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Expert system for motorcycle damage diagnosis using certainty factor and case-based reasoning methods

10.11591/ijict.v15i4.pp1829-1840
Anthony Anggrawan , Christofer Satria , Bambang Krismono Triwijoyo , Dadang Priyanto , Hasbullah Hasbullah , Victoria Cynthia Rebecca
Motorcycles are the main mode of transportation that requires maintenance due to damage. Unfortunately, damage detection on motorcycles remains traditional, despite the potential for expert systems and artificial intelligence (AI) to facilitate more straightforward diagnosis for both motorcycle users and mechanics. That is why this research aims to develop a system with an expert system approach to diagnose damage to a motorcycle. This research applies the certainty factor (CF) expert system approach and the case-based reasoning (CBR) AI method. The study found that the expert system using the CBR method outperforms the CF method in diagnosing motorcycle damage. The CF expert system method achieves an accuracy performance of 98%. In comparison, the CBR expert system method achieves an accuracy of up to 100%, or can be equated with human mechanics in diagnosing motorcycle damage. The finding implies that CBR can effectively replace human experts in detecting motorcycle damage.
Volume: 15
Issue: 4
Page: 1829-1840
Publish at: 2026-12-01

Evaluating usability, accessibility, and persuasion in learning management systems

10.11591/ijict.v15i4.pp2011-2024
Samantha Jim'enez , Fernanda Murillo-Mu'noz , Leticia Cervantes , Norma Candolfi , Berenice Fong , Camilo Caraveo , Alan Ram'ırez-Noriega
Learning management systems (LMSs) are widely adopted in engineering education to support content delivery, foster collaboration, and develop digital competencies. However, existing platforms often prioritize functionality over user engagement and accessibility. There is limited research that holistically evaluates LMS usability, persuasive design, and accessibility in the context of technical education, particularly during high-frequency tasks such as assignment submission. This study aims to evaluate and compare the usability, persuasive elements, and accessibility of blackboard, canvas, and Google Classroom during the assignment submission process from the perspective of engineering students. An experimental, between-subjects design was employed with engineering students who each interacted with one of the three LMS platforms. We evaluated usability, persuasiveness and accessibility. Canvas was the most efficient platform, with shorter task completion times and fewer errors. However, it received lower scores in satisfaction and persuasive design compared to Google Classroom, which stood out in credibility and user guidance but showed more variability in efficiency. None of the platforms fully complied with key accessibility criteria, such as keyboard operability and proper form labeling. These findings highlight the importance of designing LMS platforms that balance us ability, persuasion, and accessibility to better support engagement and equity in engineering education.
Volume: 15
Issue: 4
Page: 2011-2024
Publish at: 2026-12-01

Development of KabuTeach: a character-based decision support system for mushroom classification

10.11591/ijict.v15i4.pp1561-1576
Vilchor G. Perdido , Melinda A. Beninsig , Daniel Marvin B. Ramel
Mushroom misidentification remains a persistent public health risk in rural communities, causing poisoning and fatalities worldwide. This study presents KabuTeach, a web-based decision support system (DSS) for classifying mushrooms as edible or poisonous. The system is trained on the UCI mushroom dataset (8,124 instances, 22 categorical morphological attributes) using the categorical Naïve Bayes algorithm with Laplace smoothing (α=1.0) through the Rubix ML framework. The system employed a hybrid interactive structure and event-driven architecture with the model view–controller pattern, enabling real-time classification, human-in-the-loop expert validation, and SMS-based result delivery for low-connectivity rural environments. Model evaluation using a 70:30 holdout method achieved 99.63% accuracy, while 5-fold cross-validation yielded a mean accuracy of 99.66% (±0.1118%). Performance metrics, including a confusion matrix, per-fold results with standard deviations, precision, recall, F1-score, and specificity, confirm model robustness, attributed to the clean and discriminative categorical features of the dataset and the use of Laplace smoothing. Usability testing with 102 respondents using the USE questionnaire achieved a grand mean of 5.93 (agree). Reliance on the North American UCI dataset limits direct Philippine applicability; future work will focus on locally sourced, expert-labeled datasets. KabuTeach is a reliable, interpretable, and user-centered DSS that bridges traditional knowledge and modern ICT for safer rural mushroom foraging.
Volume: 15
Issue: 4
Page: 1561-1576
Publish at: 2026-12-01

