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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.
Arjuna Subject : -
Articles 758 Documents
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Anomaly detection in containerized virtual environments using a process mining approach Richard Angkawinata; I Made Murwantara
International Journal of Advances in Applied Sciences Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i2.pp656-663

Abstract

The growing adoption of container-based virtual environments in cloud computing introduces new challenges in anomaly detection due to the systems’ dynamic nature. Traditional monitoring approaches often fail to capture inefficiencies or security risks effectively. This study proposes a process mining approach to identify anomalies by analyzing event logs from containerized systems. Event data from the AIOps challenge 2020 was converted into extensible event stream (XES) format and processed using the inductive visual miner in the ProM tool to generate Petri Net models, accurately visualizing container activity flows. A conformance checking analysis was conducted to evaluate the alignment between modeled and actual behavior. Results demonstrated a high fitness score, confirming the model’s precision in reflecting true operational processes and its ability to reveal minor deviations indicative of potential anomalies. These findings highlight process mining as a promising method to enhance security, transparency, and performance monitoring in virtual environments. The research also recommends integrating process mining with real-time monitoring systems for proactive anomaly detection, thereby improving responsiveness and resilience in cloud-based infrastructures.
SmartCloud QR: a next-gen online attendance system leveraging QR code and cloud technology Kriangsak Chanthinok; Palan Jantarajaturapath; Konkanok Donsophon
International Journal of Advances in Applied Sciences Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i2.pp594-610

Abstract

This study presents SmartCloud quick response (QR), a cloud-based attendance monitoring system designed to enhance efficiency, accuracy, and fraud prevention in educational institutions. Traditional paper-based attendance methods are time-consuming, error-prone, and vulnerable to proxy attendance. To address these limitations, the system was developed using a software-as-a-service architecture that integrates QR code technology with cloud computing, enabling scalable deployment without requiring dedicated institutional hardware infrastructure. The research was conducted in two phases: phase 1 involved user needs analysis to identify operational challenges in attendance management and phase 2 evaluated system effectiveness through large-scale implementation involving 27,070 participants. Statistical analyses, including independent samples t-tests and one-way analysis of variance (ANOVA), were performed to examine satisfaction differences across gender and user roles. The results demonstrated a high overall satisfaction rate (92.84%), with only minimal and practically negligible differences observed across demographic groups. The findings empirically support the technology acceptance model (TAM), indicating that perceived usefulness and ease of use are reinforced by fraud prevention mechanisms and infrastructure-light deployment. This study contributes to the applied educational informatics literature by introducing a scalable software-as-a-service attendance framework that enhances fraud prevention, reduces institutional hardware investment, and operationalizes privacy-by-design principles in large-scale real-world implementation.
Mapping artificial intelligence applications in electronic medical records research Bima Ananta Putra; Merita Arini
International Journal of Advances in Applied Sciences Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i2.pp646-655

Abstract

The integration of electronic medical records (EMR) and artificial intelligence (AI) in healthcare improves data accessibility, security, diagnostic accuracy, personalized care, and overall system performance. Despite increasing interest, a comprehensive understanding of the field’s development, key contributions, and dominant research themes remains limited. This study presents a bibliometric analysis of 681 articles selected from 1893 initial records retrieved from the Scopus database (2015–2025) using the keywords “electronic medical record” AND “artificial intelligence.” Data were analyzed using Microsoft Excel for trend analysis and VOSviewer for keyword co-occurrence and thematic clustering. Results show steady publication growth, mainly from developed countries and health informatics institutions. Four main research themes emerged: i) AI adoption in healthcare systems, ii) patient characteristics and clinical assessment, iii) predictive models and machine learning (ML) algorithms, and iv) deep learning (DL) and diagnostic accuracy. Nevertheless, research gaps persist in areas such as patient safety, data privacy, ethical issues, primary care implementation, healthcare workforce roles, and specific algorithmic approaches. Trust in AI systems also requires deeper investigation.
Using machine learning to understand the root causes of type 2 diabetes in Saudi Arabia Mohammad Saeed Al Ghamdi; Alaa Omar Khadidos; Adel Omar Khadidos
International Journal of Advances in Applied Sciences Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i2.pp790-803

