Fransisco Ready Permana, . (2023) PERBANDINGAN ALGORITMA EXTREME GRADIENT BOOSTING DAN RANDOM FOREST UNTUK MEMPREDIKSI HARGA TERENDAH SAHAM DENGAN INDEX ISSI. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
Stocks with the ISSI index (Indonesian Sharia Stock Index) are stocks that can be used as an investment choice because these stocks have a fairly good level of stability compared to other stock indices. Therefore, this research wants to create a machine learning model that can predict the lowest price of ISSI shares as a lower threshold value and compare two reliable algorithms, namely the random forest algorithm and extreme gradient boosting (XGBoost) using stock data taken from the Google Finance website. The stages include problem identification, literature study, data preparation, dataset loading, exploratory data analysis, preprocessing, data sharing, data training, and model evaluation. To find out which algorithm is better, the two algorithms are compared using three assessment metrics such as Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and R2. As a result, the average value of the MSE random forest test was 0.6458 and the average MSE value for the XGBoost test was 0.8019; The average value of the MAPE random forest test was 0.0033 and the average value of the MAPE XGBoost test was 0.0037; The average R2 value of the random forest test is 0.9985 and the average R2 test value is 0.9982. From these values, the random forest gives smaller MSE and MAPE values and a larger R2 value compared to XGBoost. So based on these three assessment metrics, it can be concluded that random forest can predict the lowest price of stocks with the ISSI index more accurately than XGBoost.
Item Type:  Thesis (Skripsi) 

Additional Information:  [No.Panggil: 1910511045] [Pembimbing: Iin Ernawati] [Penguji 1: Widya Cholil] [Penguji 2: Yuni Widiastiwi] 
Uncontrolled Keywords:  Stocks, ISSI, Random Forest, Extreme Gradient Boosting. 
Subjects:  Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) 
Divisions:  Fakultas Ilmu Komputer > Program Studi Informatika (S1) 
Depositing User:  Fransisco Ready Permana 
Date Deposited:  27 Jul 2023 07:50 
Last Modified:  27 Jul 2023 07:50 
URI:  http://repository.upnvj.ac.id/id/eprint/25066 
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