Pengelompokan Data Menggunakan Pattern Reduction Enhanced Ant Colony Optimization dan Kernel Clustering
One method of optimization that can be used for clustering is Ant Colony Optimization (ACO). This method is good in data clustering, but has disadvantage in terms of time and quality or solution convergence. In this study, ACO-based Pattern Reduction Enhanced Ant Colony Optimization (PREACO) method...
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| Format: | Article |
| Language: | English |
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Universitas Gadjah Mada
2016-08-01
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| Series: | Jurnal Nasional Teknik Elektro dan Teknologi Informasi |
| Subjects: | |
| Online Access: | http://ejnteti.jteti.ugm.ac.id/index.php/JNTETI/article/view/251 |
| _version_ | 1828288690435129344 |
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| author | Dwi Taufik Hidayat Chastine Fatichah R.V. Hari Ginardi |
| author_facet | Dwi Taufik Hidayat Chastine Fatichah R.V. Hari Ginardi |
| author_sort | Dwi Taufik Hidayat |
| collection | DOAJ |
| description | One method of optimization that can be used for clustering is Ant Colony Optimization (ACO). This method is good in data clustering, but has disadvantage in terms of time and quality or solution convergence. In this study, ACO-based Pattern Reduction Enhanced Ant Colony Optimization (PREACO) method with a gaussian kernel function is proposed. First, it sets up initial solution. Second, the magnitude of pheromone is calculated to find the centroid randomly. With the initialized solution, the weight of the solution is calculated and the center of cluster is revised. The solution will be evaluated through a gaussian kernel functions. Function 'pattern enhanced reduction' is useful to ensure maximum value of pheromone update. Those steps will be conducted repeatedly until the best solution is chosen. Tests are performed on multiple datasets, with three test scenarios. The first test is carried out to get the right combination of parameters. Second, the error rate measurement and similarity data using Sum of Squared Errors is done. Third, level of accuracy of the methods ACO, ACO with the kernel, PREACO, and PREACO with the kernel is compared. The test results show that the proposed method has a higher accuracy rate of 99.8% for synthetic data, 93.8% for wine data than other methods. But it has a lower accuracy by 88.7% compared to the ACO. |
| first_indexed | 2024-04-13T10:09:38Z |
| format | Article |
| id | doaj.art-463f4922c9c84c9896d0b462e74991c4 |
| institution | Directory Open Access Journal |
| issn | 2301-4156 2460-5719 |
| language | English |
| last_indexed | 2024-04-13T10:09:38Z |
| publishDate | 2016-08-01 |
| publisher | Universitas Gadjah Mada |
| record_format | Article |
| series | Jurnal Nasional Teknik Elektro dan Teknologi Informasi |
| spelling | doaj.art-463f4922c9c84c9896d0b462e74991c42022-12-22T02:50:58ZengUniversitas Gadjah MadaJurnal Nasional Teknik Elektro dan Teknologi Informasi2301-41562460-57192016-08-015315516010.22146/jnteti.v5i3.251Pengelompokan Data Menggunakan Pattern Reduction Enhanced Ant Colony Optimization dan Kernel ClusteringDwi Taufik Hidayat0Chastine Fatichah1R.V. Hari Ginardi2Institut Teknologi Sepuluh NopemberInstitut Teknologi Sepuluh NopemberInstitut Teknologi Sepuluh NopemberOne method of optimization that can be used for clustering is Ant Colony Optimization (ACO). This method is good in data clustering, but has disadvantage in terms of time and quality or solution convergence. In this study, ACO-based Pattern Reduction Enhanced Ant Colony Optimization (PREACO) method with a gaussian kernel function is proposed. First, it sets up initial solution. Second, the magnitude of pheromone is calculated to find the centroid randomly. With the initialized solution, the weight of the solution is calculated and the center of cluster is revised. The solution will be evaluated through a gaussian kernel functions. Function 'pattern enhanced reduction' is useful to ensure maximum value of pheromone update. Those steps will be conducted repeatedly until the best solution is chosen. Tests are performed on multiple datasets, with three test scenarios. The first test is carried out to get the right combination of parameters. Second, the error rate measurement and similarity data using Sum of Squared Errors is done. Third, level of accuracy of the methods ACO, ACO with the kernel, PREACO, and PREACO with the kernel is compared. The test results show that the proposed method has a higher accuracy rate of 99.8% for synthetic data, 93.8% for wine data than other methods. But it has a lower accuracy by 88.7% compared to the ACO.http://ejnteti.jteti.ugm.ac.id/index.php/JNTETI/article/view/251kernel clusteringant colony optimizationpattern reduction enhanced ant colony optimization |
| spellingShingle | Dwi Taufik Hidayat Chastine Fatichah R.V. Hari Ginardi Pengelompokan Data Menggunakan Pattern Reduction Enhanced Ant Colony Optimization dan Kernel Clustering Jurnal Nasional Teknik Elektro dan Teknologi Informasi kernel clustering ant colony optimization pattern reduction enhanced ant colony optimization |
| title | Pengelompokan Data Menggunakan Pattern Reduction Enhanced Ant Colony Optimization dan Kernel Clustering |
| title_full | Pengelompokan Data Menggunakan Pattern Reduction Enhanced Ant Colony Optimization dan Kernel Clustering |
| title_fullStr | Pengelompokan Data Menggunakan Pattern Reduction Enhanced Ant Colony Optimization dan Kernel Clustering |
| title_full_unstemmed | Pengelompokan Data Menggunakan Pattern Reduction Enhanced Ant Colony Optimization dan Kernel Clustering |
| title_short | Pengelompokan Data Menggunakan Pattern Reduction Enhanced Ant Colony Optimization dan Kernel Clustering |
| title_sort | pengelompokan data menggunakan pattern reduction enhanced ant colony optimization dan kernel clustering |
| topic | kernel clustering ant colony optimization pattern reduction enhanced ant colony optimization |
| url | http://ejnteti.jteti.ugm.ac.id/index.php/JNTETI/article/view/251 |
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