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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 31 Documents
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Search results for , issue "Vol 14, No 2: June 2025" : 31 Documents clear
Road pavement deformation using remote sensing technique Patel, Kishan; Gujar, Rajesh
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp345-351

Abstract

The road surface reflects the status of the city’s infrastructure. Road safety and driving comfort can be affected by the rough surface. To minimize road hazards, pavement conditions must be periodically inspected for damaged surfaces. A quick and efficient data collection can be provided by the radar images. For a large spatial coverage, radar image provides a non-destructive data collection technique for analyzing road conditions and classifying distress. The surface distress can be correlated by analyzing the images collected from high-resolution cameras and satellites. This article outlines the applicability of synthetic aperture radar (SAR) and interferometric synthetic aperture radar (InSAR) based images to manage and monitor pavement infrastructure. Therefore, the detection of deteriorating surfaces can be improved by analyzing the radar images timely. The results showed the deficiencies on the surface that can be used to mitigate bad pavement conditions and allow road users to use good road infrastructure with safety and comfort.
Bridging technology and healthcare: user acceptance of a surgical site infection system Akhmad, Afan Fatkhur; Ulfa, Maria
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp523-532

Abstract

Surgical site infections (SSI) continue to be a problem for surgeons, and unfortunately, SSI information systems are underutilized. This study analyzed the user acceptance of the SSI information system based on the extended technology acceptance model (TAM2). A cross-sectional questionnaire-based study. The variables studied intention to use (IU), perceived ease of use (PEOU), demographic factors (FD), subjective norm (SN), Image (I), job relevance (JR), output quality (OQ), result demonstrability (RD), perceived usefulness (PU). Data were collected by filling out questionnaires and then analyzed using smart-partial least squares (PLS). In total, 61 nurses were included. Most respondents are aged 31-35 (26.23%), and most working periods are between 11-15 years (27.87%). There were significant positive effects on SN to PU (β=0.12; p 0.05). This study concluded that PEOU is the most influential variable in the IU the SSI information system.
Content based image retrieval using visual geometric group19 with Jaccard similarity measure Narayanaswamy, Rajath Arakere; Gowda, Vidyalakshmi Krishne
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp430-438

Abstract

Content-based image retrieval (CBIR) is an important research area that focuses on emerging techniques for handling large image collections and enabling efficient retrieval. The main challenge of image retrieval is to extract relevant feature vectors for image description. Therefore, visual geometric group 19 (VGG19) with Jaccard is proposed in this research for CBIR. The VGG19 allows to capture of hierarchical features, and it is appropriate for texture and fine detail characteristics. It enables to production of robust and discriminative feature representations because of numerous convolutional layers. The Jaccard is utilized as a similarity measure among feature vectors by comparing the union and intersection of feature sets. It is helpful to manage sparse and higher-dimensional data that provides a fast and accurate image retrieval process. The Caltech 256 and Corel 1K datasets are considered and it is preprocessed by image resizing and normalization. The raw images are resized to ensure dataset similarity and normalized into the range of 0 and 1. The metrics such as recall, f-measure, and average precision are used to calculate the VGG19-Jaccard performance. The VGG19-Jaccard achieves average precision of 99.0 and 99.8% for Caltech 256 and Corel 1K datasets compared to the two-stage CBIR technique.
Techno-economic analysis and optimization of solar energy systems: a case study at Ar-Raniry State Islamic University Saputri, Fahmy Rinanda; Linelson, Ricardo; Salehuddin, Muhammad; Al-Haidar, Muhammad Dzaky
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp322-335

