Recommendation System for Specialization Selection Using K-Means Density Canopy

Sistem Rekomendasi Pemilihan Peminatan Menggunakan Density Canopy K-Means

  • Ridho Ananda Institut Teknologi Telkom Purwokerto
  • Muhammad Zidny Naf’an Institut Teknologi Telkom Purwokerto
  • Amalia Beladinna Arifa Institut Teknologi Telkom Purwokerto
  • Auliya Burhanuddin Institut Teknologi Telkom Purwokerto
Keywords: recommendation system, density canopy, k-means, silhouette index, selec, selection of specialization

Abstract

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.

 

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References

Saputa A., Mulyawan B., Sutrisno T., 2019. Rekomendasi Lokasi Wisata Kuliner di Jakarta Menggunakan Metode K-Means Clustering dan Simple Additive Weighting. Jurnal Ilmu Komputer dan Sistem Informasi, 7(1), pp. 14-21.

Putra I. M. A.W., Indrawa G., Aryanto K. Y. E., 2018. Sistem Rekomendasi Berdasarkan Data Transaksi Perpustakaan Daerah Tabanan dengan Menggunakan K-Means Clustering. Jurnal Ilmu Komputer Indonesia, 3(1), pp. 18-22.

Aprizal, Hasriani, Rauf A., 2018. Implementasi Sistem Rekomendasi Barang Customer pada E-Commerce MTC Karebosi Menggunakan Metode K-Means Clustering dan Metode Decision Tree, Prosiding Seminar Ilmiah Sistem Informasi dan Teknologi Informasi, Makassar, Indonesia. 7(2).

Afifuddin R. N., Nurjanah D., 2019. Sistem Rekomendasi Pemilihan Mata Kuliah Peminatan Menggunakan Algoritme K-means dan Apriori (Studi Kasus: Jurusan S1 Teknik Informatika Fakultas Informatika), E-proceeding of Engineering, 6(1), pp. 2359-2367.

Widiyanti, W., 2017. Sentroid Awal Metode K-Means Dengan Pendekatan Metode Berhirarki Dalam Pengelompokkan Provinsi Di Indonesia. Skripsi FMIPA: Institut Pertanian Bogor.

Ananda R., Burhanuddin A., 2019. Analisis Mutu Pendidikan Sekolah Menengah Atas Program Ilmu Alam Di Jawa Tengah Dengan Algoritme K-Means Terorganisir. Journal of INISTA, 2(1), pp. 065-072.

Kumar A., Ingle Y. S., Pande A., Dhule P., 2014. Canopy Clustering: A Review on Pre-Clustering Approach To K-Means Clustering. IJIACS, 3(5), pp. 22-29.

Zhang G., Zhang C., Zhang H., 2018. Improved K-Means Algorithm Based on Density Canopy. Journal Knowledge-based Systems, 145, pp. 289-297.

Abriyanti A., Damastuti N., 2019. Segmentasi Mahasiswa dengan Unsupervised Algoritme guna Membangun Strategi Marketing Penerimaan Mahasiswa. Jurnal Insand Comtech, 4(2), pp. 10-18.

Cahyana N. H., Aribowo A. S., 2018. Metode Data Mining K-Means Untuk Klasterisasi Data Penanganan Dan Pelayanan Kesehatan Masyarakat. Seminar Nasional Informatika Medis, pp. 24-31.

Listiani, L., Agustin, Y. H., Ramdhani, M. Z., 2019. Implementasi Algoritme K-Means Cluster Untuk Rekomendasi Pekerjaan Berdasarkan Pengelompokan Data Penduduk. Seminar Nasional Sistem Informasi dan Teknik Informatika, pp. 761-769.

Rendon E., Abundez I. M., Gutierrez C., Zagal S. D., Arizmendi A., Quiroz E.M., Arzate H. E., 2011. A Comparison of Internal and External Cluster Validation Indexes. in Proceeding of the 2011 American Conference on Applied Mathematics and the 5th WSEAS International Conference on Computer Engineering and Applications.

Rendon E., Abundez I. M., Quiroz E. M., 2011. Internal Versus External Cluster Validation Indexes. International Journal of Computers and Communications, 5(1), pp. 27-34.

Anitha S., Metilda M., 2019. An Extensive Investigation of Outlier Detection by Cluster Validation Indices. Ciencia e Tecnica Vitivinicola-A Science and Technology Journal, 34(2),pp. 22-32.

Hassan S. I., Samad A., Ahmad O., Alam A., 2019. Partitioning and Hierarchical Based Clustering: A Comparative Empirical Assessment on Internal and External Indices, Accuracy, and Time. Int. J. Inf. Tech., doi: https://doi.org/10.1007/s41870-019-00406-7

Published
2020-02-20
How to Cite
Ananda, R., Muhammad Zidny Naf’an, Amalia Beladinna Arifa, & Auliya Burhanuddin. (2020). Recommendation System for Specialization Selection Using K-Means Density Canopy. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(1), 172 - 179. https://doi.org/10.29207/resti.v4i1.1531
Section
Artikel Teknologi Informasi