ALGORITMA NEURAL NETWORK BACKPROPAGATION UNTUK PREDIKSI HARGA SAHAM PADA TIGA GOLONGAN PERUSAHAAN BERDASARKAN KAPITALISASINYA

Abstract

Stock is one type of investment where investors can gain profits in the form of capital gains and dividends. Types of shares based on the level of capitalization are divided into 3 types, namely the first layer (blue chips), the second layer, and the third layer. One of the techniques that investors use in order to make a profit is technical analysis, which is using data of past stock prices and volumes based on the assumption that trends can recur following historical data patterns. Based on the assumptions of technical analysis, it is possible to use data mining to predict stock prices. In this study, stock price predictions will be carried out by comparing three types of companies based on their capitalization, for first layer stocks using PT. Bank Central Asia Tbk (BBCA), the second layer using PT. XL Axiata Tbk (EXCL), and third layer using PT Pembangunan Graha Lestari Indah Tbk. The data mining algorithm that will be used is the Neural Network Backpropagation method. The attributes used as predictors are open, high, low, and volume, while the objective attribute is close. This study aims to determine whether daily stock historical data can be used to predict stock prices using the Neural Network Backpropagation method and how to compare the results of predictions between 3 companies with different capitalization levels. The result of RMSE for BBCA by using the most optimal combination of parameters and 3 hidden layer is 123.84. The result of RMSE for EXCL by using the most optimal combination of parameters and hidden layer 2 is 37.36. The result of RMSE for PGLI by using the most optimal combination of parameters and hidden layer 6 is 6.16. So that the backpropagation neural network algorithm is most optimally applied to third layer companies, PT. Pembangunan Graha Lestari Indah Tbk because the RMSE value is the smallest.