Adaptive weighted multi-criteria decision making using modified dijkstra’s algorithm: integrating real-time traffic conditions with hybrid WSM-WPM for dynamic route optimization

10.11591/ijict.v15i4.pp1929-1943
Basorudin Basorudin , Handaru Jati , Nurkhamid Nurkhamid , Antoni Pribadi , Satria Riki Mustafa , Puput Dani Prasetyo Adi
Traffic congestion is a problem that needs to be solved. A smart route optimization system is needed to overcome this problem, not only using traditional methods but also using algorithms that have the ability to find the shortest route. This study proposes a framework system for multi-criteria decision making that adapts and improves the Dijkstra and weighted product method (WPM) algorithms. The Dijkstra algorithm is modified to be more integrated in solving the fastest travel route, anticipating traffic congestion, improving road safety, saving fuel, and providing real-time data. The weighted sum method (WSM)-WPM hybrid framework has the advantage of WSM additive normalization with the zero-dimensional properties of WPM to overcome different measurement units. Experimental results show a significant reduction in travel time and fuel consumption, as well as an increase in route safety percentage. This system maintains computational efficiency, and this research provides a practical and scalable solution for intelligent transportation systems (ITS), improving the driver experience and overall traffic network safety. The results of the congestion avoidance configuration with the highest hybrid score show a result of 0.4573, yielding a viable alternative route, namely (V1→V3→V6→V9→V12→V14).
Volume: 15
Issue: 4
Page: 1929-1943
Publish at: 2026-12-01

Arabic calligraphy style recognition with edge enhancement and attention

10.11591/ijict.v15i4.pp1510-1522
Bilal Abdulrahman Tuama , Farhan Mohamed
Arabic calligraphy style recognition is a challenging task because visually similar styles often share curved strokes, connected structures, and subtle ornamental variations. This study proposes a convolutional neural network (CNN) architecture that combines learnable edge enhancement, squeeze-and excitation (SE) attention, and optional BiLSTM-based sequential modeling for Arabic calligraphy style classification. The edge module is designed to preserve fine-grained stroke boundaries, while the attention mechanism emphasizes informative channel responses and the BiLSTM models width wise contextual dependencies. Experiments were conducted on a public Arabic calligraphy style dataset using a stratified 70/15/15 train-validation test split. The best configuration achieved 98.22% accuracy and 98.20% F1-score on the test subset. These findings indicate that adaptive edge-aware feature learning can improve the discrimination of visually similar Arabic calligraphy styles. Nevertheless, broader validation on additional datasets and repeated runs is still needed to confirm generalizability.
Volume: 15
Issue: 4
Page: 1510-1522
Publish at: 2026-12-01

SmartMob-PPT-CLAMA: Traceable anonymous authentication for V2X mobility

10.11591/ijict.v15i4.pp1954-1963
Haewon Byeon
Smart mobility ecosystems connect autonomous vehicles, shared mobility platforms, electric-vehicle charging infrastructure, pedestrians, and roadside units through real-time V2X messages. This paper redesigns PPT-CLAMA, a privacy-preserving and traceable certificateless anonymous mutual authentication scheme, for high-speed internet of vehicles (IoV) and smart mobility environments. The proposed SmartMob-PPT-CLAMA introduces mobility-adaptive pseudonym pools, RSU-assisted handover caching, lightweight V2X authentication headers, hierarchical traceability, and Sybil aware regional trust filtering. The protocol preserves the original security objectives of anonymity, unlinkability, perfect forward secrecy, known session-specific temporary information security, and lawful traceability, while addressing mobility-specific weaknesses such as long-lived pseudonym tracking, handover re-authentication delay, centralized trace authority failure, high-speed Sybil attacks, and broadcast replay. In the evaluated Raspberry Pi 4B and SUMO/NS-3 workload, the proposed design reduces authentication delay from 1.10 ms to 0.82 ms, reduces communication overhead from 470 bits to 312 bits, and achieves 99.1% handover re-authentication success. The design is mapped to ETSI ITS security, IEEE 1609.2, ISO/SAE 21434, and GDPR Article 32 requirements.
Volume: 15
Issue: 4
Page: 1954-1963
Publish at: 2026-12-01

