Open International Journal of Informatics
Open International Journal of Informatics (OIJI) Open International Journal of Informatics (OIJI) is an international peer-reviewed journal (Open Access) dedicated to the latest advancement of Informatics sponsored by Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia. The goal of this journal is to provide a platform for scientists, researchers and academicians all over the world to promote, share, and discuss various new issues and developments in broad areas of Informatics. The journal has the following topics that cover the theoretical and application of Informatics but not limited to: Artificial Intelligence, Business Intelligence, Blockchain, Cloud Computing, Data Analytics, Deep Learning, Embedded System, Cyber-Physical System, Edge Computing, Information Systems, Information Security, Informatics related discipline (e.g. Agro-based, Business, Health, Sports, Social, etc), Intelligent Systems and Computing Technologies, Internet of Things (IoT), Machine Learning, Software Engineering, Systems Integration, Forensic and Social Engineering, Computer Ethics and Privacy Indexed by: MyJurnal, Google Scholar
Predicting Juvenile Crime Severity Using Machine Learning: A Study in Malaysian Rehabilitation Schools
This study aims to classify juvenile crime severity using machine learning techniques based on data collected from 102 juvenile offenders across three rehabilitation schools under the Malaysian Department of Social Welfare. A self-administered survey captured demographic, psychosocial, and behavioral factors, while the target variable was categorized into three levels of crime severity. Three supervised classification models namely Logistic Regression, K-Nearest Neighbors (KNN), and Random Forest were applied to identify predictive patterns, and the Synthetic Minority Oversampling Technique (SMOTE) was employed to address class imbalance. Feature selection and ranking were conducted using Random Forest feature importance, with statistical validation provided through multinomial logistic regression p-values. The results indicate that Random Forest achieved the highest predictive performance, attaining an accuracy of 58.1% before SMOTE and 51.6% after SMOTE, outperforming Logistic Regression (45.2%) and KNN (41.9%). When restricted to the six most important predictors which are Family Background, Emotional Health, Gender, Experience of Premarital Sex, Age Group, and Education Level, the Random Forest model maintained its peak accuracy of 58.1%, indicating that a reduced-feature model can preserve predictive performance while improving interpretability. Within this subset, Family Background and Emotional Health were identified as the most influential features by the Random Forest model. In parallel, multinomial logistic regression analysis identified Gender, Experience of Premarital Sex, and Age Group as statistically significant predictors of crime severity (p < 0.05). Overall, the findings suggest that a combination of demographic, behavioral, and psychosocial factors plays a significant role in differentiating juvenile crime severity among Malaysian offenders. This study highlights the potential of AI-driven predictive modelling to support early risk identification, targeted rehabilitation planning, and evidence-based policy development within juvenile justice management.
Copyright (c) 2026 Open International Journal of Informatics
Leveraging Artificial Intelligence to Mitigate Supply Chain Vulnerabilities: Insights from the Malaysia Supply Chain Pressure Index (MSCPI)
The increasing complexity of global supply chains has exposed economies to systemic vulnerabilities arising from pandemics, geopolitical tensions, logistics constraints, commodity price volatility and climate-related disruptions. Malaysia is highly sensitive to these pressures because its trade, manufacturing, port and logistics systems are closely linked to regional and global production networks. Previous work on the Malaysia Supply Chain Pressure Index (MSCPI) provides a Malaysia-specific measurement of supply chain stress using Principal Component Analysis and indicators related to transportation, trade, production, logistics, labor, climate and macroeconomic conditions. However, an index-based monitoring approach remains primarily descriptive unless it is connected to predictive analytics and decision-support mechanisms. This article proposes an Artificial Intelligence (AI)-enabled framework that leverages MSCPI as a structured analytical input for forecasting supply chain stress, detecting early warning signals, identifying vulnerability drivers and supporting mitigation decisions. The proposed framework integrates MSCPI with machine learning and time-series modelling approaches, including Random Forest, gradient-based models and sequence learning, to capture nonlinear and lagged relationships between macroeconomic indicators and supply chain stress. The article argues that AI can transform MSCPI from a retrospective stress indicator into a forward-looking policy intelligence tool. The discussion shows that transportation costs, trade fluctuations, industrial production, labor dynamics, exchange rate movements and climate variables are useful inputs for risk prediction. The study contributes conceptually by linking index construction, AI-based prediction and resilience-oriented policy action within the Malaysian supply chain context. It offers practical guidance for policymakers, industry stakeholders and researchers seeking to strengthen proactive supply chain vulnerability mitigation.
