Dimensional Data Unsupervised Learning Using an Analytic Hierarchy Process in Determining Attributes in the Classification Algorithm


  • Shinta Ayunda Putri * Mail Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
  • Mustakim Mustakim Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
  • (*) Corresponding Author
Keywords: BPNN; PNN; K-Means Clustering; Classification

Abstract

Systematic keyword is needed in improving the quality of higher education, one of which is the needed to increase the competence of graduates every year. In increasing student graduation, it is necessary to classify student graduation to find out whether the student is said to be on time (TW) or possibility on time (KTW) using the BPNN and PNN methods. The data used is the Alumni data of the 2013-2020 Information System  study program with 7 criteria use, namely GPA, Total Credits, Number of Repetitive Courses, Taking TA Curse in Semester 7, Procrastination, Self-Confidence, and Discipline. The data obtained is then carried out in the process of sharing training data and testing data using K-Means Clustering with the aim; of getting the best accuracy results. Furthermore, the classification stage using BPNN and PNN resulted in an accuracy of 98% and 95% with learning rate of 0.125 and a spread value of 0.1

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Article History
Submitted: 2022-06-23
Published: 2022-06-30
Abstract View: 61 times
PDF Download: 46 times
How to Cite
Putri, S., & Mustakim, M. (2022). Dimensional Data Unsupervised Learning Using an Analytic Hierarchy Process in Determining Attributes in the Classification Algorithm. Building of Informatics, Technology and Science (BITS), 4(1), 235−240. https://doi.org/10.47065/bits.v4i1.1752
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