Abstract

This research examines the implementation of a solar power generation system at Ar-Raniry State Islamic University (UIN Ar-Raniry), specifically focusing on the Faculty of Tarbiyah and Keguruan building. The study aims to enhance energy efficiency, assess economic feasibility, and reduce environmental impacts by optimizing solar energy potential through variables such as local meteorological conditions, panel orientation, tilt angles, and system efficiencies. Utilizing PVSyst software for simulations, the research evaluates technical performance, life cycle costs, and carbon dioxide (CO₂) emission reductions. The results indicate that the solar Photovoltaic (PV) system can generate 251,214 kWh annually while reducing CO₂ emissions by 173,095 kg. Economically, the investment is deemed feasible, with a payback period of 7.8 years, a lower cost of energy (LCOE) compared to State Electricity Company (PLN) tariffs, a positive net present value (NPV), and a high internal rate of return (IRR). Although there are minor losses in thermal and module quality, the system remains effective. This study contributes significantly to sustainable energy policies in higher education and recommends further long-term performance monitoring and exploration of additional renewable energy technologies on campus.
Optimized control strategy for enhanced stability in grid-connected photovoltaic-wind hybrid energy systems Thiruveedula, Madhu Babu; Kaliyamoorthy, Asokan; Sravani, Kosara; Yadav, Murgam Sharath; Kumar, Petturi Satish; Akash, Routhu
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp609-617

Abstract

To improve stability in grid-connected photovoltaic-wind (PV-wind) hybrid energy systems, this research presents optimized model predictive control (MPC) and proportional resonant (PR) control algorithms. The proposed MPC strategy enhances power management by forecasting future system behavior and optimizing control actions accordingly, while the PR controller effectively handles grid-synchronized voltage regulation and harmonic compensation. Together, these advanced control techniques significantly improve grid stability, ensure optimal utilization of renewable energy resources (RER), and maintain power quality under varying operating conditions. The performance of the hybrid system is evaluated through extensive simulations that consider a range of real-world scenarios, including fluctuating load demands and diverse climatic conditions. The results confirm the effectiveness of the proposed MPC and PR-based control in dynamically adjusting power output from wind and photovoltaic sources, thereby ensuring reliable and efficient grid integration. These findings highlight the potential of intelligent control systems in enabling the secure, stable, and long-term adoption of renewable energy within modern power grids.
Analysis of ice creams from goat milk kefir and red dragon fruit Santoso, Aman; Radiati, Lilik Eka; Damayanthi, Evy; Armaini, Armaini; Mujahidah, Amiroh Nabilah; Sanjaya, Eli Hendrik; Muntholib, Muntholib; Asrori, Muhammad Roy
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp481-489

Abstract

Ice cream from goat milk kefir is lower lactose than cow milk kefir. Combining goat milk kefir with red dragon fruit in ice cream formulations can improve the quality of the product. This study aims to determine the sensory characteristics, total solid, total flavonoid, and antioxidant activity of goat kefir-based ice cream flavored red dragon fruit as quality evaluation. The study used a completely randomized design with 4 treatments and 3 replications. The treatment was the ratio of goat kefir and dragon fruit, including 40:60, 50:50, 60:40, and 70:30 in the ice cream mixture. 30 panelists participated in the organoleptic test. Total solid testing referred to SNI 01-3713-1995. Determination of total flavonoid content was carried out by forming AlCl3 complexes spectrophotometrically and assaying antioxidant activity used 2.2-diphenyl-1-picrylhydrazyl (DPPH) method. The results showed that there was no significant difference in the organoleptic test for taste, color, and texture. The results of the total solids test showed that the higher the addition of goat kefir to ice cream, the lower the total solids produced. While the addition of goat kefir increased the total flavonoids in ice cream. The antioxidant activity with the best formulation of 50:50 was categorized as moderate level, which is 136.59 ppm.
A deep learning-based myocardial infarction classification based on single-lead electrocardiogram signal Darmawahyuni, Annisa; Sari, Winda Kurnia; Afifah, Nurul; Tutuko, Bambang; Nurmaini, Siti; Marcelino, Jordan; Isdwanta, Rendy; Khairunnisa, Cholidah Zuhroh
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp352-360

Abstract

Acute myocardial infarction (AMI) carries a significant risk, emphasizing the critical need for precise diagnosis and prompt treatment of the responsible lesion. Consequently, we devised a neural network algorithm in this investigation to identify myocardial infarction (MI) from electrocardiograms (ECGs) autonomously. An ECG is a standard diagnostic tool for identifying acute MI due to its affordability, safety, and rapid reporting. Manual analysis of ECG results by cardiologists is both time-consuming and prone to errors. This paper proposes a deep learning algorithm that can capture and automatically classify multiple features of an ECG signal. We propose a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) for automatically diagnosing MI. To generate the hybrid CNN-LSTM model, we proposed 39 models with hyperparameter tuning. As a result, the best model is model 35, with 86.86% accuracy, 75.28% sensitivity and specificity, and 83.56% precision. The algorithm based on a hybrid CNN-LSTM demonstrates notable efficacy in autonomously diagnosing AMI and determining the location of MI from ECGs.
Susceptibility of Aedes aegypti to malathion and permethrin insecticides in Enrekang Regency: an experimental study Sulasmi, Sulasmi; Ahmad, Hamsir; Juherah, Juherah; Suryadi, Iwan; Rachmawati, Siti
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp291-299

