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31,377 Article Results

A study of onshore wind turbine spatial arrangement in Thailand

10.11591/ijaas.v15.i3.pp1297-1315
Suparatchai Vorarat , Watcharapong Tantawat
This study examines the impact of wind turbine spatial arrangement on electricity generation efficiency in onshore wind energy farms within Thailand's meteorological and geographical context. An empirical analysis was conducted on 20 operational wind energy farms representing approximately 96% of Thailand's installed onshore wind capacity. Geographic information system (GIS) techniques were applied to measure the spacing and layout of the turbines, and the National Renewable Energy Laboratory's (NREL's) system advisor model (SAM) was employed to simulate the amount of electricity generated each year, considering wake effects, terrain, and site-specific wind resources. Capacity factors were calculated and compared against operational data. Results indicate capacity factors range from 17.12% to 30.75%, with a mean of 23.34%. Research reveals that wind turbine spacing is not the primary determinant of wind energy farm performance in Thailand. When the wind resources and site characteristics are good, the wind energy farms can have good performance from many different spacing setups. On the other hand, layout optimization by itself can't make up for bad wind conditions. These findings demonstrate the need for a more comprehensive design for wind energy farms in areas with moderate wind resources.
Volume: 15
Issue: 3
Page: 1297-1315
Publish at: 2026-09-01

Modeling Indonesia’s regional economic growth for sustainable development planning

10.11591/ijaas.v15.i3.pp1286-1296
Julia Loisa , Suryo Widiantoro , Caecelia Cynthia Limanan , Kezia Kezia , Kenvil Lesmana
This study aims to evaluate Indonesia’s regional economic performance using gross regional domestic product (GRDP) per capita at the provincial level from 2015 to 2021. Utilizing business intelligence technology through Tableau, the study integrates descriptive, predictive, and prescriptive analytics to examine interregional GRDP dynamics. The k-means clustering method is employed to classify provinces into three clusters based on economic similarity. The results reveal a persistent economic disparity between the western and eastern regions of Indonesia, with provinces such as DKI Jakarta and East Kalimantan grouped in the high GRDP cluster, while East Nusa Tenggara and Papua fall into the lowest cluster. Trendline and forecasting analyses provide projections of economic growth through 2030, indicating that some provinces demonstrate stable upward trends, whereas others show stagnation or declining patterns. Based on these findings, this research offers data-driven policy recommendations aimed at promoting more equitable and sustainable economic development across Indonesia.
Volume: 15
Issue: 3
Page: 1286-1296
Publish at: 2026-09-01

Intrusion detection in evolving internet of things environments using decentralized data systems

10.11591/ijaas.v15.i3.pp1252-1261
Zobayer Alam , Arnab Bishakh Sarker , Jariatun Islam , Sifat Rahman Ahona
Growing internet of things (IoT) deployments have widened the attack surface for cyber threats that static, signature-dependent intrusion detection systems (IDSs) struggle to counter, particularly against previously unseen attack variants. This research introduces a hybrid IDS framework built on a stacking ensemble of four heterogeneous base classifiers, namely random forest (RF), extreme gradient boosting (XGBoost), light gradient-boosting machine (LightGBM), and a shallow multi-layer perceptron (MLP), coupled with a PyTorch-based neural network meta-classifier. Recursive feature elimination (RFE) guided by a RF estimator selected the 15 most informative behavioral flow features, while a hybrid random sampling approach corrected severe class imbalance in the training data. Detection outputs are stored immutably through the interplanetary file system (IPFS) via the Pinata gateway, enabling decentralized, content-addressed logging of IDS alerts. Evaluated on the CIC-BCCC-NRC-TabularIoT-2024 benchmark, the model achieved a classification accuracy of 99.51%, precision of 99.71%, recall of 99.30%, and an F1-score of 99.51%, establishing that pairing meta-learning with decentralized log storage yields a robust, auditable IDS suited for dynamic IoT environments.
Volume: 15
Issue: 3
Page: 1252-1261
Publish at: 2026-09-01

Hybrid machine learning for predictive spare parts replacement in medical equipment

