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All Journal JITER-PM (Jurnal Inovasi Terapan - Pengabdian Masyarakat) INOVTEK Polbeng - Seri Informatika
Dwi Hartanto , Anggit
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Computer Science & IT Economics, Econometrics & Finance Education Engineering Health Professions Social Sciences

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Pemanfaatan Sistem Informasi sebagai Pendukung Integrasi Data -, Atik Nurmasani; Sharazita Dyah Anggita; Eli Pujastuti; Ika Asti Astuti; Dwi Hartanto , Anggit
JITER-PM (Jurnal Inovasi Terapan - Pengabdian Masyarakat) Vol. 2 No. 3 (2024): JITER-PM
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jiter-pm.v2i3.6357

Abstract

Utilization of technology in an institution can be done with many alternatives. One of them is through the use of information systems to support business processes. Data management and data archiving have different provisions and methods between work units and data is owned by each work unit. This difference results in data not being integrated and difficulty finding data when needed. The solution is that archiving and data management is carried out centrally through a website information system to support business processes. The method used consists of analyzing collaborators needs, creating an information system, demoing an information system, installing an information system, and evaluating the use of an information system. The formulated requirements can be used as a basis for creating an information system as a data integration center. The result of information system is demonstrated to collaborators to obtain feedback. The final result of the information system needs to be installed to be installed online so it can be used by collaborators to get feedback of the use the information system. The features in the available information system can help work units archive and manage data easily. Yayasan admins can access work unit data online as needed.
Optimization of Stock Trading Strategies Using a Hybrid Reinforcement Learning and Forecasting Model Hidayat, Rezha Ikhwan; Dwi Hartanto , Anggit
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/9vzmbf06

Abstract

Stock price prediction is an interesting challenge in machine learning due to the non-linear nature of the market. Although forecasting models can predict prices, they often do not provide optimal trading strategies. Reinforcement learning (RL) has the potential to optimize strategies, but it is highly dependent on the input states. This study integrates two methods—a CNN-LSTM forecasting model and RL (A3C)—to develop an algorithmic trading strategy. The model is evaluated using historical INDF stock data (2016–2024) with a data-split validation protocol of 80% training and 20% testing. Backtesting simulations on the period (Feb 2023–Dec 2024) show that the hybrid model achieves a cumulative total return of 121.44%. This result was obtained using an all-in trading strategy (one full position at a time) and includes transaction costs: a trading fee of 0.01% per transaction and a borrow interest rate of 0.0003% per day for short positions. This performance significantly outperforms traditional strategies: Buy and Hold (23.45%), MA Crossover (51.13%), RSI (9.09%), and MACD (−29.08%). The hybrid model also achieves a Sharpe Ratio of 2.381 (annualized, assuming a 0% risk-free rate).
Co-Authors -, Atik Nurmasani Anggita, Sharazita Dyah Eli Pujastuti Hidayat, Rezha Ikhwan Ika Asti Astuti