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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.
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Articles 45 Documents
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Search results for , issue "Vol 14, No 4: December 2025" : 45 Documents clear
Design and implementation of an internet of things-based automatic waste sorting system Taufik, Akhmad; Paisal, Paisal; Djalal, Muhammad Ruswandi; Dillah, Zahran Atha; Ismail, Haryono
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1155-1165

Abstract

This paper presents the design and development of an internet of things (IoT)-based automatic waste sorting system that classifies waste into four categories: organic, non-organic, metal, and others. The system integrates an Arduino Mega for control, multiple proximity sensors (inductive, capacitive, and infrared), and ultrasonic sensors for level detection, and a NodeMCU ESP8266 for real-time monitoring via the Blynk platform. A total of 100 tests (25 per bin) were conducted. Classification success rates were 92% (metal), 80% (inorganic), 84% (organic), and 100% (others), resulting in an overall accuracy of 89%. The main contribution is a combined automatic sorting and IoT monitoring framework suitable for campus-scale deployment.
Harvesting insights: exploring machine learning for crop selection and predictive farming Deshmukh, Tanvi; Rajawat, Anand Singh; Potgantwar, Amol
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp999-1009

Abstract

Modern agriculture has undergone a significant evolution, adopting advanced techniques to streamline crop management processes. One such advancement is the integration of machine learning (ML) technology, which shows great promise in optimizing crop selection and enhancing economic returns. Key determinants of crop productivity, including water availability, soil quality, weather conditions, and timely resource allocation, play pivotal roles in the farming ecosystem. Harnessing these factors, ML algorithms facilitate the identification of optimal crop choices and provide continuous monitoring of cultivation processes to anticipate evolving crop needs. This paper investigates various ML techniques employed for crop selection and evaluates their effectiveness in agricultural settings. Through a comparative analysis, we highlight the advantages of these techniques and provide insights into their potential impact on current farming management practices. By leveraging ML for predictive farming, stakeholders can make informed decisions to maximize yields, minimize resource wastage, and promote sustainable agricultural practices. This study contributes to the ongoing discourse on the integration of technology in agriculture and underscores the transformative potential of ML in shaping the future of crop management. We investigate recent papers from the years 2020 to 2024.
Computer simulation and software engineering in optical analysis of phosphor-converted white light-emitting diodes utilizing barium sulfate Trang, Le Thi; Loan, Nguyen Thi Phuong; Cong, Pham Hong; Anh, Nguyen Doan Quoc; Lee, Hsiao-Yi
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1384-1392

Abstract

Achieving uniform nanoparticle dispersion in electrospun polymer nanofibers remains a critical challenge, as conventional electrospinning often leads to particle agglomeration and nozzle clogging, reducing fiber uniformity and functional efficiency. This study explicitly addresses this problem by developing poly (vinyl alcohol) (PVA)/BaSO4 composite nanofibers through both conventional and ultrasonic-assisted electrospinning. Scanning electron microscopy (SEM) revealed that ultrasonication effectively disrupted nanoparticle agglomerates, yielding smoother and more uniform fiber morphologies. X-ray diffraction (XRD) analysis further confirmed that ultrasonic processing reduced the crystalline intensity of PVA and BaSO4, indicating enhanced polymer–filler interaction and finer BaSO4 distribution. Quantitatively, the agglomeration slope decreased from 0.039 (conventional) to 0.006, and the mean crystallite size was reduced from approximately 470 to 300 nm. These results are consistent with recent advances showing that ultrasonic electrospinning improves nanoparticle dispersion and stability in polymer matrices, thereby enhancing optical and mechanical properties. Ultimately, this work demonstrates that ultrasonic-assisted electrospinning provides a robust and scalable strategy to fabricate lightweight, flexible, and multifunctional PVA-based radiation shielding materials with superior nanoparticle dispersion and structural homogeneity.
Fit to-organization amplifies unethical pro-organizational behavior Umama, Hany Azza; Setyawati, Sri Murni; Wulandari, Siti Zulaikha
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1050-1060