Smart integrated system for optimal resource utilisation

10.11591/ijict.v15i4.pp1921-1928
Rama Krishna Peddarapu , Vaishnavi Chillara , Sudha Manasa Jagilanka , Maneesh Kanumolu , Manvitha Choutymetla
One of the main obstacles to agriculture development and sustainability is the improper use of such natural resources as water, fertilizer, and energy. In this paper, the concept of smart integrated system for optimal resource utilization (SISORU) will be presented. The idea of the project consists of the development of the web application based on ICT, machine learning (ML), and visualizing technology on behalf of the executive process in agriculture. SISORU differs from existing agricultural solutions by combining the recommendations on crop choice, cost estimation, and resource shortages detection in one solution.
Volume: 15
Issue: 4
Page: 1921-1928
Publish at: 2026-12-01

SSDHR classifier network for brain tumor detection using MRI images

10.11591/ijict.v15i4.pp1459-1468
Kalpana Telkar , K. Anusudha
Brain tumors are the chaotic growth of abnormal brain cells, which is the main cause of cancer worldwide. Symptoms vary depending on tumor size and location, and these tumors can be cancerous or non-cancerous. Early detection and characterization are crucial but challenging due to the brain's complex structure. This study proposes a self-supervised classification deep hierarchical reconstruction network (SSDHR), a combination of generative and discriminative models. This network uses convolutional Kernels of different sizes for processing of input data on separate branches to extract effective discriminative features. A parallel-structured symmetric fusion attention (SFA) module is introduced, combining attention mechanisms for coordinates, time, and frequency to enhance the model's comprehension and feature selection. The SSDHR classifier network achieves the highest accuracy (91.9%) among 10 state-of-the-art classifiers, classifying tumors into 4 classes: pituitary, meningioma, glioma, and no tumor. The model's performance is evaluated using various metrics that demonstrate its effectiveness in brain tumor classification.
Volume: 15
Issue: 4
Page: 1459-1468
Publish at: 2026-12-01

DDoS attack detection on portmap using a bidirectional recurrent neural network method

10.11591/ijict.v15i4.pp1601-1613
Wisnu Adi Putra , Deris Stiawan , Qurhanul Rizqie , Nurul Afifah , Septiani Kusuma Ningrum , Rahmat Budiarto
Portmap is a protocol used in Unix and Linux operating systems to map port numbers to their associated program numbers. In a distributed denial of service (DDoS) attack on portmap, attackers will exploit a weakness in this protocol to send many malicious or manipulative server-running portmap requests Due to the connectionless and unauthenticated nature of UDP, the portmap server will attempt to respond to every request it receives. However, attackers often disguise this so that their IP address is hidden or spoofed. As a result, the server will attempt to reply to requests to a spoofed or invalid IP address, using server resources and causing a denial of service for valid users. A DDoS attack on portmap can be quite damaging. Services associated with portmap may become unresponsive, causing significant downtime for applications or services that rely on the protocol. This study uses the CSE-CIC-IDS 2019 dataset, especially the DDoS attack dataset on portmap. This paper also employs the correlation-based feature selection (CSF) approach to extract significant characteristics from the dataset Bidirectional RNN is used to detect DDoS assaults. Utilizing 30% testing data and 70% training data, the Bidirectional RNN approach can effectively identify DDoS attacks on portmap. Accuracy is 99.9917%, recall is 99.9917%, specificity is 99.9942%, precision is 99.994%, F1-Score is 99.88%, balanced accuracy (BACC) is 99.92%, and matthews correlation coefficient (MCC) is 99.98%. By merging the CFS feature selection technique with the Bidirectional RNN architecture, this work mainly contributes to creating a more accurate and efficient DDoS attack detection method. With this method, the detection process can be carried out faster and more precise, reducing the computational burden without sacrificing detection performance.
Volume: 15
Issue: 4
Page: 1601-1613
Publish at: 2026-12-01

