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Tri A. Sundara
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tri.sundara@stmikindonesia.ac.id
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+628116606456
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ijcs@stmikindonesia.ac.id
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The Indonesian Journal of Computer Science
Published by STMIK Indonesia Padang
ISSN : 25497286     EISSN : 25497286     DOI : https://doi.org/10.33022
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
The Indonesian Journal of Computer Science (IJCS) is a bimonthly peer-reviewed journal published by AI Society and STMIK Indonesia. IJCS editions will be published at the end of February, April, June, August, October and December. The scope of IJCS includes general computer science, information system, information technology, artificial intelligence, big data, industrial revolution 4.0, and general engineering. The articles will be published in English and Bahasa Indonesia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 21 Documents
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Search results for , issue "Vol. 15 No. 1 (2026): The Indonesian Journal of Computer Science" : 21 Documents clear
A Deep Reinforcement Learning for Adaptive Spectrum Access in Geo-Enabled Cognitive Radio Networks Ntuli, Elesa; Du Chunling
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i1.4958

Abstract

The radio spectrum is getting more crowded as more people and devices connect wirelessly. Cognitive Radio Networks (CRNs), which can find and use empty spectrum, help solve this problem. One common way they do this is by using a Geo-Location Spectrum Database (GLSD) to check which parts of the spectrum are free. But these databases are not always updated in real time, so they sometimes miss chances to use the spectrum or cause interference. This study looks at a better way to handle this by using Deep Reinforcement Learning (DRL). The system we designed learns from the environment and makes smart decisions about when and where to use the spectrum. We used a Deep Q-Network (DQN) to test it in a simulated environment. Our results show that this new method improved spectrum use by 27%, reduced interference by 35%, and made decisions 22% faster than older methods that rely only on the database. The model reached about 91% accuracy in its decisions over many tests. This means that adding DRL to geo-location systems can make spectrum use more efficient, especially in busy areas or places with limited internet access. We suggest trying this setup in the real world using Software Defined Radios (SDRs) to see how it performs outside of simulations.
A Comparative Study of Generative Adversarial Networks (GANs) in Medical Image Processing: A Review Marwa M Abdulqadir; Adnan Mohsin Abdulazeez
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i1.5047

Abstract

Generative Adversarial Networks (GANs) have come to be as powerful in medical image processing with massive benefits in image quality, fusion, classification and segmentation tasks. This paper displays an in-depth analysis of GAN structures and their use cases in medical image analysis for data augment, anomaly detection, cross- modality synthesis, super-resolution, and image reconstruction. As the demand for automated diagnostic systems has increased, GANs provide an efficient way to synthesize realistic medical images, particularly in domains with limited data availability. In this review, we discuss new developments and issues on how to apply GANs to accurate and effective medical image analysis. Moreover, this work explores the strengths, limitations, and comparative performance of different GAN models across diverse datasets and clinical tasks. By identifying key differences among the GAN models, and analysing performance, this review will be a roadmap for future studies in developing GAN-based models for better diagnosis and health applications.
Analisis Sentimen Pada Ulasan Pengguna Platform E-commerce Menggunakan Algoritma K-Nearest Neighbor Tandiapa, Saron; Rorimpandey, Gladly Caren
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i1.5050

Abstract

The development of e-commerce in Indonesia offers significant opportunities for MSMEs, yet the multitude of platform choices makes it difficult for them to determine the most suitable one. This study aims to analyze the user review sentiments toward five major e-commerce platforms (Shopee, Tokopedia, Lazada, Blibli, and Bukalapak) using the K-Nearest Neighbor (KNN) algorithm. A total of 5,995 reviews were collected from the Google Play Store via web scraping, then processed through pre-processing steps (such as case folding, tokenization, and stemming). Reviews were classified into positive, negative, and neutral sentiments using a lexicon-based approach, with Term Frequency-Inverse Document Frequency (TF-IDF) as the vectorization technique. The results show that Blibli has the highest KNN accuracy (64%), while the highest positive sentiment was achieved by Lazada with an F1-score of 54%. The research also developed a web-based application to help MSMEs analyze user sentiment across various e-commerce platforms.
Design of Brushless DC (BLDC) Motor Speed Control System Using Field Oriented Control (FOC) Method Putra, Rokhmat Rizki Perkasa; Febrianto, Rokhmat
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i1.5056

