ANALISIS PERBANDINGAN KINERJA CART KONVENSIONAL, BAGGING DAN RANDOM FOREST PADA KLASIFIKASI OBJEK: HASIL DARI DUA SIMULASI
The conventional CART method is a nonparametric classification method built on categorical response data. Bagging is one of the popular ensemble methods whereas, Random Forests (RF) is one of the relatively new ensemble methods in the decision tree that is the development of the Bagging method. Unli...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Diponegoro
2019-07-01
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| Series: | Media Statistika |
| Online Access: | https://ejournal.undip.ac.id/index.php/media_statistika/article/view/18109 |
| _version_ | 1818511156379123712 |
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| author | Yogo Aryo Jatmiko Septiadi Padmadisastra Anna Chadidjah |
| author_facet | Yogo Aryo Jatmiko Septiadi Padmadisastra Anna Chadidjah |
| author_sort | Yogo Aryo Jatmiko |
| collection | DOAJ |
| description | The conventional CART method is a nonparametric classification method built on categorical response data. Bagging is one of the popular ensemble methods whereas, Random Forests (RF) is one of the relatively new ensemble methods in the decision tree that is the development of the Bagging method. Unlike Bagging, Random Forest was developed with the idea of adding layers to the random resampling process in bagging. Therefore, not only randomly sampled sample data to form a classification tree, but also independent variables are randomly selected and newly selected as the best divider when determining the sorting of trees, which is expected to produce more accurate predictions. Based on the above, the authors are interested to study the three methods by comparing the accuracy of classification on binary and non-binary simulation data to understand the effect of the number of sample sizes, the correlation between independent variables, the presence or absence of certain distribution patterns to the accuracy generated classification method. Results of the research on simulation data show that the Random Forest ensemble method can improve the accuracy of classification. |
| first_indexed | 2024-12-10T23:29:37Z |
| format | Article |
| id | doaj.art-261dc8cd2a4943f7bf23217edd324a83 |
| institution | Directory Open Access Journal |
| issn | 1979-3693 2477-0647 |
| language | English |
| last_indexed | 2024-12-10T23:29:37Z |
| publishDate | 2019-07-01 |
| publisher | Universitas Diponegoro |
| record_format | Article |
| series | Media Statistika |
| spelling | doaj.art-261dc8cd2a4943f7bf23217edd324a832022-12-22T01:29:27ZengUniversitas DiponegoroMedia Statistika1979-36932477-06472019-07-0112111210.14710/medstat.12.1.1-1215235ANALISIS PERBANDINGAN KINERJA CART KONVENSIONAL, BAGGING DAN RANDOM FOREST PADA KLASIFIKASI OBJEK: HASIL DARI DUA SIMULASIYogo Aryo Jatmiko0Septiadi Padmadisastra1Anna Chadidjah2Badan Pusat StatistikUniversitas PadjajaranUniversitas PadjajaranThe conventional CART method is a nonparametric classification method built on categorical response data. Bagging is one of the popular ensemble methods whereas, Random Forests (RF) is one of the relatively new ensemble methods in the decision tree that is the development of the Bagging method. Unlike Bagging, Random Forest was developed with the idea of adding layers to the random resampling process in bagging. Therefore, not only randomly sampled sample data to form a classification tree, but also independent variables are randomly selected and newly selected as the best divider when determining the sorting of trees, which is expected to produce more accurate predictions. Based on the above, the authors are interested to study the three methods by comparing the accuracy of classification on binary and non-binary simulation data to understand the effect of the number of sample sizes, the correlation between independent variables, the presence or absence of certain distribution patterns to the accuracy generated classification method. Results of the research on simulation data show that the Random Forest ensemble method can improve the accuracy of classification.https://ejournal.undip.ac.id/index.php/media_statistika/article/view/18109 |
| spellingShingle | Yogo Aryo Jatmiko Septiadi Padmadisastra Anna Chadidjah ANALISIS PERBANDINGAN KINERJA CART KONVENSIONAL, BAGGING DAN RANDOM FOREST PADA KLASIFIKASI OBJEK: HASIL DARI DUA SIMULASI Media Statistika |
| title | ANALISIS PERBANDINGAN KINERJA CART KONVENSIONAL, BAGGING DAN RANDOM FOREST PADA KLASIFIKASI OBJEK: HASIL DARI DUA SIMULASI |
| title_full | ANALISIS PERBANDINGAN KINERJA CART KONVENSIONAL, BAGGING DAN RANDOM FOREST PADA KLASIFIKASI OBJEK: HASIL DARI DUA SIMULASI |
| title_fullStr | ANALISIS PERBANDINGAN KINERJA CART KONVENSIONAL, BAGGING DAN RANDOM FOREST PADA KLASIFIKASI OBJEK: HASIL DARI DUA SIMULASI |
| title_full_unstemmed | ANALISIS PERBANDINGAN KINERJA CART KONVENSIONAL, BAGGING DAN RANDOM FOREST PADA KLASIFIKASI OBJEK: HASIL DARI DUA SIMULASI |
| title_short | ANALISIS PERBANDINGAN KINERJA CART KONVENSIONAL, BAGGING DAN RANDOM FOREST PADA KLASIFIKASI OBJEK: HASIL DARI DUA SIMULASI |
| title_sort | analisis perbandingan kinerja cart konvensional bagging dan random forest pada klasifikasi objek hasil dari dua simulasi |
| url | https://ejournal.undip.ac.id/index.php/media_statistika/article/view/18109 |
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