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The Use of Probabilistic Neural Network and ID3 Algorithm for Java Character Recognition

Satiabudhi, Gregorius and Adipranata, Rudy and Liliana, and Budhi, Gregorius Satia (2015) The Use of Probabilistic Neural Network and ID3 Algorithm for Java Character Recognition. In: The 2nd Management and Innovation Technology International Conference (MITiCON2015), 16-11-2015 - 16-11-2015, Bangkok - Thailand.

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        Abstract

        Java character is the character that is used especially in Java island, Indonesia. Java character has a special form of writing consist of basis characters, vowels, complementary, etc. In this research, the authors conducted Java character recognition using 2 methods, namely probabilistic neural network (PNN) and ID3 algorithm. PNN is a method of artificial neural network that can be trained supervised and unsupervised. PNN is built based on the theory of probability which is realized as an artificial neural network. This method is used because PNN has a high accuracy in the classification of data, also has high speed when performing the process. ID3 (Iterative Dichotomiser) is a decision tree algorithm. Basic ID3 algorithm using tree induction that gives attribute to the node in the tree based on how much information increases from the node. From experimental results, PNN method can achieve an accuracy up to 92.35% for data that has been trained previously, and up to 61.08 % for data hasn�t been trained before. While ID3 can achieve recognition rate of 100% for data has been trained before but only 15.57% for data hasn�t been trained before.

        Item Type: Conference or Workshop Item (Paper)
        Uncontrolled Keywords: Java character recognition, probabilistic neural network, ID3.
        Subjects: Q Science > QA Mathematics > QA76 Computer software
        Divisions: Faculty of Industrial Technology > Informatics Engineering Department
        Depositing User: Admin
        Date Deposited: 20 Apr 2016 00:28
        Last Modified: 23 Dec 2019 10:43
        URI: http://repository.petra.ac.id/id/eprint/17408

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