Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal JURNAL ELEKTRO CogITo Smart Journal
Rizki Surya Permana
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Susunan Redaksi Jurnal Elektro Rizki Surya Permana
Jurnal Elektro Vol 12 No 2 (2019): Oktober 2019
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (38.52 KB)

Abstract

-
Indek Pengarang Jurnal Vol 12.2 Rizki Surya Permana
Jurnal Elektro Vol 12 No 2 (2019): Oktober 2019
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (21.603 KB)

Abstract

-
Indek Subjek Jurnal Elektro Vol 12.2 Rizki Surya Permana
Jurnal Elektro Vol 12 No 2 (2019): Oktober 2019
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (23.717 KB)

Abstract

-
Template Jurnal Elektro Rizki Surya Permana
Jurnal Elektro Vol 12 No 2 (2019): Oktober 2019
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (110.609 KB)

Abstract

-
Predicting Stock Market Trends Based on Moving Average Using LSTM Algorithm Permana, Rizki Surya; Mahyastuty, Veronica Windha; Budiyanta, Nova Eka; Bachri, Karel Octavianus; Kartawidjaja, Maria Angela
CogITo Smart Journal Vol. 10 No. 2 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i2.648.486-495

Abstract

Prediction of the stock market is highly needed to assist traders in making decisions. Many methods are used by traders to predict this such as technical analysis and moving averages. Moving averages predict stock trends based on the past data of the stock. The disadvantage of using a moving average analysis is the delay in crossover signals. As a solution, a deep learning technique known as LSTM is applied to the moving average strategy in this paper. In this research, the BBCA stock dataset spanning from 2010 to 2018 was utilized. The data was segmented into two parts: 2010-2017 for training data and 2018 for testing data. The training process employed Long Short-Term Memory (LSTM) networks, with the subsequent results being combined with moving average crossover techniques. Validation results indicate that BBCA shows a relatively minimal error. BBCA's average MAPE is 1.1%, and its RMSE is 65.402, classifying it within the "Highly Accurate Forecasting" category. Various combinations of moving average crossovers were tested during model training, with the combination of SMA05 and SMA50 for BBCA yielding the highest profit potential. Stocks that exhibit a downward trend are more likely to incur substantial losses. The model can predict the reversal of trends by predicting the trading signal given by the moving averages.
Co-Authors Bachri, Karel Octavianus Maria Angela Kartawidjaja Nova Eka Budiyanta Veronica Windha Mahyastuty