings indicate that deeper network architectures do not always guarantee higher accuracy and may increase computational load. Overall, this configuration provides an optimal balance between learning capability and accuracy, demonstrating the potential of the BPNN method in supporting early and reliable tuberculosis detection through machine learning based chest X-ray image analysis for clinical decision support.
Performance Evaluation of YOLOv5su and SVM With HOG Features for Student Attendance Face Recognition Achmad Rozy Priambodo; Achmad Junaidi; Muhammad Muharrom Al Haromainy
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3215

Abstract

The rapid evolution of Artificial Intelligence (AI) and Computer Vision has revolutionized conventional attendance systems by introducing automated and intelligent alternatives. Traditional approaches such as manual entry and fingerprint-based systems are often inefficient, error-prone, and unsuitable for large-scale student management. This study evaluates a hybrid face recognition framework that combines You Only Look Once version 5 su, Histogram of Oriented Gradients (HOG), and Support Vector Machine (SVM) to automate student attendance. The YOLOv5su algorithm performs fast and lightweight face detection, while HOG extracts gradient-based facial descriptors classified by SVM. Experiments were conducted using a facial image dataset consisting of 500 original images from 10 classes (50 images per class), which were augmented to 3,500 images with variations in pose, expression, and illumination. The proposed YOLOv5sU–HOG–SVM model achieved 97.1% detection accuracy and 97% recognition accuracy, with mean precision, recall, and F1-score values of 0.98, outperforming conventional CNN-based hybrid models in both accuracy and computational efficiency. These results demonstrate that the combination of YOLOv5su, HOG, and SVM provides a novel balance between detection speed and recognition robustness, making it suitable for real-time academic attendance management. Future work should integrate transformer-based facial feature extraction to further enhance robustness under extreme conditions and larger-scale datasets.
An SMOTE-Optimized MLP Approach for Classification of Diabetes Health Status Ferry Trilaksana Putra; Eva Yulia Puspaningrum; Muhammad Muharrom Al Haromainy
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3340

Abstract

Diabetes mellitus requires accurate classification systems to support early detection and clinical decision-making. Prior research has explored the use of Multilayer Perceptron combined with SMOTE, yet the methodological gap remains in evaluating its effectiveness on multiclass clinical datasets with significant class imbalance, particularly for the Prediabetes category. This study addresses that gap by examining the performance of an MLP model enhanced with SMOTE to improve overall accuracy and minority-class detection. The dataset includes age, gender, blood pressure, random blood glucose, weight, and height as clinical predictors. The preprocessing pipeline consists of label encoding for categorical variables, feature standardization, and the application of SMOTE to balance class distribution. The evaluation follows a consistent 80 10 10 split for training, validation, and testing, with three repeated experimental runs to ensure result stability. On the original imbalanced dataset, the MLP achieved an accuracy of 85 percent and showed limited capability in identifying Prediabetes. After applying SMOTE, accuracy increased to 91 percent, accompanied by notable improvements in recall and F1 score across all health status categories. These results demonstrate that SMOTE enables the model to capture non-linear patterns in minority classes and strengthens overall generalization. The proposed model can be integrated into clinical screening workflows as a decision-support tool. Its predictions can help clinicians identify at-risk individuals earlier, prioritize follow-up actions, and enhance patient management in healthcare settings.
Co-Authors Achmad Junaidi Achmad Rozy Priambodo Ananda Ayu Puspitaningrum Anggraini Puspita Sari Eva Yulia Puspaningrum Ferry Trilaksana Putra