Sales Transaction Result Analysis for Increase Prediction of Income

Sucipto Sucipto

Abstract

The use of data that has long stored in the information system can be utilized to know information that supports decision-making activities. One of them is sales transaction data. During this time, the case of sales transaction data is rarely analyzed to be taken into consideration when taking a decision, such as analyzing the transaction data. The prediction analysis of the transaction should analyze the inflation factors that affect earnings. It is predicted that inflation will lead to an increase in the price of basic necessities which can also lead to employers tend to raise the price of products to be sold. Therefore, this research focuses to analyze sales transaction data at a restaurant. Where the data used is data that has been stored for 3 years within the period 2015-2017. The purpose of this study is to determine the quality comparison of sales predictions of sales between years with regard to inflation based on changes in selling prices. The result of the study is known that the prediction of goods sold with actual data in the following year is quite significant and the prediction of income is not affected by the inflation based on price comparison.

Keywords

Transaction, Prediction, Inflation

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References

T. Vinayagathasan, “Inflation and economic growth: A dynamic panel threshold analysis for Asian economies,” J. Asian Econ., vol. 26, p. 31–41., 2013.

S. Gerlach and T. Peter, “Inflation targeting and inflation persistence in Asia–Pacific,” J. Asian Econ., vol. 24, no. 4, pp. 360–373, 2012.

S. Osmekhin and F. Déléze, “Application of continuous-time random walk to statistical arbitrage,” J. Eng. Sci. Technol. Rev., vol. 8, no. 1, pp. 91–95, 2015.

J. D. Keller, L. Kornblueh, A. Hense, and A. Rhodin, “Towards a GME ensemble forecasting system: Ensemble initialization using the breeding technique,” Meteorol. Zeitschrift, vol. 17, no. 6, pp. 707–718, Dec. 2008.

D. Airinei and D. Berta, “Semantic Business Intelligence - a New Generation of Business Intelligence,” Inform. Econ., vol. 16, no. 2, pp. 72–80, 2012.

E. Turban, J. E. Aronson, and T.-P. Liang, Decision Support Systems and Business Intelligence, 7th ed. New Delhi: Prentice-Hall Inc, 2007.

S. Alessandrini, F. Davò, S. Sperati, M. Benini, and L. Delle Monache, “Comparison of the economic impact of different wind power forecast systems for producers,” Adv. Sci. Res, vol. 11, pp. 49–53, 2014.

M. Mirabela, F. Ianc, and L. Ciurlău, “The Price Stability-Important Lever within the Economy,” Ovidius Univ. Ann. Econ. Sci. Ser., vol. 16, no. 2, 2016.

J. R. Arnold and N. S. Chapman, Introduction to Material Management. New Jersey: Prentice-Hall Inc, 2004.

S. O. Hansson, Decision Theory, vol. 19, no. 1. Stockholm: Royal Institute of Technology, 2005.

J. O. Rawlings, S. G. Pantula, and D. a. Dickey, Applied Regression Analysis: A Research Tool, 2nd ed. New York: Springer-Verlag New York, 1998.

P. Jain, S. . Mani, L. . Charles, and A. Muzaffar, “Utility of Peak Expiratory Flow Monitoring,” CHEST Cardiopulm. Crit. Care J., no. 114, p. pp : 861-876., 1998.

M. Şahin and R. Erol, “A Comparative Study of Neural Networks and ANFIS for Forecasting Attendance Rate of Soccer Games,” Math. Comput. Appl., vol. 22, no. 4, p. 43, 2017.

J. M. Corrêa, A. C. Neto, L. A. T. Júnior, E. M. Carreño, and Á. E. Faria, “Linear combination of forecasts with numerical adjustment via MINIMAX non-linear programming,” GEPROS Gestão da Produção, Operações e Sist., vol. 11, no. 1, pp. 79–95, 2016.

Sucipto, Kusrini, and E. L. Taufiq, “Classification method of multi-class on C4.5 algorithm for fish diseases,” in Proceeding - 2016 2nd International Conference on Science in Information Technology, ICSITech 2016: Information Science for Green Society and Environment, 2016, pp. 5–9.

M. Inthachot, V. Boonjing, and S. Intakosum, “Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend,” Comput. Intell. Neurosci., vol. 2016, pp. 1–8, Nov. 2016.

E. M. F. El Houby, “A framework for prediction of response to HCV therapy using different data mining techniques.,” Adv. Bioinformatics, vol. 2014, p. 181056, Dec. 2014.

X. Q. Sun, H. W. Shen, X. Q. Cheng, and Y. Zhang, “Market Confidence Predicts Stock Price: Beyond Supply and Demand,” PLoS One, vol. 11, no. 7, pp. 1–10, 2016.

R. Myšková, P. Hájek, and V. Olej, “Predicting Abnormal Stock Return Volatility Using Textual Analysis of News ‒ A Meta-Learning Approach,” Amfiteatru Econ., vol. 20, no. 47, pp. 185–202, 2018.

W. Charles Lawrence Kamuyu, J. Lim, C. Won, and H. Ahn, “Prediction Model of Photovoltaic Module Temperature for Power Performance of Floating PVs,” Energies, vol. 11, no. 2, p. 447, Feb. 2018.

P. Houdek, “A Perspective on Consumers 3.0: They Are Not Better Decision-Makers than Previous Generations,” Front. Psychol., vol. 7, p. 848, Jun. 2016.

G. Ranco, I. Bordino, G. Bormetti, G. Caldarelli, F. Lillo, and M. Treccani, “Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics,” PLoS One, vol. 11, no. 1, pp. 1–14, 2016.

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