Implementation of Product Sales Forecast Using Artificial Neural Network Method

Cholid Fauzi(1*), Aly Dzulfikar(2),

(1) Politeknik Negeri Bandung
(2) Universitas Nurtanio
(*) Corresponding Author

Abstract


Product sales forecasting is used by companies to estimate or predict future sales levels using sales data in the previous year. The Artificial Neural Network Backpropagation Algorithm can forecast the sales of goods for the next period for each item in the company. The forecasting process begins by determining the variables needed in the network pattern, and then the established network pattern continued in the network training process using the backpropagation algorithm. After carrying out the network training process, the researcher comparisons with several network patterns formed. This research was conducted to discuss the forecasting analysis of PT XYZ products on spiral and leaf springs. Forecasting carried out on Toyota 48210-25290 R3 type leaf springs using the Artificial Neural Network Backpropagation method with a learning rate weight value of 0.1 hidden layers four and an error of 0.01. From the data processing analysis that has been carried out based on the weight parameters selected, the prediction of sales in April.

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References


Pebrianasari, Edy, dkk, 2015, “Analisis Pengenalan Motif Batik Pekalongan Menggunakan Algoritma ackpropagation”, Techno.COM Universitas Dian Nuswantoro.Shazmeen, S.F.,

Fausett, L, 1994, "Fundamental of Neural Network, Architecture, Algorithms, and Applications", Prentice-Hall.

Sharma, Nijhawan, 2015, "Rainfall Prediction Using Neural Network",

International Journal of Computer Science Trends and Technology 3(3): 65-69.

Siang, J. J, 2009, “Artificial Neural Network dan Pemogramannya Menggunakan MATLAB”, ANDI.

Baig, M.M.A. & Pawar, M.R., 2013. Performance Evaluation of Different Data Mining Classification Algorithm and Predictive Analysis. IOSR Journal of Computer Engineering, 10(6), pp.1-6.

Jain, V., Narula, G.S. & Singh, M., 2013. Implementation of Data Mining in Online Shopping System using Tanagara Tool. International Journal of Computer Science and Engineering, 2(1), pp.47-58.

Sahu, H., Shrma, S. & Gondhalakar, S., 2011. A Brief Overview of Data Mining Survey. International Journal o¬¬f Computer Technology and Electronics Engineering, 1(3), pp.114-21.

Lee, Choi, 2013, "A Multi-Industry Bankruptcy Prediction Model using Back-Propagation Neural Network and Multivariate Discriminant Analysis", Expert Systems with Applications 40(8): 2941-2946

Subagyo, Pangestu, 1986, “Forecasting Konsep dan Aplikasi”, BPFE UGM Yogyakarta.

Redjeki, Sri, 2013, “Perbandingan Algoritma Backpropagation dan K-Nearest Neighbor (K-NN) untuk Identifikasi Penyakit”, Seminar Nasional Aplikasi Teknologi Informasi STMIK AMIKOM.

Makridakis, Spyros, dkk, 1993, ”Metode Dan Aplikasi Peramalan Edisi ke II”. Erlangga

Park, Kang,2007, "Prediction of Fatigue Life for Spot Welds using Back-Propagation Neural Networks."

Prasetyo, E, 2014, “Data Mining Mengolah Data Menjadi Informasi Menggunakan Matlab”

Sommerville, Ian, 2011, "Software Engineering", Pearson.

Sugiyono, 2014, “Metode Penelitian Pendidikan Pendekatan Kuantitatif, Kualitatif, dan R&D, Alfabeta

Sugiyono, 2014, “Metode Penelitian Pendidikan Pendekatan Kuantitatif, Kualitatif, dan R&D




DOI: https://doi.org/10.30645/ijistech.v5i2.126

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