Ahmad Azam Sulaiman @ Mohamad Sulaiman @ Mohamad
Building Information Systems for the Digital Firm: Information Systems Development as a Planned Organizational Change
In today’s fast-paced business environment, the implementation of digital transformations is becoming a key element for companies to gain a competitive advantage, improve operational integration, and increase operational efficiency via the adoption of advanced technologies and digital platforms. However, despite the substantial investment in IT by companies across all sectors, many IT-enabled change initiatives have failed to produce the desired organizational results. Thus, it is clear that developing technology capabilities alone is not enough. This study will examine the process of implementing new information systems as a formalized, organization-wide change initiative in the context of digital transformations. In addition to synthesizing relevant prior literature in the Management Information Systems area, this study utilizes a single-case example of an Enterprise Resource Planning (ERP) project implemented in the banking industry to demonstrate how the approach used in developing systems interacts with organizational factors, including leadership, organizational culture, and the use of change management processes. The study’s findings indicate that a company’s ability to successfully implement a digital transformation will depend on a continued commitment from leadership to engage with the system development process, aligning the system development process with the overall organizational strategy, and proactively managing the social/organizational dynamics associated with the system development process. The study also illustrates that system development initiatives should be treated as long-term organizational change initiatives rather than as short-term, independent technology projects. Finally, this research adds value to the MIS body of knowledge by supporting the notion that information systems development should be viewed through the lens of an organizational change process and provides practical guidance for managers involved in planning and executing digital transformation initiatives.
Smart Contracts in Shariah-Compliant Human Milk Sharing
This article explores the integration of smart contracts and blockchain technology in supporting Shariah-compliant human milk sharing. Donor milk sharing in Muslim communities presents unique challenges due to the Islamic concept of milk kinship, which establishes familial bonds through breastfeeding and influences marriage eligibility. Traditional recordkeeping practices related to human milk sharing (HMS) are either rarely practiced or lack systematic implementation, increasing the risk of unintentional violations of milk kinship rules and undermining trust in donor milk programs. This study aims to examine how blockchain-enabled smart contracts can address these gaps in a Shariah-compliant manner. Through a conceptual analysis drawing from Islamic jurisprudence and existing blockchain literature, this study discusses the mechanisms by which smart contracts can automate consent, verify donor and recipient identities and preserve kinship records. The discussion reveals that smart contracts offer a promising framework that aligns with Islamic principles of transparency, trust and privacy in managing milk kinship. Ultimately, this research highlights a meaningful intersection of faith, ethics and technology, offering a foundation for Shariah-compliant and ethically sound healthcare practices.
Knowledge Mapping for Intelligent Recommendation in Earthquake Disaster Response: A VOSviewer-Based Approach
Effective disaster response requires timely access to relevant information and intelligent tools to interpret it. This study explores the relatively underexplored application of VOSviewer, originally designed for bibliometric analysis, as a knowledge mapping and recommendation tool in the context of earthquake disaster management. Using a dataset of tweets related to the 2023 Turkey–Syria earthquake, the study applies binary and full counting techniques to identify frequently occurring terms, thematic clusters, and key term relationships. The findings reveal dominant concerns such as humanitarian aid, emotional and religious responses, and geopolitical references, along with the notable influence of public figures. By repurposing VOSviewer for crisis-related social media analysis, the research demonstrates its potential as an intelligent analytical and decision-support tool, capable of guiding emergency response strategies. The study contributes to the growing field of crisis informatics by highlighting how knowledge mapping can enhance situational awareness and inform targeted, data-driven disaster response efforts.
Machine Learning–Driven Prediction of Asthma Exacerbations: A Step Forward Toward Personalized Care — A Narrative Review
Asthma exacerbations present a major difficulty in asthma management, necessitating the development of effective strategies for timely intervention and prevention. This study examined recent advances and knowledge regarding using machine learning (ML) methods to predict asthma exacerbation in different populations. Through a thorough analysis of existing literature, the study investigates various ML methods, including specific risk factors and biomarkers, and their potential impact on clinical practice. Recent studies have shown that ML can effectively predict exacerbation by utilizing patient demographics, clinical characteristics, environmental factors, and biomarkers. Innovative approaches for extracting distinctive features, ensemble learning techniques, and personalized, predictive models have emerged as potential options for early prognosis and customized treatments. Environmental factors significantly influence exacerbation prediction, emphasizing the importance of incorporating diverse datasets and environmental exposures. While challenges exist in acquiring data, interpreting models, and validating results, ML methods offer valuable tools to improve asthma treatment and enhance patient outcomes. Future advancements in the field will focus on integrating various data types, developing personalized predictive models, and improving model interpretability to integrate them into the clinical workflow seamlessly. By employing proactive, personalized, and data-driven management techniques that cater to each patient’s unique needs and align with population health goals, ML has the potential to revolutionize asthma care.
