Prediction of Hyacinth Handicraft Trends in New Era

Pinthusorn Pasanajano

Abstract


The purpose of this research is to predict trends of Hyacinth handicraft products based on web application with make order, select products, register member, customer service, summary report and payment notification. The research method is System Development Life Cycle (SDLC) that involves many phases, including planning, design, building, testing, implementing and evaluating. The result of this research is useful for customer who want to order hyacinth handicraft products and it is better way to get their orders. The conclusion of this research showed that Decision Tree (J48) is the appropriate algorithms that can predict hyacinth handicraft trends in new era. It can be said that web application of Hyacinth handicraft is easy to use and can also promote Hyacinth handicraft all time.

 

Keywords: Classification, Hyacinth, Web application

 


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References


Rakotoarisoa, T. F., Waeber, P. O., Richter, T., & Mantilla-Contreras, J. (2015). Water hyacinth (Eichhornia crassipes), any opportunities for the Alaotra wetlands and livelihoods?. Madagascar Conservation & Development, 10(3), 128-136.

Aibinu, A. M., Salami, M. J. E., & Shafie, A. A. (2010, November). Application of modeling techniques to diabetes diagnosis. In 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES) (pp. 194-198). IEEE.

Farhad, A., & Sanjay, P. (2017). Comparative Study of J48, Naive Bayes and One-R Classification Technique for Credit Card Fraud Detection using WEKA [J]. Advances in computational sciences and technology, 10(62), 1731-1743.

Syarif, I., Prugel-Bennett, A., & Wills, G. (2016). SVM parameter optimization using grid search and genetic algorithm to improve classification performance. Telkomnika, 14(4), 1502.

Hossin, M., & Sulaiman, M. N. (2015). A review on evaluation metrics for data classification evaluations. International journal of data mining & knowledge management process, 5(2), 1.

Jain, Y. K. (2012). Upendra,“An Efficient Intrusion Detection based on Decision Tree Classifier Using Feature Reduction,”. International Journal of Scientific and Research Publication, 2(1), 1-6. [4] Muntean, M., & Diana, T. (2009). Information technology & organizational knowledge management.

Buddhinath, G., & Derry, D. (2006). A simple enhancement to one rule classification. Department of Computer Science & Software Engineering. University of Melbourne, Australia, 40.

Al Amrani, Y., Lazaar, M., & El Kadiri, K. E. (2018). Random forest and support vector machine based hybrid approach to sentiment analysis. Procedia Computer Science, 127, 511-520.


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