Design framework for higher education certificates using blockchain technology

10.11591/ijict.v15i4.pp1800-1815
Salem S. M. Khalifa , Ali Mohamed E. Ejmaa , Abdulmawla Najih , Ismail Aboulqasim Omar Abumilyana
Digital academic credentials face persistent threats from credential fraud, while traditional centralized verification systems suffer from single points of failure and administrative delays. Although blockchain technology offers immutability and trust, existing solutions present significant research gaps: (1) direct on-chain storage violates data protection regulations; (2) a lack of decentralized identity governance leaves networks vulnerable to unauthorized; and (3) existing literature lacks formal operational algorithms and empirical benchmarking under scalable workloads. To resolve these gaps, this paper proposes a multi-tiered design framework for higher education certificates anchored in design science research (DSR) methodology. The proposed solution bridges these limitations by implementing a privacy-preserving hybrid architecture that combines off-chain encrypted File System storage with on-chain SHA-256 hash anchoring. To address governance vulnerabilities, W3C Decentralized Identifiers are integrated with a smart-contract Issuer Registry and a global revocation list to enforce strict issuer access control. Furthermore, formal algorithmic protocols are established for system enrollment, issuance, and off-chain encryption. Empirical evaluations conducted using Hyperledger Caliper demonstrate a 100% transaction success rate, a stable throughput of 17.7 TPS, and sub-second verification latencies (10.53 ms), validating the framework’s privacy compliance, security, and performance scalability for global academic verification.
Volume: 15
Issue: 4
Page: 1800-1815
Publish at: 2026-12-01

A muzzle-based self-enhancing cow recognition method using YOLO and LLM

10.11591/ijict.v15i4.pp1861-1870
Rifat Sarker Aoyon , Ismail Hossain , JIA UDDIN
In the modern era, using the technology of image processing, people are beingfacilitated in many sectors. Therefore, we have decided to do research on cowrecognition using the methods of image processing. Because cows are prettyessential for human beings, and people from the entire world are being facilitatedby cows. Because of that reason, many technologies have been invented till nowfor the cow. Here we have presented an approach to identify the cow. Like thefingerprint of a human, the cow muzzle is also unique. Due to this reason, wecan recognize the cow based on the muzzle. To recognize muzzles, we have usedYolo and the Structural Similarity Index (SSI). Image processing is a sector ofArtificial Intelligence and data is quite important to train models. Moreover, totrain a model for image processing, it is needed to manage a high-configurationcomputer. But there are many regions in the world where people hardly get suchpowerful machines. To mitigate this issue, we have introduced a unique methodwhere we do not need a large amount of data for the image processing task. Toeliminate the necessity of collecting a huge amount of data, we included a self-enhancing feature, which helps the model to predict accurately and also helpsto improve performance by increasing the amount of data. However, withouta large amount of data, it will be possible to predict appropriately because ofthe presence of self-enhancing features. This certain program helps the modelto improve its capability for delivering faster responses. LLM is responsible toadd the self-enhancing feature. On the other hand, to find out the most suitableYolo model for our research, we have trained data with three different versionsof Yolo: Yolo v7, v8, and v9. We evaluated the performances of these threeversions in terms of Precision, Recall, mAP50, and mAP50-95 and found Yolov9 to be the best among them. It excels with a precision of 0.391, a recall of0.916, a mAP50 of 0.559, and a mAP50-95 of 0.307.
Volume: 15
Issue: 4
Page: 1861-1870
Publish at: 2026-12-01

An explainable AI-based intrusion detection system for cyber threats in stock markets using the SECURE framework

10.11591/ijict.v15i4.pp1726-1735
Anandhi Ramani , Malathy S. , Ramya K.
As electronic trading technology accelerates the way trades occur in financial markets, the ability to manipulate the markets in many different ways has be come stronger through this same technology. Two ways this happens are via spoofing and layering, which pervert the way prices are determined, confuse in vestors, and reduce the level of trust in the integrity of financial markets. Exist ing surveillance mechanisms were predominantly rule-based or relied on opaque machine learning models, limiting their adaptability and interpretability. This study proposed SECURE, an explainable artificial intelligence (AI)-based in trusion detection system designed to detect and interpret spoofing and layering activities in real time. The framework integrated ensemble machine learning models, cybersecurity-inspired monitoring logic, and shapley additive expla nations (SHAP)-based explainability to achieve accurate detection with trans parent decision-making. Using Level-II order book data, the system analysed behavioural and microstructural trading features to classify manipulative pat terns while generating feature-level explanations for each alert. Experimental evaluation demonstrated that the proposed framework achieved high detection accuracy, low false-positive rates, and dependable real-time performance. The results indicated that combining intrusion detection principles with explainable AI improved regulatory interpretability and strengthened the effectiveness of au tomated market surveillance systems.
Volume: 15
Issue: 4
Page: 1726-1735
Publish at: 2026-12-01

