Implementation of K-Means clustering algorithm in mapping the groups of graduated or dropped-out students in the Management Department of the National University
This study aims to determine the characteristics of students who are likely to graduate or drop out (DO) in the management department of the National University, Jakarta. The study was conducted by implementing the K-Means algorithm, where each data is grouped according to the closest distance to th...
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| Format: | Article |
| Language: | English |
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Program Studi Teknik Informatika Universitas Trilogi
2021-06-01
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| Series: | JISA (Jurnal Informatika dan Sains) |
| Subjects: | |
| Online Access: | https://trilogi.ac.id/journal/ks/index.php/JISA/article/view/848 |
| _version_ | 1828385944311431168 |
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| author | Muhammad Darwis Liyando Hermawan Hasibuan Mochammad Firmansyah Nur Ahady Rizka Tiaharyadini |
| author_facet | Muhammad Darwis Liyando Hermawan Hasibuan Mochammad Firmansyah Nur Ahady Rizka Tiaharyadini |
| author_sort | Muhammad Darwis |
| collection | DOAJ |
| description | This study aims to determine the characteristics of students who are likely to graduate or drop out (DO) in the management department of the National University, Jakarta. The study was conducted by implementing the K-Means algorithm, where each data is grouped according to the closest distance to the centroid. Determination of Cluster C1 graduate or C2 drop out is based on the attributes of status of students (active, leave, out and non-active), educational status (graduated or DO), GPA, total credits taken and length of study. To facilitate the clustering process, Orange tools are used that provide K-Means algorithm features. The total data input in this study were 1988 students from various classes. As a result, a pattern or mapping of graduated or DO students was found based on the attributes mentioned earlier. Testing the results of this cluster with the silhouette method, by measuring the distance between cluster members, both C1 and C2, showed good Silhouetter value, reaching 85%. The management department, National University can use the results of this study to predict the graduation of their students. |
| first_indexed | 2024-12-10T05:29:48Z |
| format | Article |
| id | doaj.art-5526bcabb176472da7e906910369202b |
| institution | Directory Open Access Journal |
| issn | 2776-3234 2614-8404 |
| language | English |
| last_indexed | 2024-12-10T05:29:48Z |
| publishDate | 2021-06-01 |
| publisher | Program Studi Teknik Informatika Universitas Trilogi |
| record_format | Article |
| series | JISA (Jurnal Informatika dan Sains) |
| spelling | doaj.art-5526bcabb176472da7e906910369202b2022-12-22T02:00:36ZengProgram Studi Teknik Informatika Universitas TrilogiJISA (Jurnal Informatika dan Sains)2776-32342614-84042021-06-01411910.31326/jisa.v4i1.848528Implementation of K-Means clustering algorithm in mapping the groups of graduated or dropped-out students in the Management Department of the National UniversityMuhammad Darwis0Liyando Hermawan Hasibuan1Mochammad Firmansyah2Nur Ahady3Rizka Tiaharyadini4Universitas Budi LuhurUniversitas Budi LuhurUniversitas Budi LuhurUniversitas Budi LuhurUniversitas Budi LuhurThis study aims to determine the characteristics of students who are likely to graduate or drop out (DO) in the management department of the National University, Jakarta. The study was conducted by implementing the K-Means algorithm, where each data is grouped according to the closest distance to the centroid. Determination of Cluster C1 graduate or C2 drop out is based on the attributes of status of students (active, leave, out and non-active), educational status (graduated or DO), GPA, total credits taken and length of study. To facilitate the clustering process, Orange tools are used that provide K-Means algorithm features. The total data input in this study were 1988 students from various classes. As a result, a pattern or mapping of graduated or DO students was found based on the attributes mentioned earlier. Testing the results of this cluster with the silhouette method, by measuring the distance between cluster members, both C1 and C2, showed good Silhouetter value, reaching 85%. The management department, National University can use the results of this study to predict the graduation of their students.https://trilogi.ac.id/journal/ks/index.php/JISA/article/view/848data miningclusteringk-mean algorithmorangegraduationmapping |
| spellingShingle | Muhammad Darwis Liyando Hermawan Hasibuan Mochammad Firmansyah Nur Ahady Rizka Tiaharyadini Implementation of K-Means clustering algorithm in mapping the groups of graduated or dropped-out students in the Management Department of the National University JISA (Jurnal Informatika dan Sains) data mining clustering k-mean algorithm orange graduation mapping |
| title | Implementation of K-Means clustering algorithm in mapping the groups of graduated or dropped-out students in the Management Department of the National University |
| title_full | Implementation of K-Means clustering algorithm in mapping the groups of graduated or dropped-out students in the Management Department of the National University |
| title_fullStr | Implementation of K-Means clustering algorithm in mapping the groups of graduated or dropped-out students in the Management Department of the National University |
| title_full_unstemmed | Implementation of K-Means clustering algorithm in mapping the groups of graduated or dropped-out students in the Management Department of the National University |
| title_short | Implementation of K-Means clustering algorithm in mapping the groups of graduated or dropped-out students in the Management Department of the National University |
| title_sort | implementation of k means clustering algorithm in mapping the groups of graduated or dropped out students in the management department of the national university |
| topic | data mining clustering k-mean algorithm orange graduation mapping |
| url | https://trilogi.ac.id/journal/ks/index.php/JISA/article/view/848 |
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