BibTex Citation Data :
@article{J.Gauss39485, author = {Anita Mila Oktafani and Iut Tri Utami and Puspita Kartikasari}, title = {Optimasi Backward Elimination Pada Klasifikasi Penyakit ISPA Menggunakan Algoritma Naïve Bayes Classifier}, journal = {Jurnal Gaussian}, volume = {14}, number = {1}, year = {2025}, keywords = {}, abstract = { The cases of ISPA in Indonesia are still a problem that threatens the health of various age groups, especially toddlers. ISPA needs to be dealt with quickly so that the classification of ISPA data is carried out. This research classifies ISPA with optimize the features selection Backward Elimination in the classification of ISPA using a Naïve Bayes Classifier algorithm. Selection of this algorithm is because this algorithm is superior than other classification methods when testing on categorical types and the process is fast also produces high accuracy. The use of Backward Elimination aims to select irrelevant variables and increase the accuracy of the Naïve Bayes Classifier. The result of this study without feature selection obtains an accuracy 79,41%. The accuracy of the Naïve Bayes Classifier model increased by 2,94% after feature selection was implemented to 82,35%. This point out that the performance of Backward Elimination feature selection is effective in optimizing the classification of ISPA using the Naïve Bayes Classifier algorithm. Keywords: ISPA, Data Mining, Naïve Bayes Classifier, Feature Selection, Backward Elimination }, issn = {2339-2541}, pages = {23--30} doi = {10.14710/j.gauss.14.1.23-30}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/39485} }
Refworks Citation Data :
The cases of ISPA in Indonesia are still a problem that threatens the health of various age groups, especially toddlers. ISPA needs to be dealt with quickly so that the classification of ISPA data is carried out. This research classifies ISPA with optimize the features selection Backward Elimination in the classification of ISPA using a Naïve Bayes Classifier algorithm. Selection of this algorithm is because this algorithm is superior than other classification methods when testing on categorical types and the process is fast also produces high accuracy. The use of Backward Elimination aims to select irrelevant variables and increase the accuracy of the Naïve Bayes Classifier. The result of this study without feature selection obtains an accuracy 79,41%. The accuracy of the Naïve Bayes Classifier model increased by 2,94% after feature selection was implemented to 82,35%. This point out that the performance of Backward Elimination feature selection is effective in optimizing the classification of ISPA using the Naïve Bayes Classifier algorithm.
Keywords: ISPA, Data Mining, Naïve Bayes Classifier, Feature Selection, Backward Elimination
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