Sistem Rekomendasi Pemilihan Peminatan Menggunakan Density Canopy K-Means
The carelessly selection of specialization course leaves some students with difficulty. Therefore, it is needed a recommendation system to solve it. Several approaches could be used to build the system, one of them was K-Means. K-Means required the number of initial centroid at random, so its result...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2020-02-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | http://jurnal.iaii.or.id/index.php/RESTI/article/view/1531 |
| _version_ | 1797329331750436864 |
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| author | Ridho Ananda Muhammad Zidny Naf’an Amalia Beladinna Arifa Auliya Burhanuddin |
| author_facet | Ridho Ananda Muhammad Zidny Naf’an Amalia Beladinna Arifa Auliya Burhanuddin |
| author_sort | Ridho Ananda |
| collection | DOAJ |
| description | The carelessly selection of specialization course leaves some students with difficulty. Therefore, it is needed a recommendation system to solve it. Several approaches could be used to build the system, one of them was K-Means. K-Means required the number of initial centroid at random, so its result was not yet optimal. To determine the optimal initial centroid, Density Canopy (DC) algorithms had been proposed. In this research, DC and K-Means (DCKM) was implemented to build the recommendation system in the problem. The alpha criterion was also proposed to improve the performance of DCKM. The academic quality dataset in the 2018 informatics programs students of ITTP was used. There were three main stages in the system, namely determination of the weight of the course in dataset, implementation of DCKM, and determination of specialization recommendations. The results showed that the system by using DCKM has good quality based on the Silhouette results (at least 0.655). The system also used standar valuation scale in ITTP and silhouette index in the process of system. The results showed that 176 (65.91%) students were recommended in IT specialization, 25 (9.36%) students were recommended in MM specialization and 66 (24.7%) students were recommended in SC specialization. |
| first_indexed | 2024-03-08T07:04:10Z |
| format | Article |
| id | doaj.art-e458182f6ae34632a2a41f48c670c606 |
| institution | Directory Open Access Journal |
| issn | 2580-0760 |
| language | English |
| last_indexed | 2024-03-08T07:04:10Z |
| publishDate | 2020-02-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj.art-e458182f6ae34632a2a41f48c670c6062024-02-03T05:15:47ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602020-02-014117217910.29207/resti.v4i1.15311531Sistem Rekomendasi Pemilihan Peminatan Menggunakan Density Canopy K-MeansRidho Ananda0Muhammad Zidny Naf’an1Amalia Beladinna Arifa2Auliya Burhanuddin3Institut Teknologi Telkom PurwokertoInstitut Teknologi Telkom PurwokertoInstitut Teknologi Telkom PurwokertoInstitut Teknologi Telkom PurwokertoThe carelessly selection of specialization course leaves some students with difficulty. Therefore, it is needed a recommendation system to solve it. Several approaches could be used to build the system, one of them was K-Means. K-Means required the number of initial centroid at random, so its result was not yet optimal. To determine the optimal initial centroid, Density Canopy (DC) algorithms had been proposed. In this research, DC and K-Means (DCKM) was implemented to build the recommendation system in the problem. The alpha criterion was also proposed to improve the performance of DCKM. The academic quality dataset in the 2018 informatics programs students of ITTP was used. There were three main stages in the system, namely determination of the weight of the course in dataset, implementation of DCKM, and determination of specialization recommendations. The results showed that the system by using DCKM has good quality based on the Silhouette results (at least 0.655). The system also used standar valuation scale in ITTP and silhouette index in the process of system. The results showed that 176 (65.91%) students were recommended in IT specialization, 25 (9.36%) students were recommended in MM specialization and 66 (24.7%) students were recommended in SC specialization.http://jurnal.iaii.or.id/index.php/RESTI/article/view/1531recommendation systemdensity canopyk-meanssilhouette indexselecselection of specialization |
| spellingShingle | Ridho Ananda Muhammad Zidny Naf’an Amalia Beladinna Arifa Auliya Burhanuddin Sistem Rekomendasi Pemilihan Peminatan Menggunakan Density Canopy K-Means Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) recommendation system density canopy k-means silhouette index selec selection of specialization |
| title | Sistem Rekomendasi Pemilihan Peminatan Menggunakan Density Canopy K-Means |
| title_full | Sistem Rekomendasi Pemilihan Peminatan Menggunakan Density Canopy K-Means |
| title_fullStr | Sistem Rekomendasi Pemilihan Peminatan Menggunakan Density Canopy K-Means |
| title_full_unstemmed | Sistem Rekomendasi Pemilihan Peminatan Menggunakan Density Canopy K-Means |
| title_short | Sistem Rekomendasi Pemilihan Peminatan Menggunakan Density Canopy K-Means |
| title_sort | sistem rekomendasi pemilihan peminatan menggunakan density canopy k means |
| topic | recommendation system density canopy k-means silhouette index selec selection of specialization |
| url | http://jurnal.iaii.or.id/index.php/RESTI/article/view/1531 |
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