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PENERAPAN METODE LEARNING VECTOR QUANTIZATION (LVQ) UNTUK MENGIDENTIFIKASI PENYAKIT MATA MERAH VISUS NORMAL

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Penilaian

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Taking care and maintaining healty eyes are very impotant for human, because eyes are one of the senses that help human to do daily activities. Eyes that give visual information to human, cannot be separates for the threatof many eyes diseases. The diseases can attack from small to big scale. Unfortunately, eyes diseases are usually considered not to have such potential to harm human, so eyes health often to be ignored by people in general. Therefore, in this paper system to identify eyes diseases has been developed using Learning Vector Quantization (LVQ) method. This method can give classification to pattern that represent specific class, which will move to a nearer position to corresponding class when the classification data point is true. In this research, there are 17 symptoms and 4 eyes diseases that processed in training and testing processes, where the data were divided into training data and testing data. In training process, LVQ method did some stages to get final weight. The weight will be used in testing process. Using LVQ method, the resulted accuracy is 95%, with the value of precision, recall, and f1-score is 96,3%, 95% and 95,2% that means this system works fine, so it can be concluded that LVQ method can be used for eyes diseases identification. Keywords : identification, eyes diseases, Learning Vector Quantization

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207002909	T75458	T754582022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T754582022

Publisher

Inderalaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2022

Collation

xi, 74 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

003.107

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Jurusan Teknik Informatika
Identifikasi Sistem

Specific Detail Info

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Statement of Responsibility

SEPTA

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