Penerapan Metode K-Means Untuk Clustering Mahasiswa Berdasarkan Nilai Akademik Dengan Weka Interface Studi Kasus Pada Jurusan Teknik Informatika UMM Magelang
<p class="Abstract">The selection process among outstanding students in a department has a big problem. This process is not fair because only involve one criteria and ignore the other criteria. We need the best student to participate in a competition held by the Indonesia Security In...
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Muhammadiyah Yogyakarta
2016-01-01
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| Series: | Semesta Teknika |
| Subjects: | |
| Online Access: | https://journal.umy.ac.id/index.php/st/article/view/708 |
| _version_ | 1819004802969894912 |
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| author | Asroni Asroni Ronald Adrian |
| author_facet | Asroni Asroni Ronald Adrian |
| author_sort | Asroni Asroni |
| collection | DOAJ |
| description | <p class="Abstract">The selection process among outstanding students in a department has a big problem. This process is not fair because only involve one criteria and ignore the other criteria. We need the best student to participate in a competition held by the Indonesia Security Incident Response Team on Internet Infrastructure (ID SIRTII) of the Ministry of Communication and Information. This process uses Weka software to calculate the best student. It provides the various method to explore the data. One of them is clustering method. There are many algorithms in clustering method. In this research, we will investigate widely about one of that algorithms. Its name is K-Means. This algorithm (K-Means) will give the recommendations about the best student based on the cluster. It will represent the many clusters of a student group. The best cluster can be calculated more to get the names of the best students group. They are eligible to enter the competition. K-means involve the GPA (Grade Point Average) and related course to support the academic skill in order to get the best student. This research helps the teacher select the best student to enter the competition. Many similar cases can use this algorithm in order to get the best student.</p> |
| first_indexed | 2024-12-20T23:42:41Z |
| format | Article |
| id | doaj.art-2cfd5020f1c642989ea655c948f2b2dd |
| institution | Directory Open Access Journal |
| issn | 1411-061X 2502-5481 |
| language | English |
| last_indexed | 2024-12-20T23:42:41Z |
| publishDate | 2016-01-01 |
| publisher | Universitas Muhammadiyah Yogyakarta |
| record_format | Article |
| series | Semesta Teknika |
| spelling | doaj.art-2cfd5020f1c642989ea655c948f2b2dd2022-12-21T19:23:02ZengUniversitas Muhammadiyah YogyakartaSemesta Teknika1411-061X2502-54812016-01-011817682611Penerapan Metode K-Means Untuk Clustering Mahasiswa Berdasarkan Nilai Akademik Dengan Weka Interface Studi Kasus Pada Jurusan Teknik Informatika UMM MagelangAsroni Asroni0Ronald Adrian1Universitas Muhammadiyah YogyakartaUniversitas Muhammadiyah Yogyakarta<p class="Abstract">The selection process among outstanding students in a department has a big problem. This process is not fair because only involve one criteria and ignore the other criteria. We need the best student to participate in a competition held by the Indonesia Security Incident Response Team on Internet Infrastructure (ID SIRTII) of the Ministry of Communication and Information. This process uses Weka software to calculate the best student. It provides the various method to explore the data. One of them is clustering method. There are many algorithms in clustering method. In this research, we will investigate widely about one of that algorithms. Its name is K-Means. This algorithm (K-Means) will give the recommendations about the best student based on the cluster. It will represent the many clusters of a student group. The best cluster can be calculated more to get the names of the best students group. They are eligible to enter the competition. K-means involve the GPA (Grade Point Average) and related course to support the academic skill in order to get the best student. This research helps the teacher select the best student to enter the competition. Many similar cases can use this algorithm in order to get the best student.</p>https://journal.umy.ac.id/index.php/st/article/view/708clustering, k-means, algorithm |
| spellingShingle | Asroni Asroni Ronald Adrian Penerapan Metode K-Means Untuk Clustering Mahasiswa Berdasarkan Nilai Akademik Dengan Weka Interface Studi Kasus Pada Jurusan Teknik Informatika UMM Magelang Semesta Teknika clustering, k-means, algorithm |
| title | Penerapan Metode K-Means Untuk Clustering Mahasiswa Berdasarkan Nilai Akademik Dengan Weka Interface Studi Kasus Pada Jurusan Teknik Informatika UMM Magelang |
| title_full | Penerapan Metode K-Means Untuk Clustering Mahasiswa Berdasarkan Nilai Akademik Dengan Weka Interface Studi Kasus Pada Jurusan Teknik Informatika UMM Magelang |
| title_fullStr | Penerapan Metode K-Means Untuk Clustering Mahasiswa Berdasarkan Nilai Akademik Dengan Weka Interface Studi Kasus Pada Jurusan Teknik Informatika UMM Magelang |
| title_full_unstemmed | Penerapan Metode K-Means Untuk Clustering Mahasiswa Berdasarkan Nilai Akademik Dengan Weka Interface Studi Kasus Pada Jurusan Teknik Informatika UMM Magelang |
| title_short | Penerapan Metode K-Means Untuk Clustering Mahasiswa Berdasarkan Nilai Akademik Dengan Weka Interface Studi Kasus Pada Jurusan Teknik Informatika UMM Magelang |
| title_sort | penerapan metode k means untuk clustering mahasiswa berdasarkan nilai akademik dengan weka interface studi kasus pada jurusan teknik informatika umm magelang |
| topic | clustering, k-means, algorithm |
| url | https://journal.umy.ac.id/index.php/st/article/view/708 |
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