Implementasi Algoritma K-Means untuk Klasterisasi Peserta Olimpiade Sains Nasional Tingkat SMA
DOI:
https://doi.org/10.37034/jsisfotek.v1i3.6Keywords:
K-Means Clustering, Centroid, Science Olympiad, Competence, Best StudentAbstract
The abundance of students causes student data in the system to also be abundant. Schools often find it difficult to manage large amounts of data manually, especially in selecting National Science Olympiad participants and decisions made are less effective. So this research was conducted with the aim of helping the school in selecting OSN participants appropriately and effectively. The method used is Clustering with K-Means algorithm on the report card grades of students majoring in Natural Sciences at SMA Negeri 5 Sijunjung. The results in this study get 3 clusters of students on the selection of OSN participants, namely students who are Very Competent, Competent and Less Competent. This research can be used as a benchmark used by schools in making decisions on the selection of OSN participants.
References
Setiawan R. (2016). Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Untuk Menentukan Strategi Promosi Mahasiswa Baru ( Studi Kasus : Politeknik Lp3i Jakarta). J. Lentera Ict, 3(1), 76–92.
Heni S., & Irham Gufroni, A. (2017). Penerapan Data Mining Dalam Pengelompokan Penderita Thalassaemia. Jurnal Nasional Teknologi Dan Sistem Informasi, 03(02), 299–305. https://doi.org/10.25077/TEKNOSI.v3i2.2017.299-305
Maulida L. (2018). Penerapan Datamining Dalam Mengelompokkan Kunjungan Wisatawan Ke Objek Wisata Unggulan Di Prov. Dki Jakarta Dengan K-Means. JISKA (Jurnal Informatika Sunan Kalijaga), 2(3), 167. https://doi.org/10.14421/jiska.2018.23-06
Wicaksono A. E. (2016). Implementasi Data Mining Dalam Pengelompokan Peserta Didik di Sekolah untuk Memprediksi Calon Penerima Beasiswa Dengan Menggunakan Algoritma K-Means (Studi Kasus SMA N 6 Bekasi). Jurusan Teknik Informatika, Universitas Gunadarma, 21(3), 206–216. http://dx.doi.org/10.35760/tr.2016.v21i3.1599
Sindhu R., Nandal R., Dhamija P., & Sehrawat H. K. (2017). A Review On K-Mean Algorithm And It’s Different Distance Matrics. International Journal of Engineering and Technology, 9(2), 1423–1430. https://doi.org/10.21817/ijet/2017/v9i2/170902227
Jaroji J., Danuri D., & Putra Fajri P. (2016). K-Means Untuk Menentukan Calon Penerima Beasiswa Bidik Misi Di Polbeng. INOVTEK Polbeng - Seri Informatika, 1(1), 87–94. https://doi.org/10.35314/ISI.V1I1.129
Raval U. R., & Jani C. (2016). Implementing & Improvisation of K-means Clustering Algorithm. International Journal of Computer Science and Mobile Computing, 55(5), 191–203. Retrieved from http://www.ijcsmc.com/docs/papers/May2016/V5I5201647.pdf
Yunita (2018). Penerapan Data Mining Menggunkan Algoritma K-Means Clustring Pada Penerimaan Mahasiswa Baru (Studi Kasus : Universitas Islam Indragiri). Jurnal Sistemasi, 7(September), 238–249. https://doi.org/10.32520/stmsi.v7i3.388
Abdullah H. A, (2018). Improve the Performance of K-Means by Using Genetic Algorithm for Classification Heart Attack. International Journal of Electrcal and Computer Engineering (IJECE), 2088-8708, https://doi.org/10.11591/ijece.v8i2.pp1256-1261
Lubis H. A. (2016). Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Berbasis Customer Relantionship Management. Jurnal Sistemasi, 7(September), 238–249.
Purba W., Tamba S., & Saragih J. (2018). The effect of mining data k-means clustering toward students profile model drop out potential. Journal of Physics: Conference Series, 1007(1). https://doi.org/10.1088/1742-6596/1007/1/012049
Khade S., Pandhare P., Navale S., Patil K., & Gaikwad V. (2016). Fruit Quality Evaluation Using K-Means Clustering Approach. International Journal of Advances in Science Engineering and Technology, 4(2). 2321-9009.
Haribabu T., & Raju, I. (2019). An Optimal Approach of Initial Centroid Selection for Effective Clustering. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(5), 2278-3075
Ismi D. P., Panchoo, & S., Mrinto. (2016). K-Means Clustering Based Filter Feature Selection on High Dimensional Data. International Journal of Advances in Intellegent Informatics. 38-45, http://dx.doi.org/10.12928/ijain.v2il.54
Metisen B. M., & Sari H. L. (2016). Analisis Clustering Menggunakan Metode K-Means dalam Pengelompokan Penjualan Produk pada Swalayan Fadhila. Jurnal Media Infotama, 11(2). 1858-2680
