Analisis Kinerja Algoritma Support Vector Machine (SVM) Guna Pengambilan Keputusan Beli/Jual Pada Saham PT Elnusa Tbk. (ELSA)

Abstract

Stock is one of investing method that can improve the economy. Remote trading is one of the most popular trading method. Remote trading requires prediction of stock transaction signals to make it easier for traders to make decisions. Technical analysis is made easy with various indicators in analyzing stock price chart movements, such as Bollinger Bands, Pivot Point, MACD, Stochastic, ADX, and CCI, and then combined with Support Vector Machine (SVM) algorithm to classify sell/buy/hold classes, so we can obtain a pattern that is useful for predicting stock transaction signal decisions. The study was using WEKA software by analyzing the combination of indicators with the SVM algorithm where the object is historical data stocks of PT Elnusa Tbk. (ELSA). The highest profit obtained from this study is 28,02% which is the best model of the results of the data that is trained using non-aggressive sub sectors data using exponent value 2.