Determination of Initial K-means Centroid in the Process of Clustering Data Evaluation of Teaching Lecturers
Penentuan Centroid Awal K-means pada Proses Clustering Data Evaluasi Pengajaran Dosen
Abstract
Decision making about microteaching for lecturers in ITTP with the low teaching quality is only based on three lowest order from teaching values. Consequently, the decision is imprecise, because there is possibility that the lecturers are not three. To get the precise quantity, an analysis is needed to classify the lecturers based on their teaching values. Clustering is one of analyses that can be solution where the popular clustering algorithm is k-means. In the first step, the initial centroids are needed for k-means where they are often randomly determined. To get them, this paper would utilize some preprocessing, namely Silhouette Density Canopy (SDC), Density Canopy (DC), Silhouette (S), Elbow (E), and Bayesian Information Criterion (BIC). Then, the clustering results by using those preprocessing were compared to obtain the optimal clustering. The comparison showed that the optimal clustering had been given by k-means using Elbow where obtain four clusters and 0.6772 Silhouette index value in dataset used. The other results showed that k-means using Elbow was better than k-means without preprocessing where the odds were 0.75. Interpretation of the optimal clustering is that there are three lecturers with the lower teaching values, namely N16, N25, and N84.
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References
Nurzahputra A., Pratana A. R., Puwinarko A., 2017. Sistem Pendukung Keputusan Pemilihan line-up Pemain Sepak Bola Menggunakan Metode Fuzzy Multiple Attribute Decision Making dan K-means Clustering. Jtsiskom, 5(3),pp. 106-109.
Cerah T. P. N., Nurhayati O. D., Isnanto R. R., 2019. Perbandingan Metode Segmentasi K-means Clustering dan Segmentasi Region Growing untuk Pengukuran Luas Wilayah Hutan Mangrove. Jtsiskom, 7(1),pp. 31-37.
Ananda R., Burhanuddin A., 2019. Analisis Mutu Pendidikan Sekolah Menengah Atas Program Ilmu Alam di Jawa Tengah dengan Algoritme K-means Terorganisir. Inista, 2(1), pp. 065-072.
Kodinariya T. M., Makwana P. R., 2013. Reviewer on Determining Number of Cluster in K-means Clustering. International Journal of Advance Research in Computer Science and Management Studies, 1(6), pp. 90-95.
Widiyanti, W., 2017. Sentroid awal metode k-means dengan pendekatan metode berhirarki dalam pengelompokkan provinsi di Indonesia. Skripsi, Indonesia: Institut Pertanian Bogor.
Zhou R., Zhang S., Chen C., Ning L., Zhang Y., 2016. A distance and density-based clustering algorithm using automatic peak detection, 2016 IEEE International Conference on Smart Cloud. New York, 18-20 Nov 2016, IEEE.
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.
Yan H., Lu Y., Ma H., 2018. Density-based Clustering Using Automatic Density Peak Detection, In Proceedings of the 7th International Conference on Pattern Recognition Applicaions and Methods (ICPRAM 2018), China.
Zhang G., Zhang C., Zhang H., 2018. Improved K-means Algorithm Based on Density Canopy. Journal Knowledge-based Systems, 145, pp. 289-297.
Luka S. S. P., Candiasa I. M., Aryanto K. Y. E., 2019. Analisis Pembentukan Kelompok Diskusi Panel Siswa Menggunakan Algoritma Fuzzy C-means dan K-means. Jurnal Pendidikan Teknologi dan Kejuruan, 16(2), pp. 267-277.
Ananda R., 2019. Silhouette Density Canopy K-means for Mapping the Quality of Education Based on the Results of the 2019 National Exam in Banyumas Regency. Jurnal Khazanah Informatika, 5(2), pp. 158-168.
Li M., Wang H., Long H., Xiang J., Wang B., Xu J., Yang J., 2019. Community Detection and Visualization in Complex Network by the Density-Canopy-Kmeans Algorithm and MDS Embedding, IEEE Access, 20 August 2019, IEEE.
Ananda R., Naf’an M. Z., Arifa A. B., Burhanuddin A., 2020. Sistem Rekomendasi Pemilihan Peminatan Menggunakan Density Canopy K-means. Jurnal Resti, 4(1), pp. 172-179.
Johnson R. A., Wichern D. W., 2007. Applied Multivariate Statistical Analysis. 6th ed. New York (US): Pearson Education.
Romanuke V. V., 2018. Optimization of A Dataset For A Machine Learning Task by Clustering and Selecting Closest-To-The-Centroid Objects,. Вісник Хмельницького національного університету, 1(6), pp. 263-265.
Ali N., Hamilton N., Calaf M., Cal R. B., 2019. Classification of the Reynolds Stress Anisotropy Tensor in Very Large Thermally stratified Wind Farms Using Colormap Image Segmentation. Journal of Renewable and Sustainable Energy, 11, pp. 1-9.
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. Amerika, January 2011.
Rendon E., Abudez I., Arizmendi A., Quiroz E. M., 2011. Internal Versus External Cluster Validation Indexes. International Journal of Computers and Communications, 5(1), pp. 27-34.
Khairati A. F., Adlina A. A., Hertono G. F., Handari B. D., 2019. Kajian Indeks Validitas pada Algoritma K-Means Enhanced dan K-Means MMCA, Prosiding Seminar Nasional Matematika (PRISMA). Semarang, February 2018.
Baarsch J., Celebi M. E., 2012. Investigation of Internal Validity Measures for K-means Clustering, In Proceedings of the IMECS. March 14 – 16, 2012, Hong Kong.
Munthe A. D., 2019. Penerapan Clustering Time Series untuk Menggerombolkan Provinsi di Indonesia Berdasarkan Nilai Produksi Padi. Jurnal Litbang Sukowati, 2(2), pp. 1-11.
Suprihatin, Utami Y. R. W., Nugroho D., 2019. K-Means Clustering untuk Pemetaan Daerah Rawan Demam Berdarah. Jurnal TIKomSIN, 7(1), pp. 8-16, 2019.
Vendramin L., Campello R. J. G. B., Hruschka E. R., 2009. On the Comparison of Relative Clustering Validity Criteria, in Proceedings of the 2009 SIAM International Conference on Data Mining. April 30 – May 2, 2009, Nevada, USA
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