PENERAPAN JARINGAN SARAF TIRUAN STANDARISASI HARGA BARANG DISTRIBUTOR DI MEDAN DENGAN ALGORITMA LAVENBERG MARQUARDT

Supriyanti Matanari, Rivalri Kristianto Hondro

Abstract


The Medan Central Bureau of Statistics is tasked with surveying data or calculating quality statistical data and information which includes accuracy of relevance and also for price standardization or determination. The problem in setting the price standard is because many companies raise prices without surveying data first and some other companies do not adjust the price of one to another. To handle this, one method is needed to study and analyze Distributor Item Price data to be uniform, using knowledge of artificial neural network analysis using the Levenberg-Marquardt method. With this activity the Medan Central Bureau of Statistics hopes that the company in an effort to increase customer satisfaction with the products produced can be done by producing a product or item with high quality and quality with balanced pricing in accordance with the conditions in the market (adjusted to community income). The results obtained through this study are the uniformity of the prices of distributor goods in the market, which have previously been through the process of testing and applying mathematically statistically using matlab software.

Keywords: Artificial Neural Networks, Levenberg-Marquardt Method, Distributor Prices, MATLAB

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References


S. Kumar and S. P. Mahulikar, “Reconstruction of aero-thermal heating and thermal protection material response of a Reusable Launch Vehicle using inverse method,†Appl. Therm. Eng., 2016.

R. N. Hidayat, R. R. Isnanto, and O. D. Nurhayati, “Implementasi Jaringan Syaraf Tiruan Perambatan Balik untuk Memprediksi Harga Logam Mulia Emas Menggunakan Algoritma Lavenberg Marquardt,†J. Teknol. dan Sist. Komput., 2013.

R. Kumalasari, W. Srigutomo, M. Djamal, I. Meilano, and H. Gunawan, “Location of Sinabung volcano magma chamber on 2013 using lavenberg-marquardt inversion scheme,†in Journal of Physics: Conference Series, 2018.

S. Kumar and S. V. Barai, “Neural networks modeling of shear strength of SFRC corbels without stirrups,†Appl. Soft Comput. J., 2010.

B. M. Wilamowski, Y. Chen, and A. Malinowski, “Efficient algorithm for training neural networks with one hidden layer,†Proceedings of the International Joint Conference on Neural Networks. 1999.

K. Benmahdjoub, Z. Ameur, and M. Boulifa, “Forecasting of rainfall using Time Delay Neural Network in Tizi-Ouzou (Algeria),†in Energy Procedia, 2013.

T. N. Nguyen and H. T. Nguyen, “Neural network decoupling technique and its application to a powered wheelchair system,†in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2015.

B. M. Wilamowski, Y. C. Y. Chen, and A. Malinowski, “Efficient algorithm for training neural networks with one hiddennlayer,†IJCNN’99. Int. Jt. Conf. Neural Networks. Proc. (Cat. No.99CH36339), 1999.

E. Agyei and K. Hatfield, “Enhancing gradient-based parameter estimation with an evolutionary approach,†J. Hydrol., 2006.

Z. Tian, B. Gu, L. Yang, and F. Liu, “Performance prediction for a parallel flow condenser based on artificial neural network,†Appl. Therm. Eng., 2014.

Z. Gao, S. Zhong, W. Zhang, and Y. Zhu, “A high-precision calibration technique for laser measurement instrument and stereo vision sensors,†in 2007 8th International Conference on Electronic Measurement and Instruments, ICEMI, 2007.

A. S. Fakharudin, M. N. Sulaiman, and N. Mustapha, “Modelling of biogas production from banana stem waste with neural networks learning strategies to optimse the production,†J. Theor. Appl. Inf. Technol., 2017.

S. S. Priya and M. H. Iqbal, “Solar Radiation Prediction using Artificial Neural Network,†Int. J. Comput. Appl., 2015.

H. M. Kamar, R. Ahmad, N. B. Kamsah, and A. F. Mohamad Mustafa, “Artificial neural networks for automotive air-conditioning systems performance prediction,†Appl. Therm. Eng., 2013.

R. Alimardani, Y. Abbaspour-Gilandeh, A. Khalilian, A. Keyhani, and S. H. Sadati, “Prediction of draft force and energy of subsoiling operation using ANN model,†J. Food, Agric. Environ., 2009




DOI: https://doi.org/10.30865/komik.v2i1.960

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