Skripsi

PENGENALAN KEPRIBADIAN MELALUI TULISAN TANGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK DENGAN LS CLASSIFIERS.

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Penilaian

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A person's handwriting is different and unique, even though it looks similar it is certainly not the same as someone else's writing. One's personality traits can be identified based on handwriting. One of the implementations is (handwriting recognition). To identify a person's personality, it can be classified by handwriting using the 'Graphology' field. The computational system to identify handwritten images can use the Convulution Neural Network method. Using the CNN method is expected to produce good accuracy with a low error rate. The CNN method is able to predict a person's personality through manuscripts as images. In addition, to increase the diversity of classifications, the Least Squared Classifiers method is needed. . LS Classifiers are designed to increase the variety of CNN methods in feature extraction and classification. The LS Classifier method is a classification method that estimates the w parameter vector and takes the best linear classifier based on the w parameter vector. Research has functions for users, including to find out someone's personality, especially extrovert and introvert personality. In this study CNN serves as Feature Extraction to classify Image and Ls Classifiers serves to increase diversity into 2 personality groups. The level of accuracy of the performance of the CNN & Ls Classifiers method in carrying out feature extraction and classification of handwritten images in determining personality has a good level of accuracy. Keywords: Handwriting, CNN, Ls Classifiers, Graphology, classification

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307006573	T130955	T1309552023	Central Library (Referens)	Available

Detail Information

Series Title

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Call Number

T1309552023

Publisher

Palembang : Prodi Magister Ilmu Komputer, Fakultas Ilmu Kom puter Universitas Sriwijaya., 2023

Collation

xvi, 65 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.707

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Prodi Magister Ilmu Komputer
Data dalam sistem-sistem komputer

Specific Detail Info

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Statement of Responsibility

MURZ

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