Development of an AI-based digital startup incubation model for the empowerment of students and MSMEs towards a creative economy

10.11591/ijict.v15i4.pp1682-1695
Umar Bakti , Maria Septijantini Alie , Iin Marliana
Micro, small, and medium enterprises (MSMEs) continue to face low digital-technology adoption, with conventional incubation programs still relying on generic training and manual assessment rather than automated, evidence-based evaluation. This study develops and validates an AI-based startup incubation model (AI-SIM), an integrated ICT platform functioning as an intelligent decision-support system for MSME digital-readiness classification, adaptive training recommendation, geospatial analysis, and student-MSME experiential learning within one operational environment. Data were collected from 4,276 MSMEs across Lampung Province, Indonesia; after strict two-level deduplication to prevent data leakage, 961 unique records were retained for modeling. A random forest classifier grouped MSMEs into three readiness categories (Traditional, Stable, Digital Ready) using two feature configurations, achieving 33.68% (10 features; 5 fold CV 36.75%) and 30.57% accuracy (7 manuscript-aligned features; 5 fold CV 37.23%), both near the 33.3% random-chance baseline. Feature importance analysis showed predictive weight spread across all seven features, led by digital infrastructure and monthly revenue, with no dominant predictor. The system was validated through field deployment, training, and focus group discussions with 84 student and MSME participants, confirming its practical value as a real-time incubation dashboard, while the classification component requires richer behavioral and transactional data to improve accuracy in future iterations.
Volume: 15
Issue: 4
Page: 1682-1695
Publish at: 2026-12-01

Smart poultry farming with IoT: an in-depth exploration of innovations and trends

10.11591/ijict.v15i4.pp1964-1975
Md. Kamal Ibne Sufian , Md. Fuyad Afzal , Afifa Sultana Offa , Sajeeb Saha , Nazma Tara
The internet of things (IoT) has emerged as a key enabling technology for smart applications such as smart homes, smart transportation, and smart agriculture. In traditional poultry farming, production largely depends on manual observation and human expertise, which often leads to inefficiencies and low productivity. IoT-based smart poultry farming provides automated monitoring and control of environmental parameters such as temperature, humidity, and poultry health using sensors and wireless communication technologies. This paper presents a comprehensive literature review of IoT-based smart poultry farming systems. This study systematically reviews 20 peer-reviewed studies published between 2013 and 2025, surveys existing IoT-based poultry farming solutions with a focus on sensing components, microcontroller platforms, system implementation approaches, and reported challenges in real-world deployments.
Volume: 15
Issue: 4
Page: 1964-1975
Publish at: 2026-12-01

Energy aware clustering and routing for wireless sensor networks using metaheuristics optimization algorithms

10.11591/ijict.v15i4.pp1736-1745
Sagar Mekala , Nagaraju Karinagappa , Mohan Dholvan
Wireless sensor networks (WSNs) and their applications reduce energy consumption to enhance the lifetime of the network. WSN applications require energy-efficient communication, but the network includes resource constraint tiny sensors across monitoring area. Data processing, aggregation, and clustering are energy-intensive, and deployment and discovery of nodes are complex. In order to overcome certain issues in WSN application, optimal selection of fine cluster heads (FCHs) and cluster heads (CHs) and optimal routes to the base station (BS) are proposed, still lot of optimizations are essential to enhance lifetime of the network. This research paper employs bacterial foraging and ant colony optimization (BFACO) to identify optimal gateways (FCHs), CHs, and energy-efficient paths from sensor modes to BS while optimizing node energy consumption. The algorithm dynamically creates routing paths with lower network overhead, enabling long-term network utilization and enhanced lifetime. Performance is evaluated against parallel research algorithms using metrics such as energy-usage, network lifespan, and packet delivery ratio (PDR). An energy-usage (52.8% of energy reduction over best case), packet delivery ratio (8.7% of enhancement over best case), and network lifetime (4.3% of rounds over best case) are WSN performance outcomes for 300 sensors. The BFACO using a clustering and routing algorithm outperforms over parallel research.
Volume: 15
Issue: 4
Page: 1736-1745
Publish at: 2026-12-01
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