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This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal on Advanced Technology, Engineering, and Information System (IJATEIS) Riwayat: Educational Journal of History and Humanities
Setyanegara, Aditya Kusuma
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Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences

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Development of a Web-Based Server Monitoring System Using the PPDIOO Method Rayendra, Riat; Setyanegara, Aditya Kusuma; Febrian Tara, Muhammad Raafi
INTERNATIONAL JOURNAL ON ADVANCED TECHNOLOGY, ENGINEERING, AND INFORMATION SYSTEM Vol. 4 No. 2 (2025): MAY
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/ijateis.v4i2.1690

Abstract

The Regional Revenue Agency (Bapenda) of Riau Province is a government institution responsible for planning, implementing, monitoring, and managing regional revenue in Riau Province. Since 2011, Bapenda has implemented a centralized local revenue tax collection system through the Registration Identification and Samsat System (SIRIS). In 2017, the SIRIS system handled an average of 3,606 transactions from 33 UPTUP across Riau Province, generating a total revenue of Rp 1,703,507,593,488. To maintain the quality of payment services, the SIRIS server must be continuously monitored to ensure optimal performance. One of the main issues in the current monitoring process is that Network Administrators must manually monitor the system at all times since it lacks an automatic alert feature for disruptions. This condition requires administrators to continuously monitor the server, which is not always feasible. This research focuses on developing a Web-Based Server Monitoring System that can provide real-time notifications via Instant Messaging Telegram. The system is developed using the Prepare, Plan, Design, Implement, Operate, Optimize (PPDIOO) methodology. User Acceptance Testing results indicate a 100% user acceptance rate for the system. Based on the testing results, it can be concluded that the developed server monitoring system effectively assists in server monitoring and promptly provides notifications via Telegram in case of disruptions. With a high acceptance rate, this system significantly facilitates Network Administrators in overseeing the server more efficiently.
Developing a Stock Prediction System Using Grammatical Evolution Setyanegara, Aditya Kusuma
Riwayat: Educational Journal of History and Humanities Vol 8, No 2 (2025): April
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jr.v8i2.45122

Abstract

Stocks represent a popular investment model in contemporary markets. However, investing in stocks carries inherent risks, potentially leading to losses when purchased at high prices but sold at significantly lower values. Technical analysis is employed to examine historical stock price behavior in order to predict future price movements. The Grammatical Evolution method has been selected to address this issue, utilizing historical stock price data as input. By constructing a comprehensive grammar in Backus-Naur Form notation, Grammatical Evolution facilitates the exploration of numerous potential predictive models, allowing for a wide range of forecasting possibilities.
Technical analysis model for stock prediction using a grammatical evolution algorithm Setyanegara, Aditya Kusuma; Sitanggang, Imas Sukaesih; Mushthofa, Mushthofa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1236-1246

Abstract

Stocks are a popular investment instrument but carry high risks, where investors may incur losses when stocks are bought at high prices and sold at lower prices. Technical analysis is used to study past stock price behavior to predict future prices. In this study, grammatical evolution (GE) is applied as an evolutionary computing technique to discover optimal functions or programs that represent historical stock price data. This study develops GE based prediction models by utilizing objective functions and search spaces defined through grammar. The model integrates technical indicators based on complex statistical models such as autoregressive integrated moving average (ARIMA), prophet, exponential smoothing, and Fibonacci retracements. Furthermore, this study employs GE to generate ensemble weights randomly, ensuring each model contributes equitably to the final prediction formula. Experiments were conducted using multiple stock datasets, including SMAR, S&P 500, the Johannesburg Stock Exchange (JSE), the New York Stock Exchange (NYSE), and Adani Enterprises (ADANIENT), to evaluate the model’s adaptability and generalization capability. The results demonstrate that the proposed GE model effectively captures complex market patterns and produces more reliable stock price predictions compared to deep learning-based approaches. Although GE requires greater computational time, the findings suggest that GE provides a flexible and effective framework for constructing hybrid stock price forecasting models in dynamic market environments.
Co-Authors Febrian Tara, Muhammad Raafi Imas Sukaesih Sitanggang Rayendra, Riat