Optimizing Mid-Sized Open-Weight Large Language Models: Fine-Tuning Strategies for Cost-Efficient AI Solutions
Large Language Models (LLMs) hold strong potential for enterprise customer service automation, particularly for schema-driven and multi-turn dialogue tasks. Yet, real-world adoption requires high accuracy while operating under strict computational and memory constraints. This study evaluates Low-Rank Adaptation (LoRA) and Quantized LoRA (QLoRA) as Parameter-Efficient Fine-Tuning (PEFT) methods for enhancing the LLaMA-8B model in structured automation involving function-call classification and JSON interactions. Experiments compared LoRA, QLoRA, and a zero-shot baseline across function-call accuracy, JSON validity, slot extraction, inference speed, and memory usage. LoRA achieved the best balance, with 84.62% F1-score in function-call accuracy and 95.38% compliance with JSON/schema guidelines. QLoRA reached perfect JSON validity (100%) but performed poorly in recognizing functions, recalling only 6.15%, making it overly conservative for real-world deployment. Both methods struggled with escalation intent detection. Findings highlight the e-off between accuracy and quantization efficiency in PEFT approaches. While QLoRA offers clear benefits in memory and latency, LoRA provides more reliable accuracy with modest resource overhead. This work offers practical guidance for selecting PEFT methods, identifying LoRA as a suitable option for deploying LLMs in structured enterprise customer service automation under resource constraints.
Towards Intelligent Aquatic Health Monitoring in Malaysia’s Coastal Waters: AI‑Driven Harmful Algal Bloom Forecasting with IoT and Cloud Infrastructure
Harmful Algal Blooms (HABs) pose significant threats to aquaculture, marine ecosystems, and coastal economies, requiring timely and reliable monitoring approaches for early detection and response. Conventional water quality monitoring methods are often limited by high operational cost, delayed data acquisition, and insufficient forecasting capability for dynamic coastal environments. This study presents an integrated intelligent aquatic health monitoring system that combines Internet of Things (IoT)-based water quality sensing, cloud-based real-time data management, and artificial intelligence (AI) models for HAB monitoring in Malaysian coastal waters. The proposed system employs multi-parameter sensors connected through ESP32 microcontrollers for continuous monitoring of key water quality indicators, with data transmission via MQTT to a cloud dashboard for visualisation and remote access. Field validation was conducted at Sungai Geting, Kelantan, by comparing prototype sensor readings against benchmark YSI ProDSS measurements. Two AI models, namely an Adjusted Combined Model (ACM) integrating Radial Basis Function Networks (RBFN) and Fuzzy C-Means clustering, and Long Short-Term Memory (LSTM), were evaluated for chlorophyll-a forecasting and HAB prediction. Experimental results showed that ACM achieved superior short-term predictive performance with lower RMSE and MAE, while LSTM demonstrated competitive performance for temporal sequence modelling. The findings demonstrate the potential of integrating IoT and AI to support cost-effective, real-time, and predictive HAB monitoring for sustainable aquaculture management and coastal environmental surveillance.
Development of a Multimodal Testbed for Dataset Collection in Detecting DGA-based Botnet Attacks Using DNS Queries, Network Traffic, and CPU Power Consumption
The detection of Domain Generation Algorithm (DGA)-based botnets in Internet of Things (IoT) environments poses significant challenges due to the dynamic and evasive nature of these botnets. Traditional detection approaches that rely primarily on single data sources such as DNS logs or network traffic often fail to capture the complex, multi-stage behavior of modern botnets. This research presents the development of a dedicated IoT-focused testbed designed to capture a comprehensive multimodal dataset integrating DNS query logs, network traffic data, and CPU power consumption. The primary contribution of this work is the creation of a phase-labelled dataset that categorizes data into three distinct stages: Normal operation, Command and Control (C&C) communication, and Attack execution. This structured labelling provides valuable temporal insights into botnet behavior and supports the development of machine learning models with early-stage detection capability, potentially identifying threats during the C&C phase, before the attacks are launched. Initial analysis and visualizations of the collected data reveal distinct behavioral patterns across power consumption, DNS activity, and network traffic. Notable findings include the identification of high correlated features within the network traffic and DNS query datasets, together with observable phase-dependent variations in CPU power consumption corresponding to different botnet activity stages. These insights suggest that integrating diverse data modalities can significantly enhance the accuracy and robustness of botnet detection in IoT environments.
Evaluating User Experience in Mobile Educational Games: A Case Study on Learning Research Methodology
User Experience (UX) in mobile educational games is essential for ensuring both learning quality and player engagement. However, most UX studies have focused on entertainment games, leaving limited understanding of UX in educational contexts. This study identifies key UX components for mobile educational games and proposes a model specifically for evaluating them. A quantitative post-game questionnaire was administered to 25 learners who had taken or were taking Research Methodology course. The instrument covered seven UX components: Challenge, Control, Motivation, Clear Goals, Playability, Content, and Knowledge Enhancement. Descriptive analysis using SPSS showed consistently positive results across all components, with Clear Goals (M = 4.37) and Playability (M = 4.26) rated highest. The findings highlight which UX features most strongly support engagement and learning, offering practical guidance for designers to balance educational content with enjoyable gameplay in mobile educational games.