Abstract

The study of unethical pro-organizational behavior (UPB) has seen a sharp rise in recent years, with research results explaining that a potential cause of UPB is organizational identification (OI). However, there are inconsistencies in the findings regarding the effect of OI on UPB in the workplace. This study seeks to test the direct effect of OI on UPB, and to explore the mediating role of fit to-organization (Fit-O). To evaluate data collected from employees of micro and small enterprises, this study utilized structural equation modeling-partial least squares (SEM-PLS) analysis, along with the variance accounted for (VAF) method to test the mediation between OI and UPB. The findings confirm that the fit or compatibility of an individual with the organization can strengthen the effect of OI on UPB. The intervening role of Fit-O in the OI-UPB relationship is a crucial theoretical contribution. This research also implies that organizations must balance increasing OI with strong ethical standards to mitigate UPB.
Stacking architecture-endpoint detection: a hybrid multi layered architecture for endpoint threat detection Wahid, Abd Rahman; Anggreani, Desi; Hayat, Muhyiddin A. M.; Abd Rahman, Aedah; Faisal, Muhammad
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1263-1280

Abstract

Modern endpoint threat detection systems face persistent challenges in balancing detection accuracy, resilience against zero-day attacks, and the interpretability of artificial intelligence (AI) models. Although deep learning (DL) approaches often achieve high accuracy on benchmark datasets, they remain vulnerable to adversarial perturbations and operate as opaque “black boxes,” thereby reducing trust and limiting practical adoption in critical infrastructures. This research introduces stacking architecture-endpoint detection (STACK-ED), a hybrid multi-layered architecture for endpoint threat detection. STACK-ED integrates three complementary paradigms: supervised learning for known attack patterns, self-supervised Fgraph-based learning for structural relationships, and unsupervised anomaly detection for emerging or unknown threats. The outputs are consolidated by a meta learner, followed by a post-hoc correction (PHC) mechanism to minimize false negatives. The framework was evaluated on a combined benchmark dataset (CSE-CIC-IDS2018 and UNSW-NB15, hereafter referred to as HIDS-Set). Experimental results demonstrate state-of-the-art performance, achieving an F2-score of 98.89% after hybrid integration and active learning, with the primary optimization objective being the reduction of undetected attacks. Furthermore, the Shapley additive explanations (SHAP) method enhances interpretability by revealing feature contributions, while the PHC successfully recovered 62.64% of missed zero-day candidates. The findings position STACK-ED not only as a highly accurate detection model but also as an adaptive, resilient, and transparent framework, offering practical implications for enterprise-grade endpoint defense and future zero-trust cybersecurity systems.
Quantum-inspired magnetic resonance imaging sequence optimization for detecting neurological diseases Savan Kumar, Kotichintala Venkata Narasimha; Kumar, Nitin
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1208-1216

Abstract

According to a research study by the National Institutes of Health, India, a magnetic resonance imaging (MRI) holds 89% diagnostic accuracy for acute stroke, while a computed tomography (CT) holds only 54%. Means there is still 11% area of improvement for accuracy measures required and there is 84% specific in identifying nerve enlargement. The possible solution is to use quantum computing; this is new era of technology in advanced design and implementation for computing techniques as compared with that of classical computers. With the goal of improving patient care, this is the area-of research using quantum technology to solve the neurological disorders. MRI and Microsoft’s quantum-inspired algorithms to enhance approach to detecting neurological disorders. To improve accuracy of MRI results in less time, an approach called magnetic resonance fingerprinting (MRF) was explored. This paper mainly focused on optimizing the sequence using Microsoft azure simulator. By generating an optimized pulse sequence and map to the accurate predefined patterns, able to create a solution that improves the diagnostic capability of MRI. Conventional computers will take long time to predict, but accuracy may alter. The proposed quantum-inspired optimization improved MRI diagnostic accuracy up to 92%, with faster sequence optimization compared to classical methods. This simulation-based proof of concept demonstrates potential for enhanced neurological disease detection while acknowledging current limitations such as simulator dependency and limited datasets.
Implementation of XGBoost for diabetes mellitus risk prediction based on health history Riansyah, Andi; Ghufron, Ghufron; Fitriyah, Lailatul; Suyanto, Suyanto
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1028-1039

