Prediksi Penjualan Pada Data Penjualan Perusahaan X Dengan Membandingkan Metode GRU, SVR, DAN SARIMAX
Keywords:
APRIORI, APRIORI-SD, Subgroup discoveryAbstract
Sales forecasting is an attempt to predict sales using several methods, such as statistical methods, machine learning, and others. Sales forecasts are considered important because poor forecasts can have an impact on the company's inventory so that it can cause storage of too much or too little goods, causing the company to lose. Therefore, we need a model that can predict sales so that companies can plan before filling stock. However, forecasts cannot be done directly, because a company's sales data is definitely influenced by various factors and sales last month are not always the same as in the future, so external data is needed in predicting future sales. Therefore, in this thesis a prediction will be made using 3 models, namely the GRU, SVR and SARIMAX models with the help of external data in the form of CPI data and inflation data. In addition, this thesis also conducted a correlation test to determine whether the sales data to be predicted has significance/relationship with external data so that it helps in predicting sales data. The results obtained from this study are that pot data is more suitable for using univariate data with the GRU model, with RMSE Train 3.22, RMSE Test 2.93. For hanger and sealware data, the best model for prediction is the SARIMAX model with univariate data type (RMSE 30.43) and multivariate data type (RMSE 8.07).References
[1] Arunraj, N. S., Ahrens, D., & Fernandes, M. 2016.
Application of SARIMAX model to forecast daily sales in
food retail industry. International Journal of Operations
Research and Information Systems (IJORIS), 7(2), 1-21. DOI
= 10.4018/IJORIS.2016040101
[2] Bohanec, M., Robnik-Šikonja, M., & Borštnar, M. K. 2017.
Organizational learning supported by machine learning
models coupled with general explanation methods: A Case of
B2B sales forecasting. Organizacija, 50(3), 217-233. DOI =
10.1515/orga-2017-0020
[3] Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. 2014.
Empirical evaluation of gated recurrent neural networks on
sequence modeling. arXiv preprint arXiv = 1412.3555.
[4] Meighani, H. M., Ghotbi, C., Behbahani, T. J., & Sharifi, K.
2018. Evaluation of PC-SAFT model and Support Vector
Regression (SVR) approach in prediction of asphaltene
precipitation using the titration data. Fluid Phase Equilibria,
456, 171-183. DOI = 10.1016/j.fluid.2017.10.022
[5] Park, H., & Hwang, S. 2021. Demand forecasting of micro
mobility using a gated recurrent unit. DOI =
10.22712/susb.20210014
[6] Pongdatu, G. A. N., & Putra, Y. H. 2018, August. Seasonal
time series forecasting using SARIMA and Holt Winter’s
exponential smoothing. In IOP Conference Series: Materials
Science and Engineering (Vol. 407, No. 1, p. 012153). IOP
Publishing. DOI = 10.1088/1757-899X/407/1/012153
[7] Rahman, M. O., Hossain, M. S., Junaid, T. S., Forhad, M. S.
A., & Hossen, M. K. 2019. Predicting prices of stock market
using gated recurrent units (GRUs) neural networks. Int. J.
Comput. Sci. Netw. Secur, 19(1), 213-222. DOI =
10.17577/IJERTV9IS050550
[8] Ramos, P., Santos, N., & Rebelo, R. 2015. Performance of
state space and ARIMA models for consumer retail sales
forecasting. Robotics and computer-integrated manufacturing,
34, 151-163. DOI = 10.1016/j.rcim.2014.12.015
[9] Sari, N. Z. A. 2019. Pengaruh Pendapatan Perkapita dan IHK
Terhadap Konsumsi Masyarakat di Kota Banjarmasin. JIEP:
Jurnal Ilmu Ekonomi dan Pembangunan, 1(1). DOI =
10.20527/jiep.v1i1.1129
[10] Singh, A., Kotiyal, V., Sharma, S., Nagar, J., & Lee, C. C.
2020. A machine learning approach to predict the average
localization error with applications to wireless sensor
networks. IEEE Access, 8, 208253-208263.
[11] Singh, A., Gaurav, K., Rai, A. K., & Beg, Z. 2021. Machine
learning to estimate surface roughness from satellite images.
Remote Sensing, 13(19), 3794. DOI = 10.3390/rs13193794