10.11591/ijaas.v15.i3.pp990-997
Fabian Halley Pata Alban Dattu , Syed Tarmizi Syed Shazali , Shirley Jonathan Tanjong , Abdul Rani Achmed Abdullah , Nurlaila Rosli
Stability of medical equipment is critical for uninterrupted healthcare services, yet hospital computerized maintenance management systems (CMMS) records are often incomplete, heterogeneous, and partially labeled for spare parts usage. In the analyzed CMMS dataset, spare-part-related work orders accounted for approximately 65.1% of maintenance cases, indicating that spare-part readiness is a major contributor to maintenance response time and potential equipment downtime. This study proposes a semi‑supervised hybrid random forest-neural network (RF-NN) model to predict whether a work order will require spare‑parts replacement and to support proactive inventory planning. Retrospective CMMS work‑order records from six hospitals (N = 295) were pre‑processed using leakage‑safe pipelines (categorical one‑hot encoding, text feature extraction from fault descriptions, and standardization of numeric fields). Semi‑supervised self‑training was used to exploit unlabeled or weakly labeled records through iterative pseudo‑labelling. Models were evaluated using stratified 10‑fold cross‑validation and reported as mean ± SD. The hybrid RF-NN achieved Accuracy = 0.986 ± 0.018, precision = 0.980 ± 0.026, recall = 1.000 ± 0.000, and F1‑score = 0.990 ± 0.013, outperforming RF, NN, and Markov baselines. These results demonstrate the practical advantage of combining semi‑supervised learning with hybrid modelling to reduce missed spare‑parts cases, minimize downtime risk, and strengthen CMMS‑ready procurement decision support.
Volume: 15
Issue: 3
Page: 990-997
Publish at: 2026-09-01

Wheat leaf disease classification using vision transformer based deep learning approach

10.11591/ijaas.v15.i3.pp954-963
P. Ashwini Ashwini , D. Mythili , P. Jyothi , K. Swathi , G. Rama Krishna , M. Sowmya
Wheat crops can be affected by numerous fungal diseases like leaf rust, stripe rust, and stem rust. These diseases can significantly reduce yield and grain quality and also result in highly economic damage. Detection of diseases at an early stage is the main and most complex task for farmers due to common morphological properties like color, shape, texture, and edges. Deep learning (DL) offers a powerful solution for detecting wheat crop diseases due to its ability to accurately identify diseases, even in complex field conditions, and facilitate early intervention. By analyzing images, DL models can learn complex patterns associated with various diseases, enabling faster and more reliable diagnoses than traditional methods. In this paper use DL models followed by a vision transformer model (ViTM) to extract multilevel features to focus more on the disease-infected area on the plant with enhanced accuracy. DL models extract hierarchical features, whereas transformer models are utilized to capture global dependencies and contextual relationships within the image. In the proposed research evaluated various DL models, InceptionV3, EfficientNet-B0, visual geometry group 16 (VGG-16), and ResNet-50 with visual transformers on the wheat disease detection dataset of 3679 images and achieved accuracy rates of 98.7%, 97.6%, 93.9%, and 97.5%, respectively. The experiment proved that InceptionV3 outperforms the other models.
Volume: 15
Issue: 3
Page: 954-963
Publish at: 2026-09-01

Multilevel local region sparse shape composition model for liver cancer classification

10.11591/ijaas.v15.i3.pp932-943
Balasubramanian Sakthisaravanan , Ramakrishnan Meenakshi , Thirugnanasambandam Akila , Saravanan Durga Devi , Subbiah Murugan
Machine learning and computer-assisted disease detection are two technological innovations that have significantly improved medical advancement, and recent research has demonstrated their effectiveness. Liver cancer is one of the important causes of cancer-related deaths internationally. Tumor detection in the liver and its classification is challenging due to poor accuracy and high processing requirements available in the existing techniques. In these, there is a loss of edge information and the quality of the images considered. To address these issues in the segmentation and classification of liver cancer, a novel feature extraction method and algorithm called the novel multilevel local region (MLR)-based sparse shape composition model (NMLR-SSC) were developed. This innovative technique identifies the existence of tumors on abdominal computed tomography (CT) images. The input images were obtained from the 3D-IRCADb-01 dataset. The algorithm showed an improvement of 0.22%. When compared to other classifiers. The proposed algorithm showed 100% specificity, sensitivity, and a 98% accuracy rate in detecting the liver tumor.
Volume: 15
Issue: 3
Page: 932-943
Publish at: 2026-09-01

Interpreting potato disease classification using explainable artificial intelligence

