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

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Classification of Diabetes Mellitus Sufferers Eating Patterns Using K-Nearest Neighbors, Naïve Bayes and Decission Tree Lubis, Ayuni Fachrunisa; Haq, Hilmi Zalnel; Lestari, Indah; Iltizam, Muhammad; Samae, Nitasnim; Rofiqi, Muhammad Aufi; Abdurrahman, Sakhi Hasan; Tambusai, Balqis Hamasatiy; Salsilah, Puja Khalwa
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.v2i1.1103

Abstract

The study investigates three classification algorithms, namely K-Nearest Neighbor (K-NN), Naïve Bayes, and Decision Tree, for the classification of Diabetes Mellitus using a dataset from Kaggle. K-NN relies on distance calculations between test and training data, using the Euclidean distance formula. The choice of k, representing the nearest neighbor, significantly influences K-NN's effectiveness. Naïve Bayes, a probabilistic method, predicts class probabilities based on past events, and it employs the Gaussian distribution method for continuous data. Decision Trees, form prediction models with easily implementable rules. Data collection involves obtaining a Diabetes Mellitus dataset with eight attributes. Data preprocessing includes cleaning and normalization to minimize inconsistencies and incomplete data. The classification algorithms are applied using the Rapidminer tool, and the results are compared for accuracy. Naïve Bayes yields 77.34% accuracy, K-NN performance depends on the chosen k value, and Decision Trees generate rules for classification. The study provides insights into the strengths and weaknesses of each algorithm for diabetes classification
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).

Show Abstract | Download Original | Original Source | Check in Google Scholar

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 Haiban, Ryando Rama Haq, Hilmi Zalnel Iltizam, Muhammad Indah Lestari, Indah Ismail, Muhammad Ibrara Lubis, Ayuni Fachrunisa Melyani, Nasya Amirah Muhammad Habibullah Rofiqi, Muhammad Aufi Salsilah, Puja Khalwa Shahid, Bilal Tambusai, Balqis Hamasatiy Tatamara, Aghnia