Evaluation of Automatic University Internal Quality Assurance System
Faisal Rahutomo, Muhammad Bisri Musthafa, Ngatmari, Cahya Rahmad, Rosa Andrie Asmara
State Polytechnic of Malang
Abstract
BAN-PT is an Indonesian body with the main task to assess the quality of Indonesian university. The assessment result is called accreditation, which has 5 years of validation time. In order to monitor the quality, University has an internal mechanism which is called an internal quality assurance system (SPMI in Indonesian). Usually, SPMI assesses the quality periodically, one or two times each year. This process needs much effort, i.e. time, manpower, and financial cost. Sometimes, internal auditor of the university does not have sufficient knowledge, as much as BAN-PT accessor. This condition causes a lack of assessment accuracy, then causes the quality of SPMI itself. In the other hand, University has abundant of condition data, saved in PDDIKTI database. This paper proposes to exploit the availability of data in this case. Therefore university able to monitor the quality by machine learning process periodically without much effort as manual SPMI process. Furthermore, this paper evaluates two machine learning methods, i.e. naive Bayes and KNN. This proposal exploits several data: student, academic, admission, and alumna. KNN and naive Bayes work in registrant and capacity ratio, student registration ratio, average student GPA in late five years, and on-time graduation scale. The experiment results show the accuracy of naive Bayes and KNN are 78.57% and 57.14% respectively.
Keywords: SPMI; PDDIKTI; KNN; naive Bayes
Topic: Computer Science and Engineering