Machine Learning Classification for Acoustic Mix Identification in Styrofoam Lightweight Concrete: A Feature Sensitivity Study
Concrete has long been the backbone of modern construction, yet the demands placed on building materials have changed considerably. Strength and structural integrity remain essential, but acoustic performance, how effectively a structure manages sound, has become a serious design consideration. This study takes that challenge as its starting point, examining Styrofoam-based lightweight concrete in which expanded polystyrene beads partially replace fine aggregate to improve sound absorption. Four concrete mixtures (FS, ST1, ST2, and ST3), were prepared with progressively increasing Styrofoam content and characterised for compressive strength, flexural strength, and sound absorption coefficient (α) using the impedance tube method (ASTM E1050-12). A dataset of 312 records was compiled from these four unique material compositions, each tested across 13 frequency levels (100–1600 Hz) and six acoustic panel configurations. Rather than predicting continuous acoustic absorption values, this study focuses on identifying between concrete mix types based on their measured material and acoustic characteristics using machine learning (ML) classification models. The acoustic behaviour of Styrofoam-based lightweight concrete is shaped by the nonlinear interaction of pore geometry, bead size distribution, and frequency-dependent viscous losses relationships that conventional empirical models cannot easily capture. ML offers a data-driven route to material identification that sidesteps those modelling constraints. Four algorithms k-Nearest Neighbours (k-NN), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Logistic Regression (LR), were evaluated using 10-fold cross-validation in Orange Data Mining 3.40.0, through two complementary analyses: Analysis 1 with the full feature set, and Analysis 2 restricted to acoustic features only. In Analysis 1, ANN achieves perfect CA = 1.000 and AUC = 1.000, while SVM reaches CA = 0.923. However, both k-NN and Logistic Regression return CA = 0.250, identical to random chance, due to the absence of internal feature normalisation in Orange. A feature sensitivity analysis confirms that ANN's perfect result is mechanistically explained by class-unique compositional features, not acoustic learning. In Analysis 2, removing those identifying features drops all models to or near random chance (CA = 0.21–0.25; balanced baseline = 0.25). A regression baseline confirms that continuous α prediction is not currently feasible (R² ≤ −0.32). The main contribution lies not in the accuracy numbers but in the dual-analysis evaluation framework, a transparent approach that clearly separates what ML can and cannot do with small experimental datasets. Experimentally, ST2 (1:1.5:2.5) emerged as the most balanced mix.
From Edge to Control: Benchmarking Software Defined Network Performance in IoT Networks
Software Defined Networking (SDN) is an emerging technology that addresses networking complexity and management by separating the control plane from the data plane in switches and routers. This approach introduces key features such as centralized control and global network visibility, which support automation. In the context of the Internet of Things (IoT), SDN helps manage the wide variety of participating devices. By combining SDN controllers, Open vSwitch, and network virtualization, it offers significant operational and management benefits for IoT architectures. However, despite these advantages, existing studies have largely focused on conceptual models or simulated environments, providing limited empirical data on SDN controller performance within real IoT network conditions. This lack of experimental benchmarking makes it difficult to determine how different controllers behave under varying network loads and scales. Addressing this gap is essential to ensure reliable controller selection and deployment in IoT ecosystems. This study investigates SDN controller implementation in IoT networks, focusing on performance evaluation using two controllers, OpenDayLight and Floodlight. Performance is measured in terms of throughput and latency across three scenarios. Unlike prior comparative studies, this work provides a practical benchmarking framework using a real IoT testbed built on Raspberry Pi and Open vSwitch, offering new insights into controller behavior from the network edge to the control layer.
Factors influencing the adoption of IoT healthcare products and services: An empirical study of the end consumers
Internet of Things (IoT) technology has been increasingly applied across numerous industries. The healthcare sector, more than many others, requires advanced technologies such as IoT to enhance quality of life and improve access to healthcare and emergency services for millions of people. IoT healthcare technology enables providers to enhance the quality of patient care, communication, and diagnostic accuracy while enhancing efficiency and reducing costs. Despite the immense opportunities offered by IoT healthcare solutions, their adoption among end users remains limited due to concerns about privacy, perceived usefulness, complexity, ease of use, and high costs. Therefore, this study examines the main factors influencing customers’ adoption of IoT healthcare products and services by integrating constructs from TAM, IDT, and Privacy Calculus Theory. The demographic data were analysed in SPSS, whereas SmartPLS was used for confirmatory factor analysis (CFA), partial least squares (PLS), and structural equation modelling (SEM). The findings indicate that perceived usefulness (PU), attitude, and observability significantly positively affect patients’ willingness to adopt IoT-based healthcare technology. In contrast, perceived privacy risk, trialability, and cost did not have a substantial impact on adoption intention. The results of this research offer multiple theoretical and practical insights for the healthcare sector, as well as for researchers, policymakers, and product developers focused on implementing smart healthcare technologies
Transformer with Technical Indicators for Long-Term Stock Market Prediction
Accurate long-term stock market prediction is challenging due to the dynamic, non-linear, and volatile nature of financial time series. This study proposes a Transformer-based forecasting model that integrates both short- and long-term technical indicators to improve prediction accuracy on the Kuala Lumpur Stock Exchange (FBM KLCI) index. By leveraging the self-attention mechanism, the model captures both immediate price fluctuations and persistent trends, while positional encoding and stacked encoder blocks enable effective sequential modeling. The proposed approach is compared against several baselines, including an LSTM network, a Transformer using only OHLCV data, and Transformers with either short-term or long-term indicators alone. Evaluation metrics include Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), as well as training and testing times. Experimental results show that the proposed dual-indicator Transformer consistently outperforms all baselines, achieving the lowest MAE (17.14), RMSE (21.29), and MAPE (1.13%), demonstrating its ability to balance responsiveness to short-term changes with long-term trend stability. While the model requires longer training due to increased complexity, the substantial gains in predictive performance justify the computational cost. These findings highlight the effectiveness of combining multiple technical indicators within a Transformer model for long-term stock market forecasting and provide a foundation for future research incorporating macroeconomic, sentiment, and real-time data to enhance predictive power and generalizability.
