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All Journal PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Melyani, Nasya Amirah
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Performance Comparison of Random Forest, Support Vector Machine and Neural Network in Health Classification of Stroke Patients Sari, Windy Junita; Melyani, Nasya Amirah; Arrazak, Fadlan; Anahar, Muhammad Asyraf Bin; Addini, Ezza; Al-Sawaff, Zaid Husham; Manickam, Selvakumar
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 1: PREDATECS July 2024
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i1.1119

Abstract

Stroke is the second most common cause of death globally, making up about 11% of all deaths from health-related deaths each year, the condition varies from mild to severe, with the potential for permanent or temporary damage, caused by non-traumatic cerebral circulatory disorders. This research began with data understanding through the acquisition of a stroke patient health dataset from Kaggle, consisting of 5110 records. The pre-processing stage involved transforming the data to optimize processing, converting numeric attributes to nominal, and preparing training and test data. The focus then shifted to stroke disease classification using Random Forest, Support Vector Machines, and Neural Networks algorithms. Data processing results from the Kaggle dataset showed high performance, with Random Forest achieving 98.58% accuracy, SVM 94.11%, and Neural Network 95.72%. Although SVM has the highest recall (99.41%), while Random Forest and ANN have high but slightly lower recall rates, 98.58% and 95.72% respectively. Model selection depends on the needs of the application, either focusing on precision, recall, or a balance of both. This research contributes to further understanding of stroke diagnosis and introduces new potential for classifying the disease.
Analysis Comparison Classification Image Disease Eye Using the CNN Algorithm, Inception V3, DenseNet 121 and MobileNet V2 Architecture Models Melyani, Nasya Amirah; Lubis, Ayuni Fachrunisa; Tatamara, Aghnia; Haiban, Ryando Rama; Iltizam, Muhammad; Rofiqi, Muhammad Aufi; Abdurrahman, Sakhi Hasan; Samae, Nitasnim; Shahid, Bilal; Habibullah, Muhammad; Ismail, Muhammad Ibrara
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 3 No. 1: PREDATECS July 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

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Abstract

Eye disease is a significant global health problem, with more than two billion people experiencing vision impairment. Some of the main causes of visual impairment include cataracts, glaucoma, diabetic retinopathy, and age-related macular degeneration. Early detection of eye disease is very important to prevent blindness. The fundus of the eye, which includes the retina and blood vessels, is an important area in the diagnosis of retinal diseases. Fundus disease can cause significant vision loss and is one of the leading causes of blindness. Automated analysis of fundus images is used to diagnose common retinal diseases, ranging from easily treatable to very complex conditions. This research discusses eye disease image classification using several Convolutional Neural Network (CNN) architectures, namely Inception V3, DenseNet 121, and MobileNet V2. The dataset used is 4217 fundus images categorized based on the patient's health condition. Data is processed through normalization and augmentation to improve model performance. Experimental results show that MobileNet V2 has the highest accuracy of 81.3%, followed by Inception V3 with 77.3%, and DenseNet 121 with 76.7%. The use of appropriate CNN models in the classification of eye fundus images can help in early detection of eye diseases, thereby preventing further visual impairment.
Co-Authors Abdurrahman, Sakhi Hasan Addini, Ezza Al-Sawaff, Zaid Husham Anahar, Muhammad Asyraf Bin Arrazak, Fadlan Haiban, Ryando Rama Iltizam, Muhammad Ismail, Muhammad Ibrara Lubis, Ayuni Fachrunisa Manickam, Selvakumar Muhammad Habibullah Rofiqi, Muhammad Aufi Samae, Nitasnim Sari, Windy Junita Shahid, Bilal Tatamara, Aghnia