cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota semarang,
Jawa tengah
INDONESIA
Media Statistika
Published by Universitas Diponegoro
ISSN : -     EISSN : 24770647     DOI : -
Core Subject : Science,
Decision Sciences, Operations Research & Management
Arjuna Subject : -
Articles 271 Documents
 24   25   26   27   28 
STOCK PRICE PREDICTION IN INDONESIA USING EXTREME GRADIENT BOOSTING OPTIMIZED BY ADAPTIVE PARTICLE SWARM OPTIMIZATION Safira, Alya Mirza; Trimono, Trimono; Hindrayani, Kartika Maulida
MEDIA STATISTIKA Vol 18, No 1 (2025): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.18.1.105-115

Abstract

High volatility is a major problem in generating accurate predictions of stock prices. It also causes unstable predictions and increases the loss risk. Therefore, an adaptive prediction model that is able to adjust to dynamic data pattern changes is needed. This study aims to address these issues by developing an Extreme Gradient Boosting (XGBoost) model optimized using Adaptive Particle Swarm Optimization (APSO). XGBoost was chosen for its ability to handle nonlinear relationships and minimize overfitting, while APSO serves to adaptively adjust parameters to obtain the optimal combination of hyperparameters. The novelty of this research lies in the application of XGBoost-APSO integration in the context of stock price prediction in the Indonesian capital market, which is characterized by high volatility. The study was conducted using daily closing price data of PT Aneka Tambang Tbk (ANTM) shares from November 2020 to May 2025 to predict prices seven days ahead. The results show that the XGBoost-APSO model provides the best performance with a MAPE value of 0.2%, superior to XGBoost-PSO (2.58%) and standard XGBoost (2.91%). This approach effectively improves prediction accuracy and supports quick and accurate investment decision making, while contributing to the development of intelligent prediction systems in the Indonesian capital market.

Page 28 of 28 | Total Record : 271

 24   25   26   27   28 

Filter by Year

2008 2025


Reset
Filter By Issues
All Issue Vol 18, No 1 (2025): Media Statistika Vol 17, No 2 (2024): Media Statistika Vol 17, No 1 (2024): Media Statistika Vol 16, No 2 (2023): Media Statistika Vol 16, No 1 (2023): Media Statistika Vol 15, No 2 (2022): Media Statistika Vol 15, No 1 (2022): Media Statistika Vol 14, No 2 (2021): Media Statistika Vol 14, No 1 (2021): Media Statistika Vol 13, No 2 (2020): Media Statistika Vol 13, No 1 (2020): Media Statistika Vol 12, No 2 (2019): Media Statistika Vol 12, No 1 (2019): Media Statistika Vol 11, No 2 (2018): Media Statistika Vol 11, No 1 (2018): Media Statistika Vol 10, No 2 (2017): Media Statistika Vol 10, No 1 (2017): Media Statistika Vol 9, No 2 (2016): Media Statistika Vol 9, No 1 (2016): Media Statistika Vol 8, No 2 (2015): Media Statistika Vol 8, No 1 (2015): Media Statistika Vol 7, No 2 (2014): Media Statistika Vol 7, No 1 (2014): Media Statistika Vol 6, No 2 (2013): Media Statistika Vol 6, No 1 (2013): Media Statistika Vol 5, No 2 (2012): Media Statistika Vol 5, No 1 (2012): Media Statistika Vol 4, No 2 (2011): Media Statistika Vol 4, No 1 (2011): Media Statistika Vol 3, No 2 (2010): Media Statistika Vol 3, No 1 (2010): Media Statistika Vol 2, No 2 (2009): Media Statistika Vol 2, No 1 (2009): Media Statistika Vol 1, No 2 (2008): Media Statistika Vol 1, No 1 (2008): Media Statistika More Issue