MULTIPLE LINEAR REGRESSION ANALYSIS WITH SPATIAL FACTOR TOWARDS SUBSIDY APPLICANT STUDENTS’ ECONOMIC DATA

• Main Author: Gunawan, Michael
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/47707
• Institution: Institut Teknologi Bandung

Education is one important thing that is the right of all people. Education itself can be formal or non-formal education. For formal education, there are various levels of education, one of which is undergraduate education obtained at university, both public and private. Each university has a different system related to operational funding, one of which is the UKT system or Uang Kuliah Tunggal. The majority of State Universities (SU) have a cross-subsidized UKT system so that students who are deemed capable of helping students who are deemed incapacitated, are also supplemented by a government budget. To get this help, there are 2 paths that can be used namely the Bidik Misi pathway and the Non Bidik Misi pathway. Each State Universities also has its own criteria in determining which students are entitled to the assistance. In this research, a case study for one of the state universities in West Java will be carried out which considers 4 categories based on the status of residence: Self-Owned Homes, House Rentals, Hitchhike and Official Houses. For each category also has different weights for each variable considered namely NJOP, Electric Power and PKP. However, in this study we want to add a new variable that is the distance variable, which is the distant between the origin province and the university, to find out whether the distance variable can also influence the calculation of UKT and hopes to produce a better model. Using multiple regression analysis by considering the possible combination of models and found that for the Bidik Misi category, there are 2 new models that take into account the distance variables in the category of Self-Owned Homes. Meanwhile, for the Non Bidik Misi category, there are 2 new models from the category of Self-Owned Houses and 3 new models from the category of Rental Houses that consider distance variables. It is hoped that the discovery of this new model can be taken into consideration in the future to use a model that also takes into account distance variables with weight adjustments that still lie in the interval of parameter interpretation. there are 2 new models from the category of Self-Owned Houses and 3 new models from the category of Rental Houses that calculate the distance variable. It is hoped that the discovery of this new model can be taken into consideration in the future to use a model that also consider distance variables with weight adjustments that still lie in the interval of parameter interpretation