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

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Implementation of K-Nearest Neighbors, Naïve Bayes Classifier, Support Vector Machine and Decision Tree Algorithms for Obesity Risk Prediction Putri, Amanda Iksanul; Husna, Nur Alfa; Cia, Neha Mella; Arba, Muhammad Abdillah; Aisyi, Nasywa Rihadatul; Pramesthi, Chintya Harum; Irdayusman, Abidaharbya Salsa
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.v2i1.1110

Abstract

An abnormal or excessive build-up of fat that can negatively impact one's health as a result of an imbalance in energy between calories consumed and burnt is known as obesity. The majority of ailments, such as diabetes, heart disease, cancer, osteoarthritis, chronic renal disease, stroke, hypertension, and other fatal conditions, are linked to obesity. Information technology has therefore been the subject of several studies aimed at diagnosing and treating obesity. Because there is a wealth of information on obesity, data mining techniques such as the K-Nearest Neighbors (K-NN) algorithm, Naïve Bayes Classifier, Support Vector Machine (SVM), and Decision Tree can be used to classify the data. The 2111 records and 17 characteristics of obesity data that were received from Kaggle will be used in this study. The four algorithms are to be compared in this study. In other words, using the dataset used in this study, the Decision Tree algorithm's accuracy outperforms that of the other three algorithms K-NN, Naïve Bayes, and SVM. Using the Decision Tree algorithm, the accuracy was 84.98%; the K-NN algorithm came in second with an accuracy value of 83.55%; the Naïve Bayes algorithm came in third with an accuracy rate of 77.48%; and the SVM algorithm came in last with the lowest accuracy value in this study, at 77.32%.
Implementation of Deep Learning for Brain Tumor Classification from Magnetic Resonance Imaging Husna, Nur Alfa; Hendri, Desvita; Wajdi, Muhammad Farid; Ginting, Ella Silvana; Pramesthi, Chintya Harum
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

Brain tumours are a medical problem that causes many people to die in the world due to brain cancer. Brain tumours are one of the dangerous types of brain cancer. MRI is well proven in the assessment of brain tumours, although conventional imaging has limitations in evaluating the extent of the tumour. In the field of medicine, there has been an increase in large amounts of data and traditional models cannot manage such data efficiently. So there is a need for medical image analysis that can store and analyse large medical data efficiently. This research will adopt a deep understanding transfer learning approach with four models namely VGG16, VGG19, MobileNetV2 and ResNet50 to classify 2 types of image shapes that detect whether a person has a brain tumour or not using Magnetic Resonance Imaging (MRI) data with Convolution Neutral Network (CNN). The number of datasets used is 4600 MRI images with 2 classes namely Brain Tumour and Health. The hyperparameters used are image size 224x224 pixels, training data ratio 70%, test data 30%, using Adam optimizer, learning rate 0.0001, using batch size 64 and epoch value 50. The best results in this study were obtained by MobileNetV2 architecture with an accuracy of 88.77%.
Co-Authors Aisyi, Nasywa Rihadatul Arba, Muhammad Abdillah Cia, Neha Mella Ginting, Ella Silvana Hendri, Desvita Irdayusman, Abidaharbya Salsa Pramesthi, Chintya Harum Putri, Amanda Iksanul Wajdi, Muhammad Farid