Abstract

Insecticide resistance in Aedes mosquitoes can undermine arbovirus control efforts. Malathion and permethrin insecticides belong to the group of insecticides used for control and if used continuously will cause immunity of target mosquitoes. This study aims to assess the level of susceptibility of Aedes aegypti to insecticides commonly used in public health in the Enrekang Regency. The type of research used was experimental research. Female Aedes aegypti were collected from rearing results with a total sample size of 240 mosquitoes which were divided into 120 mosquitoes each in 4 treatments and 2 controls on malathion 0.8% and permethrin 0.25% insecticides. The results obtained from the research on insecticide susceptibility test results using malathion 0.8% in 60 minutes of exposure averaged 55% dead and exposure for 24 hours averaged 90% mosquito death, while permethrin 0.25% insecticide in 60 minutes of exposure averaged 90% dead mosquitoes and 24 hours exposure averaged 100% mosquito death, while for the control all live. The conclusion of the study was the susceptibility test of Aedes aegypti mosquitoes to malathion 0.8% insecticide in the category of moderate resistance while permethrin 0.25% insecticide in the category of susceptible.
Sentiment analysis of vaccine data using enhanced deep learning algorithms Verma, Monika; Monga, Sandeep
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp562-579

Abstract

This paper investigates and experiments with an approach to improve sentiment analysis on vaccine datasets with deep learning. It evaluates random forest (RF), naïve Bayes (NB), and recurrent neural network (RNN) models across a variety of configurations, i.e., vector dimensions, pooling techniques, as well as evaluation methods, hierarchical SoftMax vs negative sampling. The results show that the model we proposed prevailed with an accuracy of 99.05% on a learning rate equal to 0.001, outperforming all other models based on metrics including precision, recall, and F1-score for benign/malignant cases. The results suggest that higher vector dimensions, average pooling, lowering the dropout rate, and employing hierarchical SoftMax for output significantly improve model performance. Hierarchical SoftMax performs better than negative sampling, whereas a lower dropout rate decreases overfitting and leads to improved generalization. Our results demonstrate the necessity to apply more sophisticated deep-learning tools around capturing nuances of public vaccine-related sentiment, which may be crucial for informing communication strategies and supporting decision-making in a real-world health emergency. The findings indicate that the performance of sentiment analysis with regard to COVID-19 vaccine deployment policy design and public monitoring could be enhanced by advanced deep learning algorithms.
Deep learning approach for monkeypox virus prediction: leveraging DensetNet-121 and image data Chinthala, Kishor Kumar Reddy; Kaza, Vijaya Sindhoori; Ashritha, Pilly; Shuaib, Mohammed; Alam, Shadab; Fakhreldin, Mohammad
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp439-453

Abstract

The Mpox virus, sometimes referred to as monkeypox, causes flu-like symptoms and rashes. The variola virus, which causes smallpox, is linked to the virus that causes monkeypox. Smallpox symptoms are more severe than those of Mpox, and the illness is rarely deadly. There is no connection between Mpox and chickenpox. The variola virus of smallpox and the vaccinia virus being used in the smallpox vaccine both belong to the Orthopoxvirus genus, which also includes the uncommon viral disease known as monkeypox. This study aims to increase the effectiveness of monkeypox virus (MPV) identification by utilizing global historical records. This study examines several approaches and determines which produces the best results for the input data. Performance metrics have been used to compare the efficiency to current models. The underlying patterns and correlations in the data are then taught to Dense-Net-121 through the use of the training set. The remarkable results are as follows: accuracy at 96.12%, precision at 93.2%, recall at 90%, F1-score at 91%, the area under the curve-receiver operating characteristic (AUC-ROC) at 94.5%, and specificity at 94%, outperforming the existing methods.

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