Pengaruh Kepuasan Pelanggan, Manfaat, Presepsi Harga, Risiko, Terhadap Minat Beli Pada Situs Belanja Online (Studi Empiris Pada Mahasiswa Universitas Muhammadiyah Surakarta)


  • Tania Ilu Anastasia Universitas Muhammadiyah Surakarta
  • Agus Endro Suwarno Universitas Muhammadiyah Surakarta


National Exam, Data Science, Principle Component Analysis, Cluster


The Indonesian National Exams was a standard evaluation tool for students in junior high school to measure graduate competencies achievement on some subjects. It had been carried out for more than a decade based on government regulation number 19 of 2005 which was revised to number 19 of 2015. Additionally, it aimed to provide a quality map of Indonesian education. In accordance with the National Exam aim, the objectives of this research are for (1) knowing the result of visual analysis of National Exam average score (2) knowing which the most influential subjects to the National Exam average score (3) analyzing the National Exam score cluster. Data used in this research is National Exam average score from 2016 to 2019 taken from the Center for Educational Assessment of the Ministry of Education and Culture. The result shows that in general there is a statistical significant decrease on average score of National Exam of 2016 and the years after for all subjects. National Exam which was computer based and incorporated higher order thinking skill (hots) questions was suspected to contribute to the decline. Furthermore, after 2016 National Exam was no longer a vital factor to determine graduation so that its role was not as important as before. There are also differences in average National Exam score among regions. There were 7 provinces which their average score were below the national average score. The results of principle component analysis shows some subjects contribute most to the overall average score. Moreover, the result of cluster analysis confirms that there is still a disparity education quality between provinces in Indonesia. Therefore, the regular government policies to reduce this disparity are necessary.


Dwiatmoko, Ig. A., Prima Rosa, P.H. & Gunawan, R. (2015). Analisis Statistis Data Nilai Ujian Nasional dan Nilai Sekolah Menengah Atas di Daerah Istimewa Yogyakarta. Jurnal Widya Teknik 14 (2).

Miniarti, J. (2013). Pengelompokan Sekolah Menengah Pertama Berdasarkan Rata-Rata Nilai Ujian Akhir Nasional di Kota Binjai dengan Analisis Hierrarkhi Clustering. Skripsi Jurusan Matematika, FMIPA, Universitas Negeri Medan.

Ahmar, A.S.,Napitulu, D., Rahim, R., Hidayat, R., Sonata, Y. & Azmi, M. (2018). Using K-Means Clustering to Cluster Provinces in Indonesia. 2nd International Conference on Statistics, Mathematics, Teaching, and Research. IOP Conferencee Series 1028.

James, G., Witten, D., Hestie, T. & Tibshirani, R. (2017). An Introduction to Statistical Learning with Application in R. Springer, USA.

Stevencua , S., Setiawan, J. (2018). Data Visualization of Proverty Level at Provinces in Indonesian from The Year 2013-2015. International Journal of New Media Technology (JNMT) V(1).