Abstract

Diabetes mellitus (DM) is a chronic disease with a growing global burden and specific challenges for early management, particularly in regions with limited access to healthcare. This study develops a web-based system to classify diabetes risk from medical history using extreme gradient boosting (XGBoost), an ensemble model of decision trees. The dataset comprised 520 respondents (320 DM, 200 non-DM) and underwent labeling, standardization, and an 80:20 train–test split, followed by hyperparameter selection via grid search and 5-fold cross-validation (CV). On the test set, the model achieved an accuracy of 0.9888, precision of 1.0000, recall of 0.9718, and an F1-score of 0.9857; discriminative performance was also strong with an area under the receiver operating characteristic curve (AUC ROC) of 0.839. These findings confirm that XGBoost effectively handles complex or imbalanced medical data while providing probabilistic outputs that are clinically meaningful. Deployed as a web application, the system can support early screening, triage, and clinical decision-making, thereby expediting referrals and personalizing interventions in primary care and hospital settings, especially in resource-constrained environments. This work lays the groundwork for further development, including the integration of explainable artificial intelligence (XAI) techniques to enhance clinical transparency.
Redesigning retail spaces based on customer habits and halal standards using market basket analysis Yanti, Roaida; Qurtubi, Qurtubi; Setiawan, Danang; Maradjabessy, Prita Nurkhalisa; Faisol, Nasruddin
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1089-1098

Abstract

The halal retailing standard was first introduced by the Department of Malaysian Standards in 2010, known as MS 2400-3:2010. This standard was developed to protect Muslim consumers with the assurance of halal products. However, this management system needs to be more prescriptive on how the retail layout should be organized. In addition, this management also overrides the consideration of customer purchase behaviors or preferences. This research aims to design the layout of retail outlets by considering customer buying behavior and halal retailing standards. This study used the association rule-market basket analysis (AR-MBA) to determine the pattern of customer shopping behavior. One Islamic retail in Indonesia was used as a case study, where one-month sales transaction data was analyzed using AR-MBA. In addition, the activity relationship chart (ARC) was used to qualitatively analyze the placement of a product department by considering halal retailing standards. The results of AR-MBA obtained 21 associations among product departments, which were then used as a basis for proposed layouts while still considering the product characteristics and halal retailing standards. This research output provided a proposed product layout for retail outlets by considering quantitative factors (AR-MBA output) and qualitative factors (MS standard).
Exploring the need for a kickstarter toolkit for special education educators to teach authoring and illustrating Arumugam, Shyielathy; Mustafa, Mazlina Che; Abdullah, Norazilawati; Shamsudin, lylia Dayana; Mohd Jamil, Mohd Ridhuan; Dzainudin, Masayu
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1375-1383

Abstract

This study examines the need for developing a Kickstarter toolkit designed to support special education teachers in teaching entrepreneurship through the creation of children’s storybooks. Entrepreneurship education is increasingly recognized as a valuable addition to special education, promoting skills such as creativity, problem-solving, and independence. A survey conducted among 170 special education teachers from the Kuala Kangsar district, selected through judgmental sampling, revealed strong support for introducing entrepreneurship education into special education curricula (M=3.69, SD=0.71). Teachers also emphasized the importance of employing pedagogical approaches to effectively teach entrepreneurship concepts (M=3.89, SD=0.69). Moreover, the findings indicate an urgent need for a dedicated toolkit to facilitate such activities, with educators expressing high levels of agreement regarding its necessity (M=3.99, SD=0.73). These results underscore the positive perception of special education teachers toward entrepreneurship education and the crucial role a specialized toolkit could play in equipping educators with the resources needed to foster entrepreneurial competencies in students with special needs.
Convolutional neural network model for fingerprint-based gender classification using original and degraded images Pradini, Risqy Siwi; Kusuma, Wahyu Teja; Budi, Agung Setia
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1350-1358

Abstract

Fingerprint-based gender classification is a crucial component of soft biometrics, providing valuable additional information to narrow the search space in forensic investigations and large-scale identification systems. Although deep learning models, particularly convolutional neural networks (CNNs), have demonstrated significant potential, performance validation is typically performed on high-quality fingerprint images. This creates a gap between laboratory results and real-world applications, where fingerprint evidence is often found in a degraded state, such as smudged, distorted, or partially damaged. This study attempts to bridge this gap by proposing a more realistic training approach. We design a lightweight and computationally efficient CNN and train it on a comprehensive combined dataset. The main contribution of this study lies in the data training strategy, which explicitly combines real and synthetically modified fingerprint images from the Sokoto coventry fingerprint (SOCOFing) dataset into a single, unified training set. Experimental results show that the proposed model achieves very high classification accuracy (97.39%) on a test set that also includes a combination of original and degraded images. This finding not only confirms the effectiveness of diverse data-based training to produce more robust models but also establishes a new benchmark for fingerprint based gender classification research under conditions more representative of practical scenarios.

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