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

References

Ariyanto, H. D., Hidayatulloh, F., & Murwono, J. (2013). Pengaruh penambahan gula terhadap produktivitas alkohol dalam pembuatan wine berbahan apel buang (Reject) dengan menggunakan Nopkor MZ. 11. Jurnal Teknologi Kimia dan Industri, 2(4), 226-232.

Hawusiwa, E. S., Wardani, A. K., & Ningtyas, D. W. (2014). PENGARUH KONSENTRASI PASTA SINGKONG (Manihot esculenta) DAN LAMA FERMENTASI PADA PROSES PEMBUATAN MINUMAN WINE SINGKONG [IN PRESS JANUARI 2015]. Jurnal Pangan dan Agroindustri, 3(1), 147-155.

Vamsidhar, E., Varma, K. V. S. R. P., Rao, P. S., & Satapati, R. (2010). Prediction of rainfall using backpropagation neural network model. International Journal on Computer Science and Engineering, 2(4), 1119-1121.

Widiastuti, N. A., Santosa, S., & Supriyanto, C. (2014). Algoritma Klasifikasi data mining naïve bayes berbasis Particle Swarm Optimization untuk deteksi penyakit jantung. Jurnal Pseudocode, 1(1), 11-14.

Nurmalasari, E. (2017). ALGORITMA PARTICLE SWARM OPTIMIZATION (PSO) UNTUK OPTIMASI NILAI CENTER RADIAL BASIS PROBABILISTIC NEURAL NETWORK (RBPNN) PADA KLASIFIKASI DATA BREAST CANCER. Jurnal Elektronik Nasional Teknologi dan Ilmu Komputer, 1(02).

NANIK, Rollyana Ajeng Ovihapsany Akhmad Mustofa, et al. KARAKTERISTIK MINUMAN BERALKOHOL DENGAN VARIASI KADAR EKSTRAK BUAH BIT (Beta vulgaris L.) DAN LAMA FERMENTASI. JITIPARI, 2018, 5.3.

Hardinata, J. T., Zarlis, M., Nababan, E. B., Hartama, D., & Sembiring, R. W. (2017, December). Modification Of Learning Rate With Lvq Model Improvement In Learning Backpropagation. In Journal of Physics: Conference Series (Vol. 930, No. 1, p. 012025). IOP Publishing.

Chai, S. S., Veenendaal, B., West, G., & Walker, J. P. (2008). Backpropagation neural network for soil moisture retrieval using NAFE’05 data: a comparison of different training algorithms. Int Archives Photogramm, Remote Sens Spatial Inf Sci (China), 37, 1345.

Widodo, P. P., & Handayanto, R. T. (2012). Penerapan Soft Computing Dengan Matlab. Bandung: Rekayasa Sains.

Siang, J. J. (2005). Jaringan syaraf tiruan dan pemrogramannya menggunakan Matlab. Penerbit Andi, Yogyakarta.

Jamous, R. A., Seidy, E., Tharwat, A. A., & Bayoum, B. I. (2015). Modifications of Particle Swarm Optimization Techniques and Its Application on Stock Market: A Survey. International Journal of Advanced Computer Science and Applications (IJACSA), 6(3).

Lee, K. E., bin Abdul Aziz, I., & bin Jaafar, J. Adaptive Multilayered Particle Swarm Optimized Neural Network (AMPSONN) for Pipeline Corrosion Prediction.

Suyanto, 2014. Algoritma Optimasi Deterministik atau Probabilitik. Penerbit : Graha Ilmu. Yogyakarta.

García-Gonzalo, E., & Fernández-Martínez, J. L. (2012). A brief historical review of particle swarm optimization (PSO). Journal of Bioinformatics and Intelligent Control, 1(1), 3-16

Eliantara, Felia., Imam Cholissodin dan Indriati. 2016. Optimasi Pemenuhan Kebutuhan Gizi Keluarga Menggunakan Particle Swarm Optimization.

Moghaddam, M. G., Ahmad, F. B. H., Basri, M., & Rahman, M. B. A. (2010). Artificial neural network modeling studies to predict the yield of enzymatic synthesis of betulinic acid ester. Electronic Journal of Biotechnology, 13(3), 3-4.

Bonyadi, M. R., & Michalewicz, Z. (2016). Analysis of stability, local convergence, and transformation sensitivity of a variant of the particle swarm optimization algorithm. IEEE Transactions on Evolutionary Computation, 20(3), 370-385.

Article Metrics

Abstract view : 92 times
PDF (Bahasa Indonesia) - 0 times

Refbacks

  • There are currently no refbacks.