cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
International Journal of Advances in Applied Sciences
Published by Institute of Advanced Engineering and Science
ISSN : 22528814     EISSN : 27222594     DOI : http://doi.org/10.11591/ijaas
Core Subject : Science, Education, Social,
Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics
International Journal of Advances in Applied Sciences (IJAAS) is a peer-reviewed and open access journal dedicated to publish significant research findings in the field of applied and theoretical sciences. The journal is designed to serve researchers, developers, professionals, graduate students and others interested in state-of-the art research activities in applied science areas, which cover topics including: chemistry, physics, materials, nanoscience and nanotechnology, mathematics, statistics, geology and earth sciences.
Arjuna Subject : -
Articles 797 Documents
 76   77   78   79   80 
Space vector control for multilevel static synchronous compensator in wind energy systems Gundala Muni Reddy; Ammapalli Hema Sekhar; Selika Narasimha Rao; Kuruna Divakar; Kola Murali Kumar; Nathella Munirathanm Giri Kumar; Gunti Kishor Babu; Vayyala Jyothsna Priya
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp1270-1285

Abstract

To meet the higher demand for energy, fossil fuels have been the leading energy source. These fuels produce greenhouse gases (GHG) and these gases are the reason for climate change, and the fossil fuel resources are also running out quickly. Because of this, lawmakers need to switch to clean and renewable energy sources, including solar, geothermal, wind, tidal, and biomass. The wind energy industry has developed a lot because of new technologies, which has led to more wind turbines (WTs) being connected to the grid. Integrating a lot of WTs into the grid has caused challenges like power quality (PQ), grid stability, and reactive power correction. This study utilizes a multilevel inverter-based static synchronous compensator (STATCOM) with a space vector hysteresis current controller (SVHCC) to mitigate the impacts of WTs. The proposed control strategy improves PQ by reducing total harmonic distortion (THD) and maintaining unity power factor (UPF) even in the presence of nonlinear load and induction generator (IG). The desired results are obtained with a lower switching frequency, thereby improving efficiency and reducing stress on power electronic devices. The proposed SVHCC-based STATCOM is modelled and evaluated using MATLAB/Simulink, and its performance is compared with both uncompensated system and a simple hysteresis current controller (SHCC)-based STATCOM.
Social media resonance on destination dispersion to predict tourist behavior and travel distance using geospatial analysis Nindyo Cahyo Kresnanto; Wika Harisa Putri; Muhamad Willdan
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp883-893

Abstract

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.
Optimization-enabled machine learning with feature selection for phishing detection system Lukman Adebayo Ogundele; Julius Temitayo Adepoju; Femi Emmanuel Ayo; Idayat Abike Akano; Abidemi Emmanuel Adeniyi; Halleluyah Oluwatobi Aworinde; Oluwasegun Julius Aroba; Gbohunmi Ajibesin
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp1131-1146

Abstract

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.
Predicting perceived information overload from social media use: a machine learning approach Mohamad Noorman Masrek; Endang Fitriyah Mannan; Zulfatun Sofiyani; Atiqa Nur Latifa Hanum
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp1147-1154

Abstract

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.
Hybrid lightweight encryption and searchable security framework for secure VANET image and text data sharing Fairouz Sherali; Falah Hasan Sarhan
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp1006-1014

Abstract

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.
Novel bat algorithm for short-term peak load forecasting in Sulselrabar electricity system Muhammad Rais; Rosihan Aminudin; Asnefi Asnefi; Andi Nur Putri; Irwan Syarif; Muhammad Ruswandi Djalal
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp1334-1346

Abstract

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.
Correlates and predictors of generative artificial intelligence adoption among Filipino college teachers Almer B. Gamboa; John Paul P. Miranda
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp998-1005

Abstract

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.
Quantification and traceability of mango by-products in the Hauts-Bassins and Cascades regions of Burkina Faso Wendkouni Marguerite Bamogo; Hyacinthe Kante-Traore; Bakary Tarnagda; Boureima Kagambèga; Charles Parkouda; Aly Savadogo
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp1059-1071

Abstract

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.
Wheat leaf disease classification using vision transformer based deep learning approach P. Ashwini; D. Mythili; P. Jyothi; K. Swathi; G. Rama Krishna; M. Sowmya
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp954-963

Abstract

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.
Interpreting potato disease classification using explainable artificial intelligence Rakesh Kumar Gumasta; Ajay Somkuwar
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp1123-1130

Abstract

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.

Page 78 of 80 | Total Record : 797

 76   77   78   79   80 

Filter by Year

2012 2026


Reset
Filter By Issues
All Issue Vol 15, No 3: September 2026 Vol 15, No 2: June 2026 Vol 15, No 1: March 2026 Vol 14, No 4: December 2025 Vol 14, No 3: September 2025 Vol 14, No 2: June 2025 Vol 14, No 1: March 2025 Vol 13, No 4: December 2024 Vol 13, No 3: September 2024 Vol 13, No 2: June 2024 Vol 13, No 1: March 2024 Vol 12, No 4: December 2023 Vol 12, No 3: September 2023 Vol 12, No 2: June 2023 Vol 12, No 1: March 2023 Vol 11, No 4: December 2022 Vol 11, No 3: September 2022 Vol 11, No 2: June 2022 Vol 11, No 1: March 2022 Vol 10, No 4: December 2021 Vol 10, No 3: September 2021 Vol 10, No 2: June 2021 Vol 10, No 1: March 2021 Vol 9, No 4: December 2020 Vol 9, No 3: September 2020 Vol 9, No 2: June 2020 Vol 9, No 1: March 2020 Vol 8, No 4: December 2019 Vol 8, No 3: September 2019 Vol 8, No 2: June 2019 Vol 8, No 1: March 2019 Vol 7, No 4: December 2018 Vol 7, No 3: September 2018 Vol 7, No 2: June 2018 Vol 7, No 1: March 2018 Vol 6, No 4: December 2017 Vol 6, No 3: September 2017 Vol 6, No 2: June 2017 Vol 6, No 1: March 2017 Vol 5, No 4: December 2016 Vol 5, No 3: September 2016 Vol 5, No 2: June 2016 Vol 5, No 1: March 2016 Vol 4, No 4: December 2015 Vol 4, No 3: September 2015 Vol 4, No 2: June 2015 Vol 4, No 1: March 2015 Vol 3, No 4: December 2014 Vol 3, No 3: September 2014 Vol 3, No 2: June 2014 Vol 3, No 1: March 2014 Vol 2, No 4: December 2013 Vol 2, No 3: September 2013 Vol 2, No 2: June 2013 Vol 2, No 1: March 2013 Vol 1, No 4: December 2012 Vol 1, No 3: September 2012 Vol 1, No 2: June 2012 Vol 1, No 1: March 2012 More Issue