AKADEMIK DATA MINING (ADM) K-MEANS DAN K-MEANS K-NN UNTUK MENGELOMPOKAN KELAS MATA KULIAH KOSENTRASI MAHASISWA SEMESTER AKHIR


Suhardi Rustam(1*); Haditsah Annur(2);

(1) Universitas Ichsan Gorontalo
(2) Universitas Ichsan Gorontalo
(*) Corresponding Author

  

Abstract


University as an educational institution plays an important role in producing graduates. In addition, institutions such as universitas ichsan Gorontalo save the data set. These Data include about student academic data.In the academic field, every semester, increasing the amount of data recorded with data from academic activities. It is like there is a Tsunami of data which indicate that these data are very abundant but do not give any knowledge that is not beneficial to the university, especially the faculty except the knowledge administrative. Universitas ichsan Gorontalo with the number of students reached 9000 people which is accompanied by the number of graduates is still less than ideal any period graduate, it is necessary to apply the pattern determination grade concentration courses effective for the achievement ability of students, academic Data will be used namely the data of the students 2016-2017 who has taken class subjects concentration. The application of K-Means algorithm and K-Means KNN where K=2 result in a cluster for grouping of a Class Focus on the students semester end and each cluster has a predictive value for the second klustering such, the Value of the resulting Accuracy of Algorithms KNN, namely the AUC (Area Under The Curve) =1, the Value of CA=1, the value of F1=1, the value of the precision=1 and recall=1, and the value of accuracy as the best value.


Keywords


Academic Data Mining; K-Means; K-NN; AUC accuracy

  
  

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doi  https://doi.org/10.33096/ilkom.v11i3.487.260-268
  

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References


Agarwal, S., Data mining: Data mining concepts and techniques. Proceedings - 2013 International Conference on Machine Intelligence Research and Advancement (ICMIRA 2013), https://doi.org/10.1109/ICMIRA.2013.45, 2014.

Banjarsari, M. A., Budiman, I., & Farmadi, A. "Penerapan K-Optimal Pada Algoritma Knn Untuk Prediksi Kelulusan Tepat Waktu Mahasiswa Program Studi Ilmu Komputer Fmipa Unlam Berdasarkan Ip Sampai Dengan Semester 4." Klik - Kumpulan Jurnal Ilmu Komputer, 2(2), 159–173. https://doi.org/10.20527/KLIK.V2I2.26, 2018.

Nur, F., Fauzan, R., Aziz, J., Setiawan, B. D., & Arwani, I., "Implementasi Algoritma K-Means untuk Klasterisasi Kinerja Akademik Mahasiswa", Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(6), 2243–2251, 2018.

Pradnyana, G. A., Aan, A., & Permana, J., "Perbandingan Algoritma K-Means dan Hybrid K-Means KNN untuk Pembagian Kelas Kuliah Mahasiswa", Senari, 1–8, 2017.

I. G. Ayu and D. Saryanti, “Penerapan Teknik Clustering untuk Pengelompokan Konsentrasi Mahasiswa dengan Metode K-Means" pp. 519–526, 2019.

T. A. Munandar and W. O. Widyarto, “Clustering Data Nilai Mahasiswa Untuk Pengelompokan Konsentrasi Jurusan Menggunakan Fuzzy Cluster Means,” Semin. Nas. Apl. Teknol. Inf., pp. 1907–5022, 2013.

Widodo and D. Wahyuni, “Implementasi algoritma K-Means clustering untuk mengetahui bidang skripsi mahasiswa multimedia pendidikan teknik informatika dan komputer universitas negeri jakarta,” J. Pint., vol. 01, No. September, p. 11 pages, 2018.

A. Rizky and F. Amiq, “Penerapan Metode Clustering Dengan Algoritma K- Means Untuk Rekomendasi Pemilihan Jalur Peminatan Sesuai Kemampuan Pada Program Studi,” 2013.

P. S. Informatika, U. T. Yogyakarta, and S. P. "Keputusan Pada Universitas Swadaya Gunung Djati Menggunakan Metode K-Means Clustering" pp. 1–10.

K. N. Sistem, G. A. Pradnyana, A. Aan, J. Permana, and U. P. Ganesha, “Perancangan Sistem Pembagian Kelas Kuliah Mahasiswa dengan Kombinasi Metode K-Means dan K-Nearest,” pp. 285–290, 2017.

Rohman, A. (2015). Model Algoritma K-Nearest Neighbor (K-Nn) Untuk Prediksi Kelulusan Mahasiswa. Neo Teknika, 1(1). https://doi.org/10.1017/CBO9781107415324.004

Rustam, S., "Analisa Clustering Phising Dengan K-Means Dalam Meningkatkan Keamanan Komputer", ILKOM Jurnal Ilmiah, 10(2), 175. https://doi.org/10.33096/ilkom.v10i2.309.175-181, 2018.

Rustam, S., Santoso, H. A., & Supriyanto, C., "Optimasi K-Means Clustering Untuk Identifikasi Daerah Endemik Penyakit Menular Dengan Algoritma Particle Swarm Optimization Di Kota Semarang." ILKOM Jurnal Ilmiah, 10 (3), 251. https://doi.org/10.33096/ilkom.v10i3.342.251-259, 2018.

J. Han, Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems), 2011.

G. I. Marthasari, “Implementasi Teknik Data Mining untuk Evaluasi Kinerja Mahasiswa Berdasarkan Data Akademik,” Fountain Informatics J., vol. 2, no. 2, p. 20, 2017.


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