Comparative Analysis to Determine the Best Accuracy of Classification Methods
The classification method is one of the methods of supervised learning and predictive learning. This method can be used to detect an object in the image presented, whether it is in accordance with the existing object in the training phase. There are several classification methods used, including Sup...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Fakultas Ilmu Komputer UMI
2022-08-01
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| Series: | Ilkom Jurnal Ilmiah |
| Subjects: | |
| Online Access: | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1128 |
| _version_ | 1797850435651895296 |
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| author | Warnia Nengsih Yuli Fitrisia Mardhiah Fadhli |
| author_facet | Warnia Nengsih Yuli Fitrisia Mardhiah Fadhli |
| author_sort | Warnia Nengsih |
| collection | DOAJ |
| description | The classification method is one of the methods of supervised learning and predictive learning. This method can be used to detect an object in the image presented, whether it is in accordance with the existing object in the training phase. There are several classification methods used, including Support Vector Machine (SVM), K-Nearest Neighbors (K-NN) and Decision Tree. To determine the accuracy in detecting these objects, it is necessary to measure the accuracy of each classification method used. The object that becomes simulation in this research is the object image of Guava and Pear fruit. Testing using confusion matrix. The results showed that the Support Vector Machine (SVM) method was able to detect with an accuracy of 98.09%. Then the K-Nearest Neighbors (K-NN) method with an accuracy of 98.06%, then the Decision Tree method with an accuracy of 97.57%. From the results of the accuracy test, it can be concluded that basically these three classification methods have good accuracy with a difference of 0.49% and the overall average accuracy of the classification of the three methods is 97.89% |
| first_indexed | 2024-04-09T19:00:13Z |
| format | Article |
| id | doaj.art-62f4c94ded3047e08976aa5d9fbc5bac |
| institution | Directory Open Access Journal |
| issn | 2087-1716 2548-7779 |
| language | English |
| last_indexed | 2024-04-09T19:00:13Z |
| publishDate | 2022-08-01 |
| publisher | Fakultas Ilmu Komputer UMI |
| record_format | Article |
| series | Ilkom Jurnal Ilmiah |
| spelling | doaj.art-62f4c94ded3047e08976aa5d9fbc5bac2023-04-08T08:20:28ZengFakultas Ilmu Komputer UMIIlkom Jurnal Ilmiah2087-17162548-77792022-08-0114213414110.33096/ilkom.v14i2.1128.134-141422Comparative Analysis to Determine the Best Accuracy of Classification MethodsWarnia Nengsih0Yuli Fitrisia1Mardhiah Fadhli2Politeknik Caltex RiauPoliteknik Caltex RiauPoliteknik Caltex RiauThe classification method is one of the methods of supervised learning and predictive learning. This method can be used to detect an object in the image presented, whether it is in accordance with the existing object in the training phase. There are several classification methods used, including Support Vector Machine (SVM), K-Nearest Neighbors (K-NN) and Decision Tree. To determine the accuracy in detecting these objects, it is necessary to measure the accuracy of each classification method used. The object that becomes simulation in this research is the object image of Guava and Pear fruit. Testing using confusion matrix. The results showed that the Support Vector Machine (SVM) method was able to detect with an accuracy of 98.09%. Then the K-Nearest Neighbors (K-NN) method with an accuracy of 98.06%, then the Decision Tree method with an accuracy of 97.57%. From the results of the accuracy test, it can be concluded that basically these three classification methods have good accuracy with a difference of 0.49% and the overall average accuracy of the classification of the three methods is 97.89%https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1128svmk-nndecision treeclassification |
| spellingShingle | Warnia Nengsih Yuli Fitrisia Mardhiah Fadhli Comparative Analysis to Determine the Best Accuracy of Classification Methods Ilkom Jurnal Ilmiah svm k-nn decision tree classification |
| title | Comparative Analysis to Determine the Best Accuracy of Classification Methods |
| title_full | Comparative Analysis to Determine the Best Accuracy of Classification Methods |
| title_fullStr | Comparative Analysis to Determine the Best Accuracy of Classification Methods |
| title_full_unstemmed | Comparative Analysis to Determine the Best Accuracy of Classification Methods |
| title_short | Comparative Analysis to Determine the Best Accuracy of Classification Methods |
| title_sort | comparative analysis to determine the best accuracy of classification methods |
| topic | svm k-nn decision tree classification |
| url | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1128 |
| work_keys_str_mv | AT warnianengsih comparativeanalysistodeterminethebestaccuracyofclassificationmethods AT yulifitrisia comparativeanalysistodeterminethebestaccuracyofclassificationmethods AT mardhiahfadhli comparativeanalysistodeterminethebestaccuracyofclassificationmethods |