Classification of Thyroid Class using ID3 Algorithm and Artificial Neural Network (ANN)
Thyroid disease refers to a range of conditions or issues affecting the thyroid gland. This gland, located below the Adam’s apple, is responsible for coordinating various metabolic processes in the body, making its function essential. Early detection of thyroid symptoms is crucial as an initial step...
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| Format: | Article |
| Language: | Indonesian |
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Islamic University of Indragiri
2025-01-01
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| Series: | Sistemasi: Jurnal Sistem Informasi |
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| Online Access: | https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/3440 |
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| author | Nabila Henisaniyya Citra Pertiwi Anita Desiani Ali Amran Muhammad Arhami |
| author_facet | Nabila Henisaniyya Citra Pertiwi Anita Desiani Ali Amran Muhammad Arhami |
| author_sort | Nabila Henisaniyya |
| collection | DOAJ |
| description | Thyroid disease refers to a range of conditions or issues affecting the thyroid gland. This gland, located below the Adam’s apple, is responsible for coordinating various metabolic processes in the body, making its function essential. Early detection of thyroid symptoms is crucial as an initial step in planning the necessary treatments to prevent more severe thyroid-related health risks. One commonly applied method for early detection involves classification using a data mining approach. Among the algorithms frequently used for classification are the ID3 algorithm and Artificial Neural Networks (ANN).
This study aims to obtain the best classification results for detecting thyroid disease by comparing these two algorithms. The accuracy results for percentage split testing were 88% for ID3 and 90% for ANN. Meanwhile, the accuracy values for K-Fold cross-validation were 93% for the ID3 algorithm and 95% for the ANN algorithm. Additionally, the overall average precision and recall values for both algorithms were above 75% for percentage split testing and above 90% for K-Fold cross-validation. The results indicate that ANN achieved higher percentages compared to ID3. Based on the accuracy, precision, and recall values obtained from both algorithms, it can be concluded that the ANN algorithm performs better than ID3 in classifying thyroid disease. |
| format | Article |
| id | doaj-art-4e0aa2cb45d2400bbe12cc4e3dfe1156 |
| institution | DOAJ |
| issn | 2302-8149 2540-9719 |
| language | Indonesian |
| publishDate | 2025-01-01 |
| publisher | Islamic University of Indragiri |
| record_format | Article |
| series | Sistemasi: Jurnal Sistem Informasi |
| spelling | doaj-art-4e0aa2cb45d2400bbe12cc4e3dfe11562025-08-20T03:13:14ZindIslamic University of IndragiriSistemasi: Jurnal Sistem Informasi2302-81492540-97192025-01-0114111410.32520/stmsi.v14i1.3440932Classification of Thyroid Class using ID3 Algorithm and Artificial Neural Network (ANN)Nabila Henisaniyya0Citra Pertiwi1Anita Desiani2Ali Amran3Muhammad Arhami4Universitas SriwijayaUniversitas SriwijayaUniversitas SriwijayaUniversitas SriwijayaPoliteknik Negeri LhokseumaweThyroid disease refers to a range of conditions or issues affecting the thyroid gland. This gland, located below the Adam’s apple, is responsible for coordinating various metabolic processes in the body, making its function essential. Early detection of thyroid symptoms is crucial as an initial step in planning the necessary treatments to prevent more severe thyroid-related health risks. One commonly applied method for early detection involves classification using a data mining approach. Among the algorithms frequently used for classification are the ID3 algorithm and Artificial Neural Networks (ANN). This study aims to obtain the best classification results for detecting thyroid disease by comparing these two algorithms. The accuracy results for percentage split testing were 88% for ID3 and 90% for ANN. Meanwhile, the accuracy values for K-Fold cross-validation were 93% for the ID3 algorithm and 95% for the ANN algorithm. Additionally, the overall average precision and recall values for both algorithms were above 75% for percentage split testing and above 90% for K-Fold cross-validation. The results indicate that ANN achieved higher percentages compared to ID3. Based on the accuracy, precision, and recall values obtained from both algorithms, it can be concluded that the ANN algorithm performs better than ID3 in classifying thyroid disease.https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/3440thyroid disease, classification, data mining, id3 algorithm, ann algorithm |
| spellingShingle | Nabila Henisaniyya Citra Pertiwi Anita Desiani Ali Amran Muhammad Arhami Classification of Thyroid Class using ID3 Algorithm and Artificial Neural Network (ANN) Sistemasi: Jurnal Sistem Informasi thyroid disease, classification, data mining, id3 algorithm, ann algorithm |
| title | Classification of Thyroid Class using ID3 Algorithm and Artificial Neural Network (ANN) |
| title_full | Classification of Thyroid Class using ID3 Algorithm and Artificial Neural Network (ANN) |
| title_fullStr | Classification of Thyroid Class using ID3 Algorithm and Artificial Neural Network (ANN) |
| title_full_unstemmed | Classification of Thyroid Class using ID3 Algorithm and Artificial Neural Network (ANN) |
| title_short | Classification of Thyroid Class using ID3 Algorithm and Artificial Neural Network (ANN) |
| title_sort | classification of thyroid class using id3 algorithm and artificial neural network ann |
| topic | thyroid disease, classification, data mining, id3 algorithm, ann algorithm |
| url | https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/3440 |
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