Decision Support System for Classification of Early Childhood Diseases Using Principal Component Analysis and K-Nearest Neighbors Classifier
Background: Data on early childhood disease collected in clinics has accumulated into big data. Those data can be used for classification of early childhood diseases to help medical staff in diagnosing diseases that attack early childhoods. Objective: This study aims to apply Principal Component An...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Universitas Airlangga
2019-04-01
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| Series: | Journal of Information Systems Engineering and Business Intelligence |
| Subjects: | |
| Online Access: | https://e-journal.unair.ac.id/JISEBI/article/view/9635 |
| _version_ | 1827998574710882304 |
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| author | Damar Dananjaya Indah Werdiningsih Rini Semiati |
| author_facet | Damar Dananjaya Indah Werdiningsih Rini Semiati |
| author_sort | Damar Dananjaya |
| collection | DOAJ |
| description | Background: Data on early childhood disease collected in clinics has accumulated into big data. Those data can be used for classification of early childhood diseases to help medical staff in diagnosing diseases that attack early childhoods.
Objective: This study aims to apply Principal Component Analysis (PCA) and K-Nearest Neighbor (K-NN) Classifier for the classification of early childhood diseases.
Methods: Data analysis was performed using PCA to obtain variables that had a major influence on the classification of early childhood diseases. PCA was done by observing the correlation between variables and eliminating variables that have little influence on classification. Furthermore, data on early childhood disease was classified using the K-Nearest Neighbor Classifier method.
Results: The results of system evaluation using 150 test data indicated that the classification system by applying PCA and KNN Classifier had an accuracy value of 86%.
Conclusion: PCA can be used to reduce the number of variables involved so that it can improve system performance in terms of efficiency. In addition, the application of PCA and KNN can also improve accuracy in the classification of early childhood diseases. |
| first_indexed | 2024-04-10T05:45:19Z |
| format | Article |
| id | doaj.art-6744119d3e554f969a5ea8b445bf9b39 |
| institution | Directory Open Access Journal |
| issn | 2598-6333 2443-2555 |
| language | English |
| last_indexed | 2024-04-10T05:45:19Z |
| publishDate | 2019-04-01 |
| publisher | Universitas Airlangga |
| record_format | Article |
| series | Journal of Information Systems Engineering and Business Intelligence |
| spelling | doaj.art-6744119d3e554f969a5ea8b445bf9b392023-03-06T02:56:32ZengUniversitas AirlanggaJournal of Information Systems Engineering and Business Intelligence2598-63332443-25552019-04-0151132210.20473/jisebi.5.1.13-227631Decision Support System for Classification of Early Childhood Diseases Using Principal Component Analysis and K-Nearest Neighbors ClassifierDamar DananjayaIndah WerdiningsihRini SemiatiBackground: Data on early childhood disease collected in clinics has accumulated into big data. Those data can be used for classification of early childhood diseases to help medical staff in diagnosing diseases that attack early childhoods. Objective: This study aims to apply Principal Component Analysis (PCA) and K-Nearest Neighbor (K-NN) Classifier for the classification of early childhood diseases. Methods: Data analysis was performed using PCA to obtain variables that had a major influence on the classification of early childhood diseases. PCA was done by observing the correlation between variables and eliminating variables that have little influence on classification. Furthermore, data on early childhood disease was classified using the K-Nearest Neighbor Classifier method. Results: The results of system evaluation using 150 test data indicated that the classification system by applying PCA and KNN Classifier had an accuracy value of 86%. Conclusion: PCA can be used to reduce the number of variables involved so that it can improve system performance in terms of efficiency. In addition, the application of PCA and KNN can also improve accuracy in the classification of early childhood diseases.https://e-journal.unair.ac.id/JISEBI/article/view/9635toddler diseasesprincipal component analysis (pca)k-nearest neighbor classifier |
| spellingShingle | Damar Dananjaya Indah Werdiningsih Rini Semiati Decision Support System for Classification of Early Childhood Diseases Using Principal Component Analysis and K-Nearest Neighbors Classifier Journal of Information Systems Engineering and Business Intelligence toddler diseases principal component analysis (pca) k-nearest neighbor classifier |
| title | Decision Support System for Classification of Early Childhood Diseases Using Principal Component Analysis and K-Nearest Neighbors Classifier |
| title_full | Decision Support System for Classification of Early Childhood Diseases Using Principal Component Analysis and K-Nearest Neighbors Classifier |
| title_fullStr | Decision Support System for Classification of Early Childhood Diseases Using Principal Component Analysis and K-Nearest Neighbors Classifier |
| title_full_unstemmed | Decision Support System for Classification of Early Childhood Diseases Using Principal Component Analysis and K-Nearest Neighbors Classifier |
| title_short | Decision Support System for Classification of Early Childhood Diseases Using Principal Component Analysis and K-Nearest Neighbors Classifier |
| title_sort | decision support system for classification of early childhood diseases using principal component analysis and k nearest neighbors classifier |
| topic | toddler diseases principal component analysis (pca) k-nearest neighbor classifier |
| url | https://e-journal.unair.ac.id/JISEBI/article/view/9635 |
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