APPLYING THE CLASSIFICATION ALGORITHM FOR THE SYSTEM RECOMMENDATIONS BUY SELL IN FOREX TRADING
Abstract
Abstract- Having a sophisticated application, even though often experience problems in deciding BUY - SELL in trading forex trading. This is due to the often time series predictions, in the high variable experiencing high values as well as low variables, for that it is needed a recommendation system to overcome this problem.
The application of classification algorithms to the recommendation system in support of BUY-SELL decisions is one appropriate alternative to overcome this. K-Nearest Neighbor (K-NN) algorithm was chosen because the K-NN method is an algorithm that can be used in building a recommendation system that can classify data based on the closest distance. This system is designed to assist traders in making BUY-SELL decisions, based on predictive data.
The results of the recommendation system from the ten trials predicted by Arima are recommended. When compared to the price in the field the target profit is 7% per week from ten experiments if the average profit has exceeded the target
Downloads
References
[2] Amrita Naika, Lilavati Samant, “Correlation review of classification algorithm using data mining tool: WEKA, Rapidminer, Tanagra, Orange and Knime” International Conference on Computational Modeling and Security (CMS 2016).
[3] Achmad Bayhaqy, Kaman Nainggolan, Sfenrianto Sfenrianto, Emil R. Kaburuan “Sentiment Analysis about E-Commerce from Tweets Using Decision Tree, K-Nearest Neighbor, and Naïve Bayes,” STMIK Nusa Mandiri Jakarta, Indonesia dan Bina Nusantara University, Jakarta, Indonesia 11480.
[4] A. Sespajayadi, Indrabayu, and I. Nurtanio, “Technical data analysis for movement prediction of Euro to USD using Genetic Algorithm-Neural Network,” in 2015 International Seminar on Intelligent Technology and Its Applications (ISITIA), 2015, pp. 23–26.
[5] M. Goyal and R. Vohra, “Applications of Data Mining in Higher Education”, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 2, No 1, March 2012.
[6] R. Huebner, “A survey of educational data mining research”, Research in Higher Education Journal, 2012.
[7] M.S. Mythili, A.R. Mohamed Shanavas, “An Analysis of students’ performance using classification algorithms”, IOSR, Journal of Computer Engineering, Volume 16, Issue 1, January 2014.
[8] S. Lakshmi Prabha, A.R.Mohamed Shanavas, “Educational data mining applications”, Operations Research and Applications: An International Journal (ORAJ), Vol. 1, No. 1, August 2014.
[9] C. Romero, S. Ventura and E. Garcia, "Data mining in course management systems: Moodle case study and tutorial", Computers & Education, Vol. 51, no. 1, pp. 368-384, 2008
[10] S. Ayesha, T. Mustafa, A. Sattar and M. Khan, “Data mining model for higher education system”, Europen Journal of
[11] Z. J. Kovacic, “Early prediction of student success: Mining student enrollment data”, Proceedings of Informing Science & IT Education Conference (In SITE) 2010.
[12] P. Kavipriya, A Review on Predicting Students’ Academic Performance Earlier, Using Data Mining Techniques, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 6, Issue 12, December 2016 ISSN: 2277 128X
[13] Christos Tjortjis, John Keane “A classification algorithm for data mining” Intelligent Data Engineering and Automated Learning — IDEAL 2002
[14] Abdullah H Wahbeh, Qasem A. Al-Radaideh, Mohammed N Al-Kabi, Emad M Al Shawakfa, ”Comparitive study of data mining tools over some classification methods”, (IJACSA) International Journal of Advanced Computer Science and Applications, Special Issue on Artificial Intelligence
[15] Priyadharsini. C and D. A. S. Thanamani, “An Overview of Knowledge Discovery Database and Data mining Techniques,” Int. J. Innov. Res. Comput. Commun. Eng., vol. 2, no. 1, pp. 1571–1578, 2014 [Online]. Available: http://www.rroij.com/open-access/anoverview-of-knowledge-discovery-databaseand-data-miningtechniques.pdf
Copyright Notice
An author who publishes in the Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) agrees to the following terms:
- Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-ShareAlike 4.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal
- Author is able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book) with the acknowledgement of its initial publication in this journal.
- Author is permitted and encouraged to post his/her work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).
Read more about the Creative Commons Attribution-ShareAlike 4.0 Licence here: https://creativecommons.org/licenses/by-sa/4.0/.