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All Journal Journal of Information Technology and Computer Engineering International Journal of Recent Technology and Applied Science (IJORTAS) INOVTEK Polbeng - Seri Informatika
mohamad samuri, suzani
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Intelligent 3D Analysis for Detection and Classification of Breast Cancer mohamad samuri, suzani; Megariani, Try Viananda Nova
JITCE (Journal of Information Technology and Computer Engineering) Vol. 3 No. 02 (2019)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.3.02.96-103.2019

Abstract

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammography has been one of the most reliable methods for early detection of breast carcinomas. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. Breast cancer computer aided diagnosis (CAD) systems can provide such help and they are important and necessary for breast cancer control. Micro calcifications and masses are the two most important indicators of malignancy, and their automated detection is very valuable for early breast cancer diagnosis. Since masses are often indistinguishable from the surrounding parenchymal, automated mass detection and classification is even more challenging. This research presents algorithms for building a classification system or CAD, especially to obtain the different characteristics of mass and micro calcification using association technique based on classification. Starting with an individual-specific deformable of 3D breast model, this modelling framework will be useful for tracking visible tumors between mammogram images, as well as for registering breast images taken from different imaging modalities. From the results, the classifier developed able to perform well by successfully classifying the cancer and non-cancer (normal) images with the accuracy of 97%. Apart from that, by applying color map to the final results of segmentation provides a more interesting display of information and gives more direction to the purpose of image processing, which distinguishes between cancerous and non-cancerous tissues.
Application of Machine Learning for Classifying and Identifying Security Threats Using a Supervised Learning Algorithm Approach Arta, Yudhi; Mohamad Samuri, Suzani; Syafitri, Nesi; Hanafiah, Anggi; Oktaria, Wina; Maripati, Maripati; Pandu Cynthia, Eka
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/aqjdbj22

Abstract

The rapid growth of harmful web content has intensified the demand for intelligent systems capable of accurately classifying cyber threats based on URL patterns. This study evaluates two widely used supervised learning algorithms, Random Forest and Naïve Bayes, for probabilistic classification of multi-class URL datasets. A synthetic dataset comprising 547,775 URLs was designed to reflect realistic threat distribution: benign (65.74%), phishing (14.46%), defacement (14.81%), and malware (4.99%). Each sample included simple structural features such as URL length, number of dots, HTTPS usage, and keyword indicators. Both models were tested using identical stratified train-test splits with varying sample sizes, including focused experiments on 15,000 and 100,000 entries. Results revealed that both models achieved high recall and precision only for the benign class, while failing to detect minority classes. For Random Forest, precision and recall for benign URLs reached 1.00 but dropped to 0.00 for phishing, defacement, and malware in all test scenarios. Naïve Bayes exhibited similar shortcomings, highlighting the impact of class imbalance and limited feature expressiveness. This research concludes that while Random Forest and Naïve Bayes are computationally efficient, they are inadequate for detecting cyber threats without preprocessing techniques such as SMOTE, cost-sensitive learning, or feature enrichment. Future work will explore adaptive hybrid models with contextual features and deep learning frameworks to enhance multi-class detection in real-world cybersecurity scenarios.
Explaining Cholesterol-Related Coronary Artery Disease Risk Using Machine Learning and SHAP Cynthia, Eka Pandu; Mohamad Samuri, Suzani; Shir Li, Wang; Saeed, Alabbas Hussein; Permana, Inggih; Yanto, Febi
International Journal of Recent Technology and Applied Science (IJORTAS) Vol 8 No 1: March 2026
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijortas-0801.920

Abstract

Coronary Artery Disease (CAD) remains a leading cause of global mortality, with dyslipidemia recognized as a major modifiable risk factor. This study investigates the relationship between serum lipid parameters and CAD using the Z-Alizadeh Sani clinical dataset comprising 303 patients with 55 clinical, biochemical, and electrocardiographic attributes. Logistic Regression (LR) and Random Forest (RF) models were developed to predict CAD status, supported by a standardized preprocessing pipeline, multi-split train–test evaluation (70/30, 80/20, 90/10), and performance assessment using Accuracy, Precision, Recall, F1-Score, and AUC-ROC. SHapley Additive exPlanations (SHAP) were employed to enhance model interpretability and quantify the contribution of lipid-related and clinical features to individual predictions. The RF model consistently outperformed LR across all split configurations, achieving a maximum AUC of 0.96, while LR attained an AUC of 0.90. SHAP analysis revealed that total cholesterol (CHOL) and low-density lipoprotein (LDL) were strong positive predictors of CAD, whereas high-density lipoprotein (HDL) exhibited a protective effect, in line with established cardiovascular pathophysiology. These findings demonstrate that integrating explainable machine learning with routine clinical lipid profiles can provide accurate and transparent decision support for early CAD risk stratification.
Co-Authors Eka Pandu Cynthia Eka Pandu Cynthia, Eka Pandu Febi Yanto Hanafiah, Anggi Inggih Permana Maripati, Maripati Megariani, Try Viananda Nova Oktaria, Wina Saeed, Alabbas Hussein Shir Li, Wang Syafitri, Nesi Yudhi Arta, Yudhi