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

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2007000861	T33561	T335612020	Central Library (REFERENCES)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T335612020

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer., 2020

Collation

xviii, 85 hlm.; ilus., tab.: 28 cm

Language

English

ISBN/ISSN

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Classification

005.07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Prodi Teknik Informatika, Fakultas Ilmu Komputer
Data Akreditasi

Specific Detail Info

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Statement of Responsibility

TUTI

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