Recommendation System for Major University Determination Based on Student’s Profile and Interest

Abstract

Failure study on university students is one of the serious problems we face today. Data from the Centre for Education Statistics Research and Development of the Ministry of National Education Republic of Indonesia showed that the percentage of students graduate on time from 2001 to 2011 only reached 51.97%. In addition, cases of students dropping out at the beginning of the semester is also quite significant. One of the causes of failure of this study was the selection of major’s errors when applying to university. This study offers a selection subject recommendation system that builds on the profile data and student’s interest using the technique of Association Rule. Results of the rules of the relationship will then be matched with prospective students using questionnaires dynamic, so expect new students get recommendations more valid subject fit the profile and interest respectively. The system built on this research utilizes student data stored on the academic system of Dian Nuswantoro University. This model however can be adapted by all the universities that has a system of academic information. At the end of this system is expected to be used to minimize failures caused students study majors election mistakes

Prediksi Kecocokan Jurusan Siswa SMK Dengan Support Vector Machine dan Random Forest

“…Penelitian selanjutnya adalah membahas educational data mining adalah tentang analisa perbandingan model association rule dengan classification [12]. Penelitian yang terakhir membahas sistem rekomendasi pemilihan jurusan pada universitas berdasarkan profil dan minat siswa dengan metode asosiasi [13]. Perbedaan penelitian ini dengan penelitian sebelumnya adalah penelitian ini mencoba mengkomparasikan kemampuan dua algoritma dalam melakukan klasifikasi data siswa berdasarkan kecocokan pemilihan jurusannya.…”

Validation of the Early University Dropout Intentions Questionnaire (EUDIQ-R)

“…Sixth, it incorporated the findings from Kusumaningrum et al (2017), who considered a series of indicators about students' interest in the course they were doing. Appropriate course selection by students is essential for the students to find the knowledge that they are going to acquire during their degrees interesting and useful.…”

Thesis Supervisor Recommendation with Representative Content and Information Retrieval

“…However, several reports [1], [2] show that the two factors are challenging to achieve since the number of the newly enrolled students is often two times higher than the number of graduating students (which are 1,233,218 and is 641,098 respectively). Despite all the efforts, the number of graduating students has increased only slightly over the years [3].…”