PERBANDINGAN METODE LEARNING VECTOR QUANTIZATION DAN SELF ORGANIZING MAP PADA KLASIFIKASI DATA AKREDITASI

PUTRI, MAYANG HERMEILIZA EKA and Jambak, Muhammad Ihsan and Miraswan, Kanda Januar (2020) PERBANDINGAN METODE LEARNING VECTOR QUANTIZATION DAN SELF ORGANIZING MAP PADA KLASIFIKASI DATA AKREDITASI. Undergraduate thesis, Sriwijaya University.

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Abstract

School accreditation is carried out for self-evaluation and visitation to determine the feasibility of a school's performance. The results of accreditation can be used to determine the level of school eligibility compared to the national eligibility standards which are used as standard limits. Accreditation data research uses 8 independent variables, namely content standards, process standards, graduate competencies, educators & education personnel, facilities & infrastructure, management standards, financing, and assessment standards. The method used in this research is Learning Vector Quantization and Self Organizing Map. Tests were carried out five times with different data sharing, and the average accuracy results obtained in the Learning Vector Quantization method were 87% with a computation time of 1006 (ms), while the Self Organizing Map method obtained an average accuracy result of 61% with a relatively longer computation time of 2032 (ms). From these results, it can be concluded that the use of the Learning Vector Quantization method is better than the Self Organizing Map.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Data Akreditasi, Learning Vector Quantization (LVQ), Self Organizing Map (SOM)
Subjects: Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning
Divisions: 09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User: Mayang Hermeiliza Eka Putri
Date Deposited: 25 Aug 2020 04:43
Last Modified: 25 Aug 2020 04:43
URI: http://repository.unsri.ac.id/id/eprint/33561

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