Implementation Fuzzy and Extended Kalman Filter for Estimation of High and Low Stock Price Travel Company

Ismanto Hadi Santoso; Puspandam Katias; Teguh Herlambang; Mohamad Yusak Anshori; Mukhtar Adinugroho

doi:10.30598/pijmathvol2iss1pp17-24

Ismanto Hadi Santoso Department of Accounting Magister, University of Wijaya Kusuma Surabaya
Puspandam Katias Department of Management, Universitas Nahdlatul Ulama Surabaya
Teguh Herlambang Universitas Nahdlatul Ulama Surabaya
Mohamad Yusak Anshori Department of Management, Universitas Nahdlatul Ulama Surabaya
Mukhtar Adinugroho Department of Management, Universitas Nahdlatul Ulama Surabaya

DOI: https://doi.org/10.30598/pijmathvol2iss1pp17-24

Keywords: Stock Price, EKF, Fuzzy, Estimation

Abstract

Competition in the business world is getting tougher from year to year both within a country and abroad. There are a large number of companies competing with one another, especially entering the free market share in Asia, namely the Asean Economic Community (AEC). In the current development of modern economy, Indonesia is making efforts to increase its economic growth. For this, developments in any fields are made. Among others is the service industry such as accommodation, travel, and transportation services. Considering that Indonesia is a country comprised of many islands with a variety of natural beauty, it has the very potential for tourist resort attraction. This kind of thing leads to the growth of the Travel, tourism and hotel industry to support development of tourism. With such rapid service industry development, supported by promising business opportunities, investors for such sector are encouraged. The right way to reduce risk for investors interested is to develop a system for estimating the stock prices. Therefore, in this study, the high and low stock price estimation method applied for travel companies adopted developed Kalman Filter, a comparison of two Kalman Filter development methods, namely Extended Kalman Filter (EKF) and Fuzzy Kalman Filter (FKF) as a chart for investors to take into consideration in their investment decision making. The simulation results showed that the EKF method had higher accuracy than the FKF method with an error by the EKF of 3.5% and that by the FKF of 8.9%.

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