Abstract

Research on environmentally friendly vehicles has highlighted Electric Vehicles (EVs), which utilize electric motors for propulsion. This study focuses on controlling a Brushless DC (BLDC) motor, a prevalent motor type in EVs, using the Field Oriented Control (FOC) method. The research aims to design and analyze the performance of this FOC-based speed control system. The system model and its control strategy were simulated in MATLAB/Simulink. Performance was evaluated under various speed setpoints (100, 200, and 300 rpm) and torque loads (2, 4, and 7 N.m). The results demonstrate that the proposed control system operates effectively, maintaining a steady-state error of less than 5% across all test conditions. Furthermore, all other performance parameters also remained below 5%, confirming the system's robustness and precision in regulating BLDC motor speed.
Lightweight Multimodal Fusion Architectures for Intraday Abnormal Return Reversal Prediction of S&P 500 Constituent Stocks: A Literature Review Chen, Yi Xun; Run Ming Song; Adebayo Boboye Joshua
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i1.5062

Abstract

Integrating lightweight deep learning models with multimodal fusion techniques provides a promising approach to complex predictive tasks in resource-constrained environments. Drawing on recent literature, this paper systematically reviews research in three major areas: lightweight deep learning, multimodal fusion, and intraday reversal prediction and quantitative trading strategy optimization for S&P 500 constituent stocks. Empirical studies in non-financial domains show that lightweight neural architectures can balance predictive accuracy and computational efficiency. However, their adoption in financial forecasting remains limited. Most multimodal fusion methods integrate information at the feature level. The intraday reversal effect in S&P 500 constituent stocks has been empirically confirmed. However, existing prediction models typically rely on single-modal inputs or complex architectures, without combining lightweight design and multimodal fusion, making them unsuitable for real-time intraday trading. Accordingly, this paper identifies several key research gaps and proposes hypothesis and key insights to support the practical deployment of quantitative trading.
Detection of Phishing Webpages Using a CNN-BiGRU Hybrid Deep Learning Framework Saima Anwar Lashari; Hadeel Abdulrahman Alsantli; Khan, Abdullah; Dzati Athiar Ramli
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i1.5067

Abstract

Protecting sensitive data, like passwords and financial information in the cyber-world, is becoming a critical challenge day by day. Attackers use various smart ways to exploit the mistakes of internet users. Phishing is one of the most important types of cyber-attack. Researchers have proposed various phishing detection and identification techniques in the last decade against the phishing attacks. However, many state-of-the-art techniques have shortcomings in terms of accuracy and time complexity. But they also have major issues of the high runtime overhead. On the other hand, the simple techniques with low time-complexity have issue of the accuracy because these simple techniques have high false alarm rate. To resolve these issues, this study proposed a novel hybrid-deep-learning algorithm with 3 variants to address these high time-complexity and low accuracy issues. A novel hybrid deep learning model based on Convolutional Neural Network (CNN)-(Bi-GRU) is proposed to classify a web-page phishing or legitimate. To validate the proposed hybrid model with various variants, extensive experiments have been conducted on various benchmark datasets. The experimental results have proved the validity of the proposed model as compared to state-of-the-art techniques in terms of identifying the phishing webpages accurately in comparatively less time.
A Forensic-Ready Virtual Currency Reward Framework for Metaverse-Based Work Environments Robertson, Shelley-Anne; Baror, Stacey Omeleze; Venter, Hein Salomon
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i1.5069

