Predicting the Jakarta composite index price using ANFIS and classifying prediction result based on relative error by fuzzy Kernel C-Means

F. Fanita, Zuherman Rustam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Stock index reflects the price movement a group of stock. There are many stock indices in the world. JKSE (Jakarta Composite Index) is one of the stock indices in IDX (Indonesia Stock Exchange). There are many benefits in knowing the movement of JKSE value, one of them is to reduce the risk in investing in the stock market. Therefore it is a need to predict JKSE value. The method that is used in this paper is ANFIS (Adaptive Neuro Fuzzy Inference System). ANFIS is a hybrid model which can give the better accuracy than other isolated technique of AI (Artificial intelligence) such as ANN, fuzzy logic, and GA. This paper gives two outputs, they are the prediction result and classification result based on some relative error values. Classification method that is used in this paper is Fuzzy Kernel C-Means. This two outputs will help the investors in decision making. The experimental results give an average of prediction accuracy 91 % and classification accuracy 80.2 %.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017
EditorsRatna Yuniati, Terry Mart, Ivandini T. Anggraningrum, Djoko Triyono, Kiki A. Sugeng
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735417410
DOIs
Publication statusPublished - 22 Oct 2018
Event3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017 - Bali, Indonesia
Duration: 26 Jul 201727 Jul 2017

Publication series

NameAIP Conference Proceedings
Volume2023
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017
CountryIndonesia
CityBali
Period26/07/1727/07/17

Keywords

  • ANFIS
  • Fuzzy Kernel C-Means
  • Jakarta Composite Index
  • Relative Error
  • classify
  • predict

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    Fanita, F., & Rustam, Z. (2018). Predicting the Jakarta composite index price using ANFIS and classifying prediction result based on relative error by fuzzy Kernel C-Means. In R. Yuniati, T. Mart, I. T. Anggraningrum, D. Triyono, & K. A. Sugeng (Eds.), Proceedings of the 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017 [020206] (AIP Conference Proceedings; Vol. 2023). American Institute of Physics Inc.. https://doi.org/10.1063/1.5064203