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Tri A. Sundara
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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 1,127 Documents
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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.
Perancangan Early Warning System Berbasis Data Warehouse untuk Pencegahan Mahasiswa Drop Out Syalevi, Rahmad; Purnama, Diki Gita; Ayu, Jenar Mahesa
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.v15i1.5075

Abstract

Higher education institutions require integrated, analytics-based data management to support strategic decision-making and student drop out prevention. This study aims to design a Data Warehouse (DW) model as the foundation for an Early Warning System (EWS) to detect student drop out risks at Universitas Paramadina. The DW is designed using the Kimball lifecycle approach with a star schema implementation, integrating data from multiple business processes such as academics, finance, and LMS activities. The EWS is developed using a supervised learning classification approach, utilizing Logistic Regression as the baseline model and proposing Random Forest for advanced modeling. The results demonstrate that an integrated DW effectively supports machine learning-based predictive analytics and serves as a strategic framework for proactive student drop out prevention.
Ethical Adoption of AI-Powered EdTech in Higher Education: Human-AI Interaction through an Ethically Extended UTAUT2 Model Masimba, Fine; Maguraushe, Kudakwashe; Chimbo, Bester
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.v15i1.5079

Abstract

This study addresses the need for responsible AI adoption in higher education by developing a human-centred ethical extension of the UTAUT2 model. It integrates two new constructs; AI fairness and human autonomy support and three ethical moderators: ethical risk awareness, perceived algorithm bias and user autonomy concern. To validate the framework, an empirical investigation was conducted with 400 respondents using a structured questionnaire, with data analyzed via regression. All sixteen hypotheses were supported. The model demonstrated strong predictive power, explaining 72.2% of the variance in behavioural intention and 69.1% in use behaviour. The results provide meaningful insights into how ethical perceptions influence adoption. Ultimately, the framework offers practical guidance for policymakers, educators and developers to ensure fair, trustworthy and human-centric AI integration in learning environments.
A Review on White Blood Cell Classification for Leukemia Diagnosis Using Deep and Transfer Learning Techniques Thamer, Dilan; 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.v15i1.5083

Abstract

Leukemia is a severe hematological malignancy that disrupts normal blood cell function, primarily affecting white blood cells (WBCs). Early and accurate Classification of white blood cells (WBCs) is essential for facilitating the accurate diagnosis of leukemia, thereby improving patient outcomes and reducing treatment costs. This paper provides a comprehensive review of recent deep learning and transfer learning approaches applied to WBC classification and leukemia diagnosis. Various models, including Convolutional Neural Networks (CNNs), Vision Transformers (ViT), and hybrid techniques combining handcrafted and learned features, are examined. Performance metrics such as accuracy, sensitivity, specificity, and F1-score are discussed across multiple datasets like BCCD, ALL-IDB, and Kaggle repositories. The study highlights the strengths of different models, addresses challenges such as class imbalance and data scarcity, and outlines future directions like the integration of multimodal data and real-time deployment. This review serves as a valuable resource for researchers and clinicians aiming to develop intelligent, automated systems for hematological disease diagnosis.
A Personalized Generative AI Model for Diabetes Drug Discovery: Integrating Molecular and Clinical Data Using Variational Autoencoders (VAE) Ndlovu, Belinda
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.v15i1.5084

Abstract

Diabetes drug discovery remains slow, costly, and insufficiently personalised, particularly in resource-constrained healthcare settings. This study proposes and empirically evaluates a personalised, generative artificial intelligence framework that integrates molecular and clinical data to generate diabetes drug candidates. Guided by the CRISP-DM framework, a hybrid Clinical–Molecular Variational Autoencoder (VAE) architecture was developed, combining molecular representations with anonymised patient metabolic profiles, including HbA1C, fasting glucose, BMI, cholesterol, and age. Molecular data were sourced from PubChem and ChEMBL, and generated compounds were evaluated using drug-likeness metrics, molecular validity checks, and downstream effectiveness classification. The model successfully generated chemically valid, drug-like molecules with average Quantitative Estimate of Drug-likeness (QED) scores above 0.5. At a fixed decision threshold, effectiveness classification achieved an accuracy of 0.80; however, probability calibration analysis revealed limited discriminative reliability across thresholds (AUC = 0.49), highlighting the impact of class imbalance. Unlike prior molecule-centric generative drug discovery approaches, this study presents one of the first empirically evaluated Clinical–Molecular dual-VAE frameworks for personalised diabetes drug discovery, explicitly integrating patient metabolic profiles while revealing calibration limitations in generative pharmaceutical pipelines.

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