Meor Muhammad Muaz Meor Mohammad Shariffudin
Enhancing Invoice Management Transparency and Efficiency Through AI Visualization
Invoice processing systems are critical components of financial operations. They are responsible for managing the receipt, obtaining validation, or approval, and posting of vendor invoices within the Procure-to-Pay cycle. Despite having high service level agreement compliance rates, the current semi-automated invoice processing system lacks real-time visibility in individual staff productivity. This causes delays, recurring accuracy errors, and operational inefficiencies. Manual data consolidation and limitations in optical character recognition accuracy further hinder timely decision-making. Therefore, there is a need for an intelligent, automated solution to enhance transparency and streamline performance monitoring. This study adopts a Design Science Research methodology to develop and evaluate an AI-supported interactive dashboard for invoice processing within a Shared Services Centre. The operational framework consists of five structured phases: investigation, research design, data collection and analysis, dashboard development, and reporting. The proposed solution integrates AI-driven data extraction with dynamic visualisation capabilities in Power BI, guided by Shneiderman’s Eight Golden Rules to ensure usability, clarity, and human-AI collaboration. The dashboard comprises four core modules focused on processor performance, turnaround time tracker, processing time variation, and workload distribution. Findings demonstrate that the dashboard enables real-time performance monitoring, facilitates data-driven decisions, and improves transparency across financial operations. This research contributes a practical tool to address current system limitations and sets the stage for future enhancements, including integration with SAP Intelligent Robotic Process Automation (iRPA) and real-time monitoring features for proactive process management.
Models, Constructs, and Methods in mHealth User Experience Research: A Systematic Review
The global mobile health (mHealth) market is rapidly expanding, with user experience (UX) crucial for user retention. However, previous studies have mostly focused on usability and paid little attention to UX perspective. This review systematically examines theoretical models, UX constructs, and methodological approaches in mHealth UX research. A systematic literature review (SLR) across 12 databases yielded 6,739 records; 43 empirical studies met inclusion criteria. In accordance with SLR guidelines, data were extracted and synthesized, followed by qualitative analyses. Only three studies applied UX models, indicating that theory-driven approaches remain limited in this field. In contrast, most studies relied on standardized questionnaires, through which thirty unique UX constructs were identified, with commonly examined constructs including Information Quality, Aesthetics, and Attractiveness, and less frequently studied constructs including Privacy, Utility, Flexibility, Complexity, and Context. All studies employed mixed methods. This review demonstrates that UX research in mHealth remains fragmented, with limited theoretical grounding, uneven construct coverage, and methodological reliance on questionnaires. Addressing these gaps requires integrating theory-driven models with context-sensitive evaluation tools, paving the way for more rigorous, inclusive, and practically relevant mHealth solutions.
The Ethical and Social Issues in Information Systems from Data Use, Surveillance, and ArtificialIntelligence
Digital technologies are rapidly transforming organizational operations, with Artificial Intelligence (AI) emerging as a key driver of innovation across multiple sectors. As organizations increasingly rely on Management Information Systems (MIS) to enhance productivity and service delivery, ethical concerns related to data use, surveillance, and AI have become more prominent. This paper examines the ethical and social implications of AI integration in MIS, focusing on issues of privacy, information systems management, and organizational responsibility. The study adopts a comparative analysis using ethical frameworks, including deontology, utilitarianism, the ACM Code of Ethics, and Laudon and Laudon’s Five Moral Dimensions of Information Systems, to evaluate whether current technological practices align with moral and social standards. Empirical examples, such as the governance of MySejahtera, data breaches involving Maybank, ASTRO, and the Election Commission, and the use of facial recognition technologies in airports, are analyzed. The results reveal significant gaps in transparency, accountability, and system management within both public and private institutions. These shortcomings increase organizational and societal risks associated with digital information systems. This paper concludes by recommending stronger ethical governance, enhanced regulatory frameworks, improved monitoring mechanisms, and increased public awareness to promote the responsible and sustainable use of AI in MIS.
