Classification of Wine Types Based on Composition Using Backpropagation And Particle Swarm Optimization

Kelvin Herwanda Tandrio, Kartini Aprilia Pratiwi Nuzry,, Yovi Prasetyo Ardi,, Heri Prasetyo

Abstract

This paper presents a technique for classifying types of wine using Neural Network Back Propagation (NNBP). In this research also developed Neural Network technique with Particle Swarm Optimization (PSO) algorithm to calculate neural weight and bias. In the experimental results, the use of PSO-based NNBP or Neural Network Particle Swarm Optimization (NNPSO) yielded better results than the classic NNBP method. This clearly shows that the classification of types of wine with NNPSO can classify all types of wine. Meanwhile, NNBP techniques can only classify one type of wine. With the Mean Square Error (MSE) limit of 0.2, the accuracy obtained with NNPSO is about 75% - 90% and NNBP around 25 - 45%.

Keywords

Wine; Data Mining; Backpropagation; Particle Swarm Optimization

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DOI: https://doi.org/10.20961/itsmart.v7i1.20170

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