Abstract

As immersive and distributed work environments expand within metaverse platforms, organisations face increasing challenges in maintaining trust, accountability, and integrity in digital reward systems. Virtual currency–based incentives offer flexibility and scalability but introduce risks related to integrity, dispute resolution, and evidentiary reliability. Existing approaches often rely on blockchain immutability alone, which is insufficient to support proactive preservation of digital evidence within reward systems. This paper proposes a forensic-ready architectural framework for virtual currency–based organisational reward systems, in which Digital Forensic Readiness is embedded as a core system design principle rather than a post-hoc security layer. The Metaward framework integrates motivational event instrumentation, forensic logging, traceability, and governance mechanisms to generate verifiable digital traces during normal operation. A conceptual instantiation and scenario-based analysis, supported by an illustrative proof-of-concept instantiation, demonstrate the feasibility of the proposed approach within metaverse-based work environments.
IoT-Based Automatic Clothesline System Using ESP8266 with Fuzzy Logic Control and Web-Based Monitoring Firda Ayu Adhidta; I Gede Puja Astawa; Faridatun Nadziroh
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i1.5071

Abstract

The efficiency and safety of manual clothes drying are significantly affected by weather conditions, such as rainfall, humidity, and limited sunlight. To address these challenges, this study presents the design and implementation of an Internet of Things (IoT)-based automatic clothesline system that uses an ESP8266 microcontroller with fuzzy logic control. The system incorporates multiple environmental parameters, including temperature, humidity, light intensity, and rain detection, to enable adaptive movement of the clothesline and fan activation. A fuzzy inference mechanism handles sensor data uncertainty and generates appropriate control actions in real time. Additionally, a web-based monitoring interface enables users to remotely monitor environmental conditions and system status and to manually control the system. Experimental results show that the system responds effectively to dynamic weather changes, reducing the risk of clothes being exposed to rain and enhancing overall drying efficiency. These findings suggest that the proposed approach offers a practical and reliable solution for smart household automation applications.
Adaptive Smart Cat Feeding System Based on ESP32 Using Fuzzy Logic and IoT Monitoring Stefina Hendrayani; I Gede Puja Astawa; Budi Aswoyo
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i1.5072

Abstract

Smart pet feeders are increasingly utilised to enhance daily pet care; however, most existing systems depend on fixed feeding schedules and lack adaptability to changing conditions. This study details the design and implementation of an Internet of Things (IoT)-based smart cat feeder that incorporates an ESP32 microcontroller, fuzzy logic control, and a web-based interface. The system utilises a fuzzy inference mechanism to adaptively determine feeding portions under uncertain conditions, thereby addressing the limitations of threshold-based feeding strategies. A web interface enables real-time monitoring and manual override functions. Experimental results demonstrate that the system operates reliably and provides a more flexible, adaptive feeding behaviour than conventional automatic feeders. These findings suggest that the proposed approach offers a practical and effective solution for intelligent pet care applications.
An Intelligent IoT-Based Irrigation System with Fuzzy Logic Control for Orchid Cultivation Nabilah Farah Rizqika Widodo; I Gede Puja Astawa; Faridatun Nadziroh
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i1.5074

Abstract

Efficient irrigation management is essential in orchid cultivation because orchids are susceptible to environmental factors, especially soil moisture and temperature. This study details the design and implementation of an Internet of Things (IoT)-based intelligent irrigation system for orchids using fuzzy logic control. The system integrates environmental sensors with a microcontroller platform to enable adaptive irrigation decisions in uncertain, dynamic environments. A fuzzy inference mechanism determines optimal irrigation actions, addressing the limitations of fixed-threshold methods. Furthermore, the system features real-time monitoring, enabling users to observe environmental conditions and system status remotely. Experimental results show that the system operates reliably and maintains irrigation conditions within the target range. These findings suggest that the proposed approach offers a practical and effective solution for intelligent irrigation in precision agriculture.

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