Abstract

This study examines the elevated prevalence of type 2 diabetes mellitus (T2DM) in Saudi Arabia by integrating a large-scale, regionally specific dataset from the Saudi Ministry of Health (100,000 patient records). The primary contribution is this Saudi-focused data integration—addressing a critical gap in prior studies that rely on global datasets (e.g., PIMA, UK Biobank)—and a comparative evaluation of traditional machine learning (ML) models (random forest (RF), adaptive boosting (AdaBoost)) and few shot large language models (LLMs) for early risk detection. Key features include age, body mass index (BMI), HbA1c, blood glucose, hypertension, and smoking history. Class imbalance (9% diabetic) was resolved using synthetic minority over-sampling technique with edited nearest neighbors (SMOTEENN). Ensemble ML models achieved up to 97% accuracy, while few-shot LLM reached 98% across accuracy, precision, recall, and F1-score (area under the curve (AUC) 0.99). Feature importance confirmed that HbA1c and glucose were the top predictors. This regionally tailored, high performing framework enables early detection of T2DM and culturally sensitive interventions in high-prevalence settings such as Saudi Arabia.
Intelligent hyperparameter optimization of multilayer perceptron for water quality classification Edi Ismanto; Rahmad Gunawan; Harun Mukhtar; Rahmad Al Rian; Vitriani Vitriani; Hadhrami Ab Ghani
International Journal of Advances in Applied Sciences Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i2.pp490-500

Abstract

Assessment of water quality is essential for monitoring the environment and sustainable resource management. However, complex physicochemical parameters and class imbalance pose challenges for reliable predictive modeling. Although the multilayer perceptron (MLP) is widely applied for classification tasks, its performance depends strongly on effective hyperparameter optimization. This study evaluates three metaheuristic approaches—Bayesian optimization (BO), genetic algorithm (GA), and particle swarm optimization (PSO)—to enhance the performance of MLP on an imbalanced water potability dataset, which is addressed using synthetic minority over-sampling technique (SMOTE). Experimental results obtained using stratified 10-fold cross-validation demonstrate consistent improvements over the baseline MLP (accuracy of 0.7899±0.013). BO and GA improve predictive performance, while PSO achieves the best overall results with an accuracy of 0.9324±0.006, F1-score of 0.9338±0.006, and receiver operating characteristic–area under the curve (ROC–AUC) of 0.9652±0.005. The findings indicate that PSO provides superior convergence stability and generalization by effectively balancing exploration and exploitation during hyperparameter search. The integration of class balancing and swarm-based optimization substantially enhances classification robustness and discriminative capability, supporting its applicability for intelligent water quality monitoring systems.
Descriptive data mining for multi-shelf product allocation in traditional retail Singgih Saptadi; Wiwik Budiawan; Ary Arvianto; Purnawan Adi Wicaksono; Chaterine Alvina Prima Hapsari; Dhimas Wachid Nur Saputra
International Journal of Advances in Applied Sciences Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i2.pp830-843

Abstract

The business expansion of minimarkets in small cities is one of the serious threats to the sustainability of traditional retail businesses or small independent retailers. Many traditional retailers eventually closed due to their inability to maintain competitiveness, as customers increasingly prefer shopping at modern retail outlets. A well-organized store layout can improve the shopping experience of customers, which has an impact on customer satisfaction and retail competitive advantage. Currently, shelf space allocation in traditional retail is still inattentive, making the placement of products on the shelf random and erratic. Based on these problems, this research aimed to design multi-shelf product allocation according to customer shopping patterns by combining clustering algorithms and market basket analysis (MBA). Clustering aims to divide data points into two different clusters, namely dominant product and less favored product, while MBA aims to identify the customer purchase pattern and preferences. The three MBA scenarios produced four, twelve, and forty rules. The research successfully designed two layouts by utilizing a combination of clustering and MBA algorithms. The utilization of data mining allows traditional retailers to extract information from the database to be arranged into a layout design that fits the shopping patterns and customer preferences.
Real-time human activity recognition using deep learning techniques for the next-generation healthcare system Subrata Kumer Paul; Rakhi Rani Paul; Md. Ekramul Hamid; Md. Rafiqul Islam (Rafiq)
International Journal of Advances in Applied Sciences Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i2.pp437-450

Abstract

In today's world, healthcare systems are being built with human activity recognition (HAR) to help the elderly, disabled, and children's activities by constantly observing their behavior. However, HAR using computer vision and traditional machine learning techniques is not an efficient use of healthcare system resources because of potential and accuracy issues. This study aims to examine the use of deep learning techniques in real-time HAR. This proposal is a hybrid method that utilizes the EfficientNetB0 architecture and a convolutional extension of a long short-term memory network (EfficientNetB0ConvLSTM), to achieve human-like intelligence. The EfficientNetB0 is utilized to extract image features, and convolutional long short-term memory (ConvLSTM) is utilized to categorize six human actions to recognize human activities. This approach leverages the strengths of convolutional neural networks (CNNs) in extracting spatial features from video frames and LSTMs in capturing temporal dependencies within activity sequences. Firstly, an extensive investigation is conducted on existing literature studies to select a suitable dataset. Next, the proposed method was evaluated on the challenging HMDB51 video datasets and finally achieved an accuracy of 89.22%, which is significantly higher than other methods on this dataset. This outcome shows the potential of EfficientNetB0ConvLSTM for real-time HAR applications like healthcare.
Food security-based strategies for Subak Kedisan organic rice sustainability Ni Made Classia Sukendar; Widhianthini Widhianthini; Ni Putu Rahayu Sastra Dewi; Trisha Susana Andrea
International Journal of Advances in Applied Sciences Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i2.pp749-759