Rice Price Prediction in Province Nusa Tenggara Barat (NTB) Using Comparison Of Linear Regression And Random Forest Algorithms
This research provides transparent prediction accuracy in rice industry management and can be used to more accurately forecast prices in the West Nusa Tenggara region. To determine whether the model provides better prediction accuracy, the researchers analyzed and forecasted rice prices using two machine learning algorithms: Linear Regression and Random Forest. Forecasting rice prices is difficult because the elements that support changes in rice prices, such as planted land, production levels, consumption levels, currency (rupiah) volatility, and the volume of rice imports into Indonesia, are interrelated. Based on the study, Random Forest outperformed Linear Regression, with an R² value of 0.710, indicating a better model fit. In addition, the Random Forest algorithm shows a lower error rate, which is reflected in the RMSE of 1038,394. The dataset used for this study covers the period 2006 to 2021 and is sourced from various official institutions, including the Central Bureau of Statistics and Bank Indonesia
Copyright (c) 2025 Open International Journal of Informatics
How existing machine learning models for DDoS detection differ in performance and accuracy when applied to synthetic versus real-world network traffic datasets
Machine learning–based DDoS detection systems frequently report exceptionally high performance, often exceeding 98–99% accuracy. However, such results are predominantly derived from synthetic, laboratory-generated datasets that fail to capture the complexity, variability, and noise of real operational environments. This phenomenon is not unique to cybersecurity; similar patterns have been observed in applied health technologies such as remote blood pressure monitoring, where machine learning models trained on controlled clinical datasets often demonstrate inflated performance but struggle to generalize to real-world home monitoring conditions. This paper empirically demonstrates how multiple machine learning models achieve near-perfect performance when evaluated on controlled, laboratory-created DDoS datasets. Using two widely adopted benchmark datasets, the evaluated models achieved accuracies close to 99%. However, when the same learning methods were applied to a real-world dataset constructed from 28 months of unsolicited network traffic, model accuracy declined to approximately 92%.
A Survey of Cloud-Enabled Machine Learning in Financial Services
The integration of cloud computing and machine learning (ML) is reshaping financial services by enabling scalable, real-time, data-driven decision-making. This survey reviews cloud-enabled machine learning (ML) developments from 2021 to 2025, with a focus on financial sector applications. Cloud services provide elastic computing, distributed training, and low-latency analytics, which are essential for processing large-scale financial datasets. Financial institutions increasingly leverage these capabilities to enhance credit scoring, fraud detection, risk forecasting, and customer personalization. Advances in deep learning, ensemble methods, federated learning, automated ML, and explainable AI (XAI) are improving model accuracy, operational transparency, and regulatory compliance. MLOps pipelines further streamline the deployment, monitoring, and lifecycle management of ML models in dynamic environments. Key challenges persist, including data privacy, integrating legacy systems, regulatory constraints, and latency-sensitive operations. This survey categorizes the literature into four themes. (i) Cloud architecture and service models tailored for ML workflows, (ii) supervised and unsupervised ML techniques applied in finance, (iii) comparative analysis of real-world use cases, and (iv) performance evaluation metrics and trade-offs. Unlike prior reviews, this review uniquely synthesizes recent trends across cloud-native ML technologies and maps their practical implications in regulated financial environments. Future directions include exploring edge-cloud coordination for time-critical tasks, building robust models against adversarial data, implementing privacy-preserving mechanisms, and establishing standardized governance frameworks. This survey serves as a comprehensive reference for researchers and practitioners seeking to leverage cloud-based machine learning (ML) in financial services.
Hate Speech Detection of Manglish (Malay + English) in X (Twitter) Using XLM-RoBERTa and XLNet
This study explores hate speech detection in Manglish, a code-mixed language of Malay and English widely used among Malaysian social media users. The main objective is to develop and evaluate deep learning-based models capable of identifying hate speech in Manglish tweets. A dataset of 9,241 manually annotated tweets was collected from X (formerly Twitter) and processed using the Malaya NLP library for code-mixing detection. Two state-of-the-art transformer-based models, which are XLM-RoBERTa and XLNet, were fine-tuned under both imbalanced and upsampled training conditions. Evaluation metrics, including precision, recall, F1-score, accuracy, and evaluation loss, were used to assess model performance. Results indicate that XLNet achieved the highest F1-score and fastest inference time under imbalanced conditions, while XLM-RoBERTa demonstrated stronger generalization with lower evaluation loss. After upsampling, both models improved significantly, achieving balanced performance across both classes. This research contributes a novel annotated Manglish dataset and highlights the importance of context-aware multilingual models for hate speech detection in code-mixed social media posts.
