Application of Product Moment Correlation and Complete Linkage Clustering Methods in Analyzing the Results of the Lecturer Questionnaire
Abstract
The use of questionnaires is needed by a lecturer to make improvements in the implementation of the teaching process in the classroom, so that it can correct deficiencies in the teaching process that has taken place. The results of the questionnaire cannot show the variables that must be corrected by a lecturer based on the item questionnaire. So we need a grouping of data for each variable in the results of the questionnaire. The clustering approach model cannot directly group a variable into an object to be clustered. This study clustered the variables on a questionnaire with the approach of Complete Lingkage Clustering by calculating the distance of the matrix using the Product Moment Corelation to find correlations for each questionnaire variable, so the cluster results obtained were several optimal clusters with the membership of each cluster variables from the questionnaire. Clustering data for questionnaire variables can be applied properly by applying product moment correlation to calculate the distance matrix. The cluster results can show the components of the questionnaire variables that must be corrected by the lecturer.
Keywords
Full Text:
PDFReferences
Agustian, H. (2018). TWO LEVEL CLUSTERING UNTUK ANALISIS KUESIONER AKADEMIK DI STTA YOGYAKARTA. Angkasa: Jurnal Ilmiah Bidang Teknologi, 10(1), 29-40.
Venkateswarlu, B., & Raju, P. G. (2013). Mine Blood Donors Information through Improved K-Means Clustering. arXiv preprint arXiv:1309.2597.
Bere, G. A., Tamtjita, E. N., & Kusumaningrum, A. (2016, November). Klasifikasi Untuk Menentukan Tingkat Kematangan Buah Pisang Sunpride. In Conference SENATIK STT Adisutjipto Yogyakarta (Vol. 2, pp. 109-113).
Suryono, S., Utami, E., & Luthfi, E. T. (2018). KLASIFIKASI SENTIMEN PADA TWITTER DENGAN NAIVE BAYES CLASSIFIER. Angkasa: Jurnal Ilmiah Bidang Teknologi, 10(1), 89-96.
Anisah, S., Honggowibowo, A. S., & Pujiastuti, A. (2016). Klasifikasi Teks Menggunakan Chi Square Feature Selection Untuk Menentukan Komik Berdasarkan Periode, Materi Dan Fisikdengan Algoritma Naivebayes. Compiler, 5(2).
Wibowo, A., & Honggowibowo, A. S. (2014). Sistem Pendukung Keputusan untuk Menentukan Lokasi Peternakan Ayam Broiler Dengan Metode Perbandingan Eksponensial dan Naive Bayes. Compiler, 3(2).
Sugiharti, E. (2016). ON-LINE CLUSTERING OF LECTURERS PERFORMANCE OF COMPUTER SCIENCE DEPARTMENT OF SEMARANG STATE UNIVERSITY USING K-MEANSALGORITHM. Journal of Theoretical & Applied Information Technology, 83(1).
Kaur, P. J. (2015, September). Cluster quality based performance evaluation of hierarchical clustering method. In Next Generation Computing Technologies (NGCT), 2015 1st International Conference on (pp. 649-653). IEEE.
Kurtz, A. K., & Mayo, S. T. (1979). Pearson Product Moment Coefficient of Correlation. In Statistical Methods in Education and Psychology (pp. 192-277). Springer, New York, NY.
Nazari, Z., Kang, D., Asharif, M. R., Sung, Y., & Ogawa, S. (2015, November). A new hierarchical clustering algorithm. In Intelligent Informatics and Biomedical Sciences (ICIIBMS), 2015 International Conference on (pp. 148-152). IEEE.
Redjeki, S. (2017). PEMODELAN PENGELOMPOKKAN PRESTASI DOSEN MENGGUNAKAN METODE FUZZY C-MEANS. JIKO (Jurnal Informatika dan Komputer), 2(2), 67-74.
Nurzahputra, A., Muslim, M. A., & Khusniati, M. (2017). Penerapan Algoritma K-Means Untuk Clustering Penilaian Dosen Berdasarkan Indeks Kepuasan Mahasiswa. Techno. Com, 16(1), 17-24.
DOI: http://dx.doi.org/10.28989/senatik.v4i0.194
Article Metrics
Abstract view : 298 timesPDF - 206 times
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Conference SENATIK P-ISSN :2337-3881 and E-ISSN : 2528-1666
Jumlah penggunjung = orang