A Comparative Analysis of C4.5 Classification Algorithm, Naïve Bayes and Support Vector Machine Based on Particle Swarm Optimization (PSO) for Heart Disease Prediction

Saputra, Dedi; Irmayani, Windi; Purwaningtias, Deasy; Sidauruk, Juniato

doi:10.25008/ijadis.v2i2.1221

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…The final step is evaluation, where this process aims to measure the accuracy level of the algorithm used to analyze the sentiment of the news [20]. The accuracy value is the percentage of the data set that can be correctly classified by the system, compared to the overall available news data [24].…”

Section: Methodsmentioning

confidence: 99%

A sentiment analysis of madura island tourism news using C4.5 algorithm

Savitri,

Sa’id,

Husni

et al. 2024

J. Soft Comput. Explor.

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Over the past few years, the tourism sector has experienced significant growth in its contribution. The tourism potential on Madura Island is widespread across four regencies, namely Bangkalan, Sampang, Pamekasan, and Sumenep. This potential can be harnessed to support the local government's economy and the communities in the surrounding areas. This research aims to analyze the sentiment of Madura tourism news from online sources using the Decision Tree (C4.5) method. The data used in this study consist of 100 Madura tourism news articles collected from online news portals, which will be classified using the Decision Tree (C4.5) method. The test results show that this method has an average accuracy rate of 76.5% in 10 tests. The average accuracy results demonstrate that the use of the Decision Tree (C4.5) method in this research yields a sufficiently high accuracy level in the sentiment analysis of tourism news.

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Section: Methodsmentioning

confidence: 99%

A sentiment analysis of madura island tourism news using C4.5 algorithm

Savitri,

Sa’id,

Husni

et al. 2024

J. Soft Comput. Explor.

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“…Neural networks, drawing inspiration from the human brain's functioning, exhibit a remarkable ability to capture nonlinear relationships within data, providing a nuanced understanding of students' academic abilities. Conversely, the C4.5 method, based on decision tree algorithms [10], [11], offers transparent classification rules, enhancing interpretability and facilitating informed decision-making [12].…”

Section: Introductionmentioning

confidence: 99%

Classification of Students' Academic Performance Using Neural Network and C4.5 Model

Sulika,

Kusumawati,

Arif

2024

International Journal of Advances in Data and Information Syste

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ducation involves deliberately creating an environment and learning process to empower students to fully utilize their academic and non-academic potential. It encompasses fostering spiritual qualities, religious understanding, self-discipline, cognitive abilities, and skills necessary for personal, societal, national, and state development. Madrasah Aliyah, in particular, emphasizes preparing participants for higher studies in areas of their interest, thereby showcasing their academic prowess. The evaluation of educational models like Neural Networks is crucial for ensuring their effectiveness in problem-solving. This involves testing and assessing the performance of the Neural Network model to ensure its accuracy and reliability. Similarly, the C4.5 method, based on condition data mining, is utilized to measure classification performance by assessing accuracy, precision, and recall. Research findings indicate that the neural network algorithm is more adept at accurately classifying students' academic abilities compared to the C4.5 algorithm. With an accuracy of 92.6% for the neural network algorithm and 80.6% for the C4.5 algorithm, it is evident that the former is more precise in determining the classification of students' academic abilities. This highlights the suitability of the neural network approach for classifying academic abilities in Madrasah Aliyah. Furthermore, the insights gained from this classification process can be extrapolated to benefit other madrasas.

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