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Bintang Grendis, Nuraqilla Waidha
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Computer Science & IT

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Classification of Heart (Cardiovascular) Disease using the SVM Method Abidin, Minhajul; Munzir, Misbahul; Imantoyo, Adi; Bintang Grendis, Nuraqilla Waidha; Hadi San, Ahmad Syahrul; Mostfa, Ahmed A.; Furizal, Furizal; Sharkawy, Abdel-Nasser
Indonesian Journal of Modern Science and Technology Vol. 1 No. 1 (2025): January
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/ijmst.1.1.9-15.2025

Abstract

Cardiovascular disease is one of the leading causes of death worldwide, with a high mortality rate, especially in developing countries like Indonesia. This highlights the importance of developing systems to identify and detect heart disease at an early stage. In this study, the Support Vector Machine (SVM) algorithm was used to classify cardiovascular diseases by utilizing a dataset consisting of 303 patient records obtained from Kaggle. The dataset was divided into 70% for training and 30% for testing. Before optimization using GridSearchCV, the SVM model achieved an accuracy of 79%, precision of 79%, recall of 73%, and F1-score of 76%. However, after adjusting the hyperparameters with GridSearchCV, the model's accuracy slightly decreased to 77%, with precision remaining at 79%, recall dropping to 66%, and F1-score at 72%. Despite this decline in performance after optimization, the results indicate that although SVM has potential for classifying heart disease, its performance is highly influenced by data quality and the selection of appropriate hyperparameters. Even with the performance decrease postoptimization, the model still provides useful predictions, showing consistent results and a proportional class distribution.
An Analysis of The C4.5 Decision Tree Algorithm Method Applied to The Play Tennis Dataset and Manual Calculation Approach Abidin, Minhajul; Aufa, M. Hikari; Saputra, M. Ilham Cahyo; Oyeyemi, Babatunde Bamidele; Bintang Grendis, Nuraqilla Waidha
Indonesian Journal of Modern Science and Technology Vol. 1 No. 2 (2025): May
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/ijmst.1.2.65-70.2025

Abstract

This study explores the use of the C4.5 decision tree algorithm on the Play Tennis dataset through two approaches: manual calculations and a Python-based program. As an improved version of the ID3 algorithm, C4.5 is capable of managing both categorical and numerical inputs, dealing with missing data, and utilizing entropy and information gain to determine the most important features. The dataset contains 14 entries with attributes such as Outlook, Temperature, Humidity, Windy, and the target variable PlayTennis. Entropy and information gain were calculated manually to construct the decision tree in a step-by-step manner. The resulting tree was then compared with one generated using Python tools like Pandas, NumPy, and Scikit-learn. Both trees were identical, confirming the accuracy of the method. A comparison with previous research highlights the flexibility and clarity of decision tree algorithms, making them suitable for various fields such as healthcare, finance, privacy-conscious machine learning, and materials science. These findings support the real-world usefulness of such algorithms. Overall, the study finds that C4.5 is highly effective for small classification problems and shows promise for use in larger, more complex datasets. Additionally, this research supports deeper learning of how decision tree algorithms work, making it a helpful reference for both educational and applied data science contexts.
Co-Authors Abidin, Minhajul Aufa, M. Hikari Furizal, Furizal Hadi San, Ahmad Syahrul Imantoyo, Adi Mostfa, Ahmed A. Munzir, Misbahul Oyeyemi, Babatunde Bamidele Saputra, M. Ilham Cahyo Sharkawy, Abdel-Nasser