A Proposed Framework for Mitigating Software Supply Chain Attacks in Defense Organizations
A software supply chain attack is a cyber-attack where the attack targets the supply chain to damage the security of the software and the target environment. Therefore, this research addresses the critical issue of software supply chain attacks, which exploit vulnerabilities in third-party vendors, leading to third-party compromise and software dependencies posing significant risks to national security, operational capabilities, and organizational trust in Defense organizations. The key importance of this research lies in proposing a framework to mitigate software supply chain attacks for the Defense organization, a high-value entity in the defense sector. ​​The research employs a literature review, later conducting qualitative methodology through semi-structured interviews, and thematic analysis. Data collection will involve engaging participants from academia, industry, and military personnel in cybersecurity domains. The security framework is constructed by integrating insights from military-specific policies, global frameworks, and legal aspects in a few countries, followed by participant opinion and expert validation to ensure its comprehensiveness and relevance. The expected findings include identifying key components and vulnerabilities influencing software supply chain attacks, proposing a tailored framework for the Defense organization, and evaluating the proposed framework. The originality of this research lies in its focus on the Defense organization, adapting and integrating elements from global frameworks and military-specific policies to address unique challenges in a defense context. The practical significance of this research extends to scholars, industry professionals, and Defense organizations. The proposed framework will serve as a strategic tool for the Defense organization to enhance cybersecurity resilience, streamline decision-making processes, and foster trust in third-party engagements.
A Review of Detection Challenge for Signature and Anomaly-Based Detection in Detecting HTTP DDoS Attacks
Distributed Denial of Service (DDoS) attacks have become one of the most serious concerns in the cybersecurity domain due to their ability to mimic legitimate traffic. The attack is significantly challenging to detect when occurring at the application layer because it exploits genuine request patterns, forged headers, automated attack tools, and public proxies to mimic legitimate traffic, making detection extremely difficult. This paper reviews signature-based and anomaly-based detection techniques utilized by prior studies to detect HTTP DDoS attacks. The review output reveals that signature-based detection methods are effective for known attack patterns, while anomaly-based detection excels at detecting previously unseen behaviors. However, the signature-based detection struggles to recognize new attack patterns, unlike anomaly-based detection. Both of these detections also experience significant challenges in differentiating between authentic users and automated attack tools when public proxies are used. This review concludes that signature-based and anomaly-based detection techniques remain inadequate for detecting the attack. This review also suggests that future research should focus on a hybrid detection to detect request headers in real-time and the multi-version HTTP protocol to improve detection accuracy.
A Review Internet of Things (IoT) Security Challenges
The Internet of Things (IoT) has achieved widespread adoption among millions of individuals today, driven by the growing use of smart devices, including smartphones, smart cars, lighting, air conditioning, and high-speed networks. IoT is applied in various areas, including industries, healthcare, homes, agriculture, oil and gas, and education, to name a few. However, due to the many connections of IoT devices, there is no exact method or algorithm for overseeing communications and security concerns, such as Mirai-based botnet Attacks, AI-powered IoT Attacks (Emerging Threats), Satellite & Space IoT attacks, heterogeneity challenges, and Distributed Denial of Service (DDoS) attacks, which have led to enormous challenges in the IoT environment. The paper divides the challenges into three categories: recent IoT attacks, general security challenges, and existing attacks in IoT. Besides, this paper aims to review and investigate the security challenges in the IoT environment and propose various solutions to mitigate these challenges. Based on the analysis of recent IoT attacks, satellite and space attacks account for a 70% share, while in the broader security challenge, heterogeneity accounts for 80%. In existing IoT attacks, the DDoS attack poses a 90% risk.
Access Control in IoT using Hyperledger Fabrics Blockchain for Temperature, Humidity, and Pressure
IoT system is widely used in industry and gives many benefits to the user. It helps a lot with monitoring and provides real-time information from sensors to the main node. While the IoT brings benefit, it also has its risks, vulnerabilities, and weaknesses. Based on these flaws, it can potentially be manipulated by the hackers to access and deny the services of the IoT system. Having access control through Hyperledger Fabrics Blockchain, it provides a permission blockchain framework that allows only registered devices or sensors to communicate and store the sensor data, which is temperature, humidity, and pressure. This paper will describe generally the integration of IoT using Sense Hat emulator and Hyperledger Fabrics Blockchain setup to show only the legitimate sensors are allowed to be stored in the ledger. In this study, it shows that access control can be managed using blockchain, especially for IoT, which have lacks resources to manage the traditional access control.
Digital Transformation : Establishing the Core Requirements of Contractual Protocols for BIM Integration in Water Treatment Plant Development
The integration of Building Information Modelling (BIM) in water treatment plant (WTP) projects presents significant opportunities for improving design coordination, construction efficiency, and lifecycle asset management. However, the absence of robust contractual protocols tailored to BIM implementation often leads to ambiguity in roles, responsibilities, and data ownership, increasing the risk of disputes and inefficiencies. This study aims to establish the essential components of a contractual protocol specifically designed for BIM integration in WTP projects. A systematic literature review of ten key studies was conducted, covering BIM contractual frameworks, dispute resolution mechanisms, data management standards, and sector specific applications in the water industry. Findings reveal that effective contractual protocols require five core components: (1) clearly defined roles and responsibilities, (2) standardized workflows and data exchange formats, (3) intellectual property and data ownership clauses, (4) dispute resolution procedures linked to BIM deliverables, and (5) performance and compliance monitoring mechanisms. A proposed framework is presented to guide stakeholders in drafting BIM enabled contracts for WTP projects, reducing ambiguity and fostering collaborative project delivery.