10.11591/ijaas.v15.i3.pp1123-1130
Rakesh Kumar Gumasta , Ajay Somkuwar
Timely and accurate crop diseases detection is most important for ensuring global food security. For detecting diseases in crops, many different machine learning (ML) models were proposed. These models work as a black-box, and without proper explanation of these models’ decisions, farmers may find it difficult to trust these systems. For this, many model explainability methods were also proposed. All these methods have been evaluated qualitatively, but their quantitative and cross-comparison are missing. This study addresses this gap by emphasizing quantitative validation of models’ explanations. This study investigates the interpretability and performance of two approaches for potato leaf disease classification: i) manual feature engineering with relief-based feature selection and artificial neural network (ANN) classification with local interpretable model-agnostic explanations (LIME) and ii) deep convolutional neural networks (CNNs) based on mobile network version 2 (MobileNetV2) with gradient-weighted class activation mapping (Grad-CAM). Quantitative assessment of explanation quality was performed using fidelity and robustness metrics. While LIME achieved a lower average fidelity drop 10.90% and higher robustness 84.27% compared to Grad-CAM 18.68% fidelity drops and 79.38% robustness, Grad-CAM provided clearer visual explanations. These findings suggest that explanation effectiveness is influenced by model design and data characteristics, highlighting the need for careful selection of interpretability techniques in precision agriculture applications.
Volume: 15
Issue: 3
Page: 1123-1130
Publish at: 2026-09-01

Quantification and traceability of mango by-products in the Hauts-Bassins and Cascades regions of Burkina Faso

10.11591/ijaas.v15.i3.pp1059-1071
Wendkouni Marguerite Bamogo , Hyacinthe Kante-Traore , Bakary Tarnagda , Boureima Kagambèga , Charles Parkouda , Aly Savadogo
This study provides a regional analysis of mango by-product generation and traceability in the Hauts-Bassins and Cascades regions of Burkina Faso. Through field surveys conducted in samples of 89 processing units and stakeholder mapping, the study quantifies the volume and flow of mango residues and identifies key stakeholders and bottlenecks in the post-harvest chain. Although traceability practices remain largely manual, the study identifies opportunities for digital integration and the added value of utilizing by-products in agro-industrial applications. A total of 52,691 tons of fresh mangoes were processed, primarily from seven varieties. Five types of by-products were identified, with peels, kernels, and sorting waste being the most prevalent. These by-products represented 49.66% of the total processed mango mass. Most by-products were discarded indiscriminately, with only minimal portions allocated to livestock feed (1%) and agriculture (2%). Physical analyses conducted in drying units and laboratories revealed that processing methods significantly increased by-product generation, resulting in pulp losses of up to 31.8%. These results provide a foundation for optimizing the utilization of mango by-products in Burkina Faso through technological innovations. The findings support strategic planning for sustainable resource management and lay the groundwork for the future deployment of traceability systems in tropical agricultural contexts.
Volume: 15
Issue: 3
Page: 1059-1071
Publish at: 2026-09-01

Hybrid lightweight encryption and searchable security framework for secure VANET image and text data sharing

10.11591/ijaas.v15.i3.pp1006-1014
Fairouz Sherali , Falah Hasan Sarhan
The increasing reliance on vehicular ad hoc networks (VANETs) for instantaneous traffic orchestration and data exchange highlights the need for robust, efficient, and privacy preserving security solutions. Classical encryption methods often fail to meet the low-latency and lightweight requirements of vehicular environments. In this study, a hybrid cryptographic framework was designed that integrates searchable encryption (SE) with the Spritz stream cipher, targeting secure keyword-based access to encrypted data. By leveraging bilinear pairing for identity-based key management, the system enables flexible authentication and secure updates. Both text and image data are supported, with minimal computational overhead. Experimental results validate the feasibility of the approach in dynamic, resource-limited vehicular networks.
Volume: 15
Issue: 3
Page: 1006-1014
Publish at: 2026-09-01

Novel bat algorithm for short-term peak load forecasting in Sulselrabar electricity system

