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 272 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.
MACHINE LEARNING TO HANDLE MISSING VALUES, PREDICTION AND DETECTION OF RISK FACTOR FOR ATRIAL FIBRILLATION, DIABETES MELLITUS TYPE 2, AND PREDIABETES IN OBSTRUCTIVE SLEEP APNEA (OSA) PATIENTS Kurnia Susvitasari; Yuni Rosita Dewi; Titin Siswantining; Diana Nur Vitasari; Felicia Erinna Puspitaningtyas; Telly Kamelia
MEDIA STATISTIKA Vol 18, No 2 (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.18.2.1-12

Abstract

Machine learning is a discipline of artificialintelligence. It enables computational systems to exhibitbehaviors derived from empirical data, without explicitprogramming for the task. Algorithms are systematicallyapplied to various tasks in the biomedical and healthcare sectors,including classification, regression, clustering, dimensionalityreduction, model selection, and data preprocessing. This clinicalstudy employs machine learning techniques to resolve missingdata anomalies and to create predictive models for the risk ofAtrial Fibrillation (AF), Type 2 Diabetes Mellitus (DM), andPrediabetes in patients with Obstructive Sleep Apnea, in a casestudy at dr Cipto Mangunkusomo Hospital. The assessment ofvarious imputation and deletion methodologies (listwisedeletion, zero-imputation, central tendency substitutions, andfuzzy c-means clustering) was conducted to address thestructural incompleteness of the dataset. The disease risk wascategorized using several algorithms: Support Vector Machines,Random Forests, Extreme Gradient Boosting, and MultilayerPerceptrons. A missing value proportion of 25% or less wasobserved during the preliminary diagnostic. The empiricalfindings show that the classification accuracies for detecting AFwere 0.908, for Type 2 DM were 0.9245, and for Prediabeteswere 0.7924. The results indicate that the optimal machinelearning architecture and data imputation strategy varydepending on the specific clinical targets.

Page 28 of 28 | Total Record : 272

 24   25   26   27   28 

Filter by Year

2008 2025


Reset
Filter By Issues
All Issue Vol 18, No 2 (2025): Media Statistika 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