PERBANDINGAN METODE SUPPORT VECTOR MACHINE DAN BACKPROPAGATION NEURAL NETWORK UNTUK MENGKLASIFIKASIKAN KEPRIBADIAN BERDASARKAN CITRA TULISAN TANGAN

IKHSAN, ZANEVA RAHMANDA and Samsuryadi, Samsuryadi and Rachmatullah, M Naufal (2022) PERBANDINGAN METODE SUPPORT VECTOR MACHINE DAN BACKPROPAGATION NEURAL NETWORK UNTUK MENGKLASIFIKASIKAN KEPRIBADIAN BERDASARKAN CITRA TULISAN TANGAN. Undergraduate thesis, Sriwijaya University.

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Abstract

Personality Classification based on handwriting images was not easy, because every handwriting has a unique pattern. Unique patterns in handwriting, such as the degree of slope and spacing between letters. This study aims to develop software that can classify personality based on writing using the support vector machine and backpropagation neural network method. This study uses 1,000 data with two distribution scenarios, the first is 60% training data and 40% test data, and the second is 70% training data and 30% test data. The data will go through preprocessing, segmentation, and feature extraction before entering the support vector machine and backpropagation neural network methods. Based on the test results, in the second scenario, the support vector machine method with the RBF kernel has an accuracy of 98%, and in the first scenario, the backpropagation neural network method has an accuracy value of 55%, so in conclusion, the performance of the support vector machine method with the RBF kernel is better than the backpropagation neural network method.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Support vector machine (SVM), Backpropagation neural network (BPNN), Handwriting image, Personality
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning Q Science > QA Mathematics > QA299.6-433 Analysis > Q334.A755 Artificial intelligence. Computational linguistics. Computer science. Q Science > QA Mathematics > QA8.9-QA10.3 Computer science. Artificial intelligence. Computational complexity. Data structures (Computer scienc. Mathematical Logic and Formal Languages
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Zaneva Rahmanda Ikhsan
Date Deposited:	19 Jan 2023 05:34
Last Modified:	19 Jan 2023 05:34
URI:	http://repository.unsri.ac.id/id/eprint/86846

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