Penerapan Least Squares Support Vector Machines (LSSVM) dalam Peramalan Indonesia Composite Index

Andri Triyono; Rahmawan Bagus Trianto; Dhika Malita Puspita Arum

doi:10.32493/informatika.v6i1.10237

Authors

Andri Triyono Universitas An Nuur
Rahmawan Bagus Trianto Universitas An Nuur
Dhika Malita Puspita Arum Universitas An Nuur

DOI:

https://doi.org/10.32493/informatika.v6i1.10237

Keywords:

LSSVM, RBF Kernel, Stock Price, Prediction

Abstract

In the era of very rapidly advancing technology like today, both internet technology and computerization have made various corporate agencies or investors start thinking about the importance of the stock market in their capital division. Previously there were various purchases by the company's capital, such: gold, land, buildings, production machines, but at this time the purchase of capital shares should also start to attract attention and these purchases are legal investments. Various kinds of company shares that are sold can already be seen through the internet and it is very easy and attractive for companies that will make capital purchases, even the model can be chosen for both long-term and short-term capital purchases. This stock price forecasting system using the Least Squares Support Vector Machines (LSSVM) method will be very popular with investors to help determine conclusions for buying shares because it can reduce losses or even make the right decisions so that it will increase profits for investors or companies. Least Squares Support Vector Machines is a simpler model and has been modified from the previous model, namely: Support Vector Machines (SVM) method. Solving linear equations can be solved in a simpler way using LSSVM compared to using SVM. The variable used in the network is the close price variable. The kernel that used for this study is the RBF kernel. This study consists of three phases or stages. The first stage uses 400 historical data rows, second stage uses 800 historical data rows, and the third stage uses 1200 rows of data. This research obtains the best result of accuracy in the third stage. The third stage has the smallest MSE value: 0.00025248 by using 1200 rows of historical data.

Author Biography

Andri Triyono, Universitas An Nuur

Bekerja di Universitas An Nuur sebagai pengajar di program studi ilmu komputer dan juga sebagai programmer di berbagai sistem informasi di Universitas An Nuur

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