
Penerapan Metode Principal Component Analysis (PCA) Untuk Identifikasi Faktor-Faktor yang Mempengaruhi Sikap Mahasiswa Memilih Melanjutkan Studi ke Kota Malang
Author(s) -
Fawaidul Badri,
Sulistya Umie Ruhmana Sari
Publication year - 2021
Publication title -
building of informatics, technology and science
Language(s) - English
Resource type - Journals
eISSN - 2685-3310
pISSN - 2684-8910
DOI - 10.47065/bits.v3i3.1139
Subject(s) - principal component analysis , reliability (semiconductor) , value (mathematics) , variance (accounting) , eigenvalues and eigenvectors , islam , mathematics , statistics , component (thermodynamics) , quality (philosophy) , psychology , econometrics , theology , business , physics , accounting , philosophy , power (physics) , quantum mechanics , thermodynamics
The future of a nation depends on how good the quality of education and human resources of the nation is. Higher education is an important part of the world of education that carries the responsibility in an effort to educate the nation's life. This study aims to determine the factors that influence student attitudes in choosing to continue their studies at Islamic Higher Education (PTAI). The background of this research is that there is a significant gap between PTN and PTAI enthusiasts and the lack of student interest in Islamic tertiary institutions is very interesting to be used as research study material to find out things that are considered by students in choosing PTAI. The results of the study indicate that the data used have met the assumption test of validity, reliability, adequacy and feasibility of the data so that it can be continued in the next analysis using factor analysis using PCA. All the variables contained in this study have an extraction value of more than 50%, so it can be concluded that all the variables used can explain these factors. In the "% of variance" column, because the specified eigenvalues is 1, then the value to be taken has eigenvalues greater than 1, there are component 1, component 2, component 3, component 4, and component 5. If you use 5 components then the total factors that can explain the variance are 20.011% + 19.692% + 15.935% + 14.632% + 10.745% = 81.015%