10.11591/ijaas.v15.i3.pp1334-1346
Muhammad Rais , Rosihan Aminudin , Asnefi Asnefi , Andi Nur Putri , Irwan Syarif , Muhammad Ruswandi Djalal
This study addresses short-term load forecasting (STLF) in the South, Southeast, and West Sulawesi (Sulselrabar) power system, Indonesia, using interval type-2 fuzzy logic (IT2FL) optimized by the proposed novel bat algorithm (NBA). The NBA is employed to optimize the footprint of uncertainty (FOU) of the fuzzy membership functions for both antecedent (X and Y) and consequent (Z) variables. The forecasting model utilizes daily peak load data from the previous four days (d−4 to d−1) to predict the peak load of the forecast day (d). To evaluate the effectiveness of the proposed approach, NBA is benchmarked against particle swarm optimization (PSO), firefly algorithm (FA), and cuckoo search algorithm (CSA). The results demonstrate that the proposed IT2FL–NBA model provides the highest forecasting accuracy among the evaluated methods, achieving a mean absolute percentage error (MAPE) of 1.5650%. In comparison, IT2FL–PSO, IT2FL–FA, and IT2FL–CSA achieve MAPEs of 1.6353%, 1.6451%, and 1.6353%, respectively. For type-1 fuzzy logic (IT1FL), the NBA, PSO, FA, and CSA optimization methods produce MAPEs of 1.6668%, 1.6842%, 1.6805%, and 1.6768%, respectively. These findings demonstrate that optimizing the FOU of IT2FL using the proposed NBA significantly improves forecasting accuracy and provides a robust and reliable approach for STLF in power systems.
Volume: 15
Issue: 3
Page: 1334-1346
Publish at: 2026-09-01

Predicting perceived information overload from social media use: a machine learning approach

10.11591/ijaas.v15.i3.pp1147-1154
Mohamad Noorman Masrek , Endang Fitriyah Mannan , Zulfatun Sofiyani , Atiqa Nur Latifa Hanum
The rapid growth of social media has intensified users’ exposure to large volumes of information, increasing the risk of perceived information overload. While prior studies have primarily examined this phenomenon using explanatory models, limited attention has been given to its prediction. This study aims to predict perceived information overload from social media use using a machine learning approach. Data were collected through a survey of 349 university students in Malaysian higher education institutions. Social media use across multiple platforms was used as input features, while perceived information overload was treated as a binary target variable. Several machine learning algorithms were evaluated, including decision tree, random forest, k-nearest neighbor, support vector machine, neural network, naïve Bayes, and logistic regression. The results indicate that social media usage patterns contain predictive value for identifying information overload, with the support vector machine demonstrating the strongest discriminative performance. The study contributes to the literature by complementing existing explanatory research with a predictive perspective and highlights the potential of machine learning techniques for early identification of information overload in social media environments.
Volume: 15
Issue: 3
Page: 1147-1154
Publish at: 2026-09-01

Social media resonance on destination dispersion to predict tourist behavior and travel distance using geospatial analysis

10.11591/ijaas.v15.i3.pp883-893
Nindyo Cahyo Kresnanto , Wika Harisa Putri , Muhamad Willdan
This study investigates the influence of social media popularity on tourist destination preferences in Gunungkidul Regency, Yogyakarta, Indonesia, using an integrated big data and geographic information system (GIS) approach. Field surveys involving 504 respondents were combined with sentiment analysis of 30,432 cleaned tweets collected from X (formerly Twitter). Spatial analysis was applied to identify tourist origin patterns and destination attractiveness. The results show that neutral sentiment dominates online discussions, indicating generally positive but moderate tourist experiences. Destinations with higher popularity on social media, particularly Heha Sky View, Drini Beach, and Indrayanti Beach, attracted visitors from broader and more diverse origins. Spatial analysis also indicates stronger preferences for coastal and highland destinations. The integration of big data and GIS enhances understanding of tourism behavior and spatial destination patterns. Future studies are recommended to integrate multiple social media platforms and comparative spatial analyses to improve tourism planning and destination management.
Volume: 15
Issue: 3
Page: 883-893
Publish at: 2026-09-01