Abstract

The Petani Kedisan Mandiri as farmers group in Subak Kedisan has implemented an organic rice farming system and obtained organic certification from Seloliman Organic Certification Agency (LeSOS). However, the group faces challenges in product marketing and limited promotion. This study aims to identify internal and external factors influencing organic rice marketing, formulate alternative marketing strategies, and determine priority strategies for the group. The research applied strengths, weaknesses, opportunities, and threats (SWOT) and quantitative strategic planning matrix (QSPM) analyses. The results identified six internal strengths, with high-quality organic rice as the main strength, and eight weaknesses, with reliance on word-of-mouth marketing as the primary limitation. Six external opportunities were found, particularly the increasing market demand for organic rice, while five major threats were identified, including market segmentation limited to upper-middle-class consumers. Based on the analysis, twelve alternative strategies and one priority strategy were formulated. The main strategy is to enhance digital promotion capacity through training in social media and e-commerce utilization. Recommendations include improving digital literacy, strengthening collaboration with external stakeholders, developing digital infrastructure, leveraging healthy consumption trends, and implementing regular monitoring and evaluation.
Region of interest (ROI) compression for adrenal tumor images: a hybrid approach Faisel G. Mohammed; Sajaa G. Mohammed; Asmaa Abdul-Razzaq Al-Qaisi; Raghda A. Ali
International Journal of Advances in Applied Sciences Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i2.pp844-853

Abstract

Developments in artificial intelligence have made it considerably easier for specialists to identify and diagnose diseases by applying computer-aided diagnosis (CAD) systems to medical images. This study introduces a novel hybrid compression technique that combines lossless compression of the region of interest (ROI) with lossy compression of the surrounding areas. This approach balances compression-ratio performance and diagnostic image quality by leveraging refined ROI extraction using morphological operations. This study proposes a hybrid ROI-aware medical image compression method designed to enhance the efficiency of adrenal tumor computed tomography (CT) image transmission and storage without compromising diagnostic accuracy. While surrounding regions are compressed using lossy techniques like scalar quantization and Huffman coding, tumor regions found by a reliable automated segmentation pipeline are compressed lossless using a Lempel–Ziv–Welch (LZW) algorithm. While maintaining 95% tumor detectability, an experimental evaluation of 95 annotated CT images produced an average compression ratio of 48.00%, outperforming traditional Huffman compression (20.79%). Peak signal-to noise ratio (PSNR), structural similarity index measure (SSIM), and a new ROI fidelity score were used to measure image quality. This approach is compatible with digital imaging and communications in medicine (DICOM) standards, supports bandwidth-efficient telemedicine, picture archiving and communication system (PACS) optimization, and may be incorporated into artificial intelligence-assisted diagnostic workflows. Future strategies should incorporate multimodal fusion, compression, and adaptive ROI tracking to further enhance clinical usability.
Efficacy of Toona sureni Merr. leaf extract against Aedes spp. and Culex spp. by electric diffusion Sulasmi Sulasmi; Budirman Budirman; Haderiah Haderiah; La Taha; Nurhaidah Nurhaidah
International Journal of Advances in Applied Sciences Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i2.pp462-469

Abstract

Aedes spp. and Culex spp. mosquitoes are major vectors of dengue fever and filariasis. The extensive use of chemical insecticides has contributed to resistance and environmental concerns, underscoring need for eco-friendly alternatives. Suren leaf extract (Toona sureni Merr.) contains bioactive compounds with potential as a botanical insecticide. This study evaluates suren leaf extract delivered via an electric diffusion device for indoor adult mosquito control at concentrations of 45%, 55%, and 65% against Aedes spp. and Culex spp. A post-test-only control group design was applied with three replications, and outcomes were recorded over a 60 minutes exposure period. Mortality increased with both concentration and exposure time. At 60 minutes, mortality increased from 80% to 100% in Aedes spp. and from 64% to 96% in Culex spp. when the concentration increased from 45% to 65%. Two-way analysis of variance (ANOVA) confirmed significant effects of concentration and exposure time (p <0.001). These findings indicate that electric diffusion delivery of suren leaf extract is effective and supports its potential use for indoor vector control in endemic areas.

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