Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Pengabdian Masyarakat dan Riset Pendidikan
Hasby Firmansyah
Unknown Affiliation
Humanities Education Languange, Linguistic, Communication & Media Mathematics Other

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Analisis Pengaruh Parameter Support Vector Machine Terhadap Akurasi Prediksi Harga Saham: Penelitian Arief Priyono; Hasby Firmansyah; Wahyu Asriyani; Rizki Prasetyo Tulodo
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4529

Abstract

Stock price prediction is challenging due to fluctuating and nonlinear behavior. This study examines the effect of parameter optimization in Support Vector Machine (SVM) on prediction accuracy and error for stock prices. The dataset consists of PT Telekomunikasi Indonesia Tbk (TLKM) stock data from 2022–2024 obtained from Yahoo Finance. The workflow includes normalization, windowing-based feature construction, train–test splitting, and modeling using ε-Support Vector Regression (ε-SVR) with a Radial Basis Function (RBF) kernel. Parameter optimization is conducted via Optimize Parameters (Evolutionary) to find suitable C, gamma, and epsilon values, and the optimized model is compared against a baseline using LibSVM default parameters. Performance is evaluated using Root Mean Squared Error (RMSE), Absolute Error (AE), Correlation, and Prediction Average. Results indicate that the optimized model produces more stable predictions and follows the actual pattern more consistently, although the baseline may yield lower numerical error in some cases. This finding suggests that parameter optimization increases model sensitivity to training patterns but requires careful regularization to prevent accuracy degradation on test data.
Co-Authors Arief Priyono Asriyani, Wahyu Rizki Prasetyo Tulodo