Smart internet of things driven vehicle safety and battery management system

10.11591/ijaas.v15.i3.pp1085-1097
Batchalakuri Jyothi , Bhavana Pabbuleti , Ravi Ponnala , Patil Mounika , Edara Shiva Prasad , Enukonda Soumya , Rakesh Teerdala , Srungaram Ravi Teja , Malligunta Kiran Kumar , Kambhampati Venkata Govardhan Rao , Chilakala Rami Reddy
The proposed system design, the essential results obtained, and its relevance to modern automotive applications. The updated version now begins by identifying the dual challenge of increasing road accidents and the performance degradation of electric vehicle (EV) batteries, emphasizing the lack of integrated solutions that address both safety and battery reliability in a unified manner. Then, this study concisely describes the system design, highlighting the use of an internet of things (IoT)-enabled architecture incorporating an ADXL345 accelerometer for collision detection, global positioning system (GPS)/global system for mobile communications (GSM) modules for emergency alerting, and voltage, current, and temperature sensors for real-time battery monitoring, all interconnected through Arduino and ESP8266-based cloud communication. The abstract now summarizes key experimental results, including the system’s demonstrated accuracy in impact recognition, reduced alert latency, and reliable battery-parameter tracking supported by effective cloud visualization. Finally, this study strengthened the automotive relevance by emphasizing how this integrated platform enhances emergency responsiveness, supports predictive maintenance, and offers practical value for EVs, commercial fleet operations, and broader intelligent transportation applications. The revised abstract now provides a balanced, complete, and technically informative overview aligned with academic and automotive industry expectations.
Volume: 15
Issue: 3
Page: 1085-1097
Publish at: 2026-09-01

Correlates and predictors of generative artificial intelligence adoption among Filipino college teachers

10.11591/ijaas.v15.i3.pp998-1005
Almer B. Gamboa , John Paul P. Miranda
Filipino college teachers in public higher education institutions (HEIs) are increasingly expected to integrate generative artificial intelligence (GenAI) tools into their practice. Evidence on what drives this adoption in Philippine public universities, however, remains scarce. This study used a quantitative, cross-sectional correlational design to examine the levels, relationships, and predictors of GenAI adoption among 301 teachers from two public HEIs in Pampanga, Philippines, guided by the unified theory of acceptance and use of technology (UTAUT). Teachers reported strong agreement across all UTAUT constructs, with effort expectancy receiving the highest rating and facilitating conditions the lowest. Performance expectancy (β = 0.406, p < 0.001) was the strongest predictor of behavioral intention, followed by social influence (β = 0.295, p < 0.001) and effort expectancy (β = 0.257, p < 0.001), which together explained 65% of the variance in intention. Facilitating conditions did not significantly predict behavioral intention but, together with behavioral intention (β = 0.629, p < 0.001), predicted actual use (β = 0.266, p < 0.001). Teachers are more likely to adopt GenAI when they see clear instructional benefits and when peers and administrators encourage its use; sustained practice depends on whether institutions can provide the necessary infrastructure and training to support it. Public HEIs are urged to invest in AI literacy programs, strengthen peer and administrative endorsement, and address gaps in information and communication technology (ICT) infrastructure to support responsible and lasting GenAI integration.
Volume: 15
Issue: 3
Page: 998-1005
Publish at: 2026-09-01

Optimization-enabled machine learning with feature selection for phishing detection system

10.11591/ijaas.v15.i3.pp1131-1146
Lukman Adebayo Ogundele , Julius Temitayo Adepoju , Femi Emmanuel Ayo , Idayat Abike Akano , Abidemi Emmanuel Adeniyi , Halleluyah Oluwatobi Aworinde , Oluwasegun Julius Aroba , Gbohunmi Ajibesin
The rapid evolution of phishing methods has long been a threat to cybersecurity that demands adaptive and intelligent detection techniques. This current work envisions an improved phishing detection system that involves the integration of machine learning (ML) with feature selection for improving the performance of real-time classification. A comprehensive dataset was optimized and preprocessed through Chi-square (CHI) feature selection to reduce dimensionality while preserving the most discriminative features. Random forest (RF) algorithm, selected for its robustness, interpretability, and high computational efficiency for binary classification, was employed and achieved an accuracy of 96.25%, effectively identifying sophisticated phishing attacks. The architecture is designed to operate on large datasets with minimal computational overhead but with high precision and scalability. Experimental results also indicate the dependability of the system with an area under the curve (AUC)-receiver operating characteristic (ROC) score of 1.0, indicating perfect separation of phishing from normal cases. In addition, an adaptive classification model was used to accommodate unknown data sets with a 100% accuracy score. The results above identify the potential of adaptive, feature-optimized ML systems in the ability to handle dynamic phishing attacks and enable proactive cybersecurity defense.
Volume: 15
Issue: 3
Page: 1131-1146
Publish at: 2026-09-01
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