Advancing Public Complaint Management in Malaysia: A Data-Driven Dashboard Solution for SISPAA
The Public Complaints Bureau (PCB) of Malaysia plays a pivotal role in enhancing public service delivery by managing citizen feedback through the Public Complaint Management System (SISPAA). Despite its effectiveness in complaint tracking, SISPAA’s limited data visualization capabilities reduce operational efficiency, forcing complaint managers to manually compile and interpret reports—often leading to delays and inaccuracies. This study addresses these limitations by designing and evaluating an interactive dashboard developed in Microsoft Power BI, which integrates directly with SISPAA’s complaint database. The dashboard enables real-time monitoring, dynamic filtering, and role-specific visualizations for both complaint handlers and senior leadership. Using the Goal-Question-Metric (GQM) framework, the solution was assessed by end-users, with over 85% reporting improved usability, faster access to insights, and reduced reporting time. These findings underscore the dashboard’s potential to enhance transparency, accountability, and decision-making, thereby aligning with Malaysia’s digital governance initiatives.
Optimizing Machine Learning Models for Cardiovascular Drug Candidate Screening with XGBoost and Successive Halving
Thrombin, a key enzyme in the blood coagulation process, is an important target in the development of anticoagulant therapies. This study proposes an efficient and accurate quantitative structure–activity relationship (QSAR) modeling framework that combines the XGBoost algorithm with the Successive Halving (SH) method for hyperparameter optimization. A dataset of 3,145 compounds was collected from the ChEMBL database, and molecular descriptors were generated using the Mordred calculator. After preprocessing and feature selection, the SH-tuned XGBoost model achieved the highest performance, with an accuracy of 85.69%, F1-score of 80.93%, and ROC-AUC of 0.917. Compared to baseline and Random Search-tuned models, the SH-tuned model demonstrated superior predictive accuracy and significantly reduced training time. Confusion matrix analysis confirmed the model’s strong sensitivity and balanced classification of active and inactive compounds. These results illustrate the effectiveness of combining gradient boosting with efficient hyperparameter optimization for virtual screening. The proposed framework reduces computational cost without compromising model quality and can support early-stage drug discovery. Future extensions may include regression modeling, expanded datasets, and integration of biological or pharmacokinetic data to further improve performance and applicability.
EcoGold Monitor: An IoT-Based System for Real-Time Vermicompost Monitoring
EcoGold Monitor, an Internet of Things (IoT)-based system designed for real-time compost and soil condition monitoring. The prototype employs an ESP32 microcontroller integrated with a DS18B20 digital temperature sensor and a capacitive soil moisture sensor to collect composting data, environmental data pertinent to plant health and precision agriculture. Sensor data are acquired periodically and transmitted over Wi-Fi to the Blynk IoT platform, which provides cloud-based visualization, logging, and remote access through a web or mobile interface. To enhance user accessibility, the system incorporates WiFiManager, enabling dynamic network configuration without hardcoded credentials. This feature allows non-technical users to connect the device to any available Wi-Fi network through a temporary access point interface. Data are visualized in real-time via graphs and historical logs, allowing users to make informed decisions regarding irrigation scheduling and microclimate management.
A Pilot Analysis of Factors Affecting Satisfaction of Using Interactive Digital Whiteboard Among University Postgraduate Students
The COVID-19 lockdown has sparked educational approach all around the world and transformed the traditional learning to online engaging learning. Even so, this transformation has demonstrated the need to implement interactive e-learning technologies on many occasions, and this allows lecturers to help students flexibly develop their soft and technical skills required by the course they are attending. As a result, more online learning, like ODL and using a blended learning method, emerged. Therefore, it is important to understand students' satisfaction of using the interactive online technology into their learning be it as either a means for learning the online lectures or group task meeting assistant. This study piloted a survey on fifty Universiti Teknologi Malaysia, Kuala Lumpur (UTMKL) postgraduate students who attended online courses. This study aims to explore the clarity, reliability and validity of the questionnaire before a full-scale study takes place. An evaluation of the instrument's reliability was conducted using Cronbach's Alpha, alongside an evaluation of indicator reliability through outer loadings. The study revealed that all factors namely perceived usefulness, perceived ease of use, perceived ease of learning, perceived interaction, perceived motivation and perceived satisfaction are reliable to be postulated as the hypotheses in developing a model to explore how perceived usability factors influence postgraduate student satisfaction with the use of the digital whiteboard at UTMKL. The results of this pilot research offer important information about possible factors from usability perspectives to measure satisfaction of using interactive learning tools in teaching and learning. To create a thorough model of satisfaction factors, future studies will increase the sample size and apply inferential statistical methods. Promoting interactive learning at UTMKL and other comparable environments can be explored further by this study, which also may add the expanding corpus of research on the use of technology in education.