ANALISIS REGRESI LOGISTIK BINER TERHADAP FAKTOR PENGARUH PRESTASI AKADEMIK MAHASISWA TADRIS MATEMATIKA IAIN PURWOKERTO

Abstract

Abstract: Learning achievement or academic achievement are things that cannot be separated from learning activities because they are the output of the learning process and are evidence of an individual's ability to carry out learning activities. This is in accordance with the definition of learning achievement is the result achieved from a learning process. Student academic achievement can be known after an evaluation is carried out and reflected in the form of a GPA or Grade Point Average (GPA). In general, if the IP or GPA achieved by students of Mathematics Tadris is less than 3 (three) is not as expected, whereas if the IP or GPA achieved 3 (three) or above 3 (three) is as expected. Learning achievement or academic achievement that can be achieved by students is influenced by many intelligence factors or abilities and external factors including economic factors and social environmental factors. Factors from outside the IAIN Purwokerto Mathematics Tadris students can be in the form of scholarships, schools, students or not, and students joining the organization or not when students are studying.
 Analysis of scholarship factors, school origin, students or not, and students following the organization or not on student academic achievement can be done using binary logistic regression. Based on binary logistic regression analysis the results showed that scholarship factors if categorized as students getting scholarships or not affecting academic achievement were only 0.2%, school-originating factors from the SMA and MA categories had an effect of 22.4%, high school or vocational category 81, 1%, MA or SMK category of 69.9%, MA or MAK category and SMK or MAK have no effect due to the absence of the IAIN Purwokerto Mathematics Tadris student from MAK, the factor of students staying in the cottage or not having an effect is 10.7% , and student factors follow the organization or not at 71.3%.
 Keywords: Binary Logistic Regression, Scholarship, School Origin, Education, Organization, Achievement.