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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 680 Documents
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Impact of natural-white and red-blue light-emitting diode lighting on hydroponic basil growth and energy efficiency Boonmee, Chaiyant; Srisongkram, Warunee; Wongsuriya, Wipada; Sritanauthaikorn, Patcharanan; Kiatsookkanatorn, Paiboon; Watjanatepin, Napat
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.pp406-415

Abstract

Advanced phosphor-converted white light-emitting diodes (pc-WLEDs) have been developed to mimic the natural sunlight spectrum, potentially enhancing plant growth compared to traditional red-blue (R-B) LEDs. This study aimed to compare the effects of natural-white pc-WLED (nsW-pcLED) and conventional R-B LED (R:B 3.24) on the growth, yield, and energy efficiency of hydroponically grown sweet basil. It was cultivated in a deep-water culture system under identical conditions with a photosynthetic photon flux density (PPFD) of 200±10 µmol·m⁻²·s⁻¹ and a 16/8 light/dark photoperiod over 28 days. Key growth parameters, including plant height, stem diameter, leaf number, and plant fresh weight (PFW), were measured, while energy consumption was recorded to assess efficiency. Results indicated that nsW-pcLED significantly enhanced growth, with plants achieving an average height of 44.30±1.51 cm, stem diameter of 6.68±0.21 mm, and a PFW of 34.20±6.12 g, compared to 35.88±4.05 cm, 4.66±0.88 mm, and 23.02±5.26 g under R-B LED (p <0.05), respectively. The nsW-pcLED treatment produced an average net growth of 1,221 g·m⁻² versus 536.43 g·m⁻² for R-B LED and delivered 33.05 g·m⁻²·kW·h⁻¹ compared to 11.17 g·m⁻²·kW·h⁻¹, while consuming 23% less energy. These findings highlight nsW-pcLED’s superior performance for indoor hydroponic cultivation. Future studies should explore its application in large-scale systems and across diverse crop species.
Enhancing artificial neural network performance for energy efficiency in laboratories through principal component analysis Desmira, Desmira; Bakar, Norazhar Abu; Hamid, Mustofa Abi; Hakiki, Muhammad; Ismail, Affero; Fadli, Radinal
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.pp310-321

Abstract

This study investigates energy efficiency challenges during laboratory activities. Inefficient energy use in the practicum phase remains a critical issue, prompting the exploration of innovative forecasting models. This research employs artificial neural network (ANN) models integrated with principal component analysis (PCA) to predict energy consumption and optimize usage. The findings reveal that PCA components, including eigenvalues, eigenvectors, and matrix covariance values, significantly influence the ANN model's performance in forecasting energy production. The ANN training achieved a high correlation coefficient (R=1) with a mean squared error (MSE) of 0.045931 after 200,000 epochs, demonstrating the model's robustness. While testing results showed a moderate correlation (R=0.46169), the models demonstrated potential for refinement and scalability. This integration of ANN and PCA models provides a reliable framework for accurately forecasting energy usage, offering an effective strategy to enhance energy efficiency in laboratory settings. By optimizing energy consumption, this approach has the potential to reduce operational costs and environmental impact. The strong performance metrics highlight the practical utility of these models in educational contexts, contributing to sustainable energy management and better resource allocation. Furthermore, the reduction in energy-related environmental impacts underscores the broader applicability of these models for fostering sustainable development in similar contexts.
Carbonized mangrove wood as photothermal material for solar water desalination Pandara, Dolfie Paulus; Unso, Kristina; Bobanto, Maria Daurina; Tamuntuan, Gerald Hendrik; Angmalisang, Ping Astony; Ferdy, Ferdy; Tiwow, Vistarani Arini; Kumaunang, Maureen
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.pp542-551

Abstract

The investigation into the physical properties of carbonized mangrove wood (CMW) is essential for its development as an efficient solar heat absorber. This study explores the physical characteristics of CMW and its potential application in solar desalination. Initially, the mangrove wood was cleaned with running water, followed by ultrasonication at a frequency of 42 kHz in 96% ethanol for 5 minutes, and then heated at 125 °C for 2 hours. The carbonization process was conducted in a furnace for 1 hour at temperatures of 400, 500, and 600 °C. The physical properties of CMW were analyzed using an X-ray diffractometer (XRD), Fourier transform infrared spectroscopy (FTIR), energy dispersive spectroscopy, and scanning electron microscopy (SEM). The findings revealed the formation of a carbon structure at 2 theta angles of approximately 24.08, 23.26, and 23.16°, with carbon contents of 45.05, 36.86, and 39.37%, respectively. CMW was identified as a porous material, making it highly effective for sunlight absorption in seawater evaporation. The hydroxyl content within the CMW structure enhanced its water evaporation capabilities. In experimental investigations aimed at desalinating seawater, a 300-watt halogen lamp was positioned 15 centimeters above the CMW's surface, resulting in an evaporation rate of 5.33 kg.m-2.h-1. CMW shows significant promise as a solar evaporator.
Optimizing diabetes prediction using machine learning: a random forest approach Maenge, Aone; Sigwele, Tshiamo; Bhende, Cliford; Mokgethi, Chandapiwa; Kuthadi, Venumadhav; Omogbehin, Blessing
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.pp454-468

Abstract

Diabetes, a leading cause of global mortality, is responsible for millions of deaths annually due to complications such as heart disease, kidney failure, and stroke. Projections indicate that 700 million people will be affected by diabetes in 2045, placing immense strain on global healthcare systems. Early detection and accurate prediction of diabetes are essential in mitigating complications and reducing mortality rates. However, existing diabetes prediction frameworks face challenges, including imbalanced datasets, overfitting, inadequate feature selection, insufficient hyperparameter tuning, and lack of comprehensive evaluation metrics. To address these challenges, the proposed random forest diabetes prediction (Random DIP) framework integrates advanced techniques such as hyperparameter tuning, balanced training, and optimized feature selection using a random search cross-validation (RandomizedSearchCV). This framework significantly improves predictive accuracy and ensures reliable clinical applicability. Random DIP achieves 99.4% accuracy, outperforming related works by 7.23%, the area under curve (AUC) of 99.6%, surpassing comparable frameworks by 7.32%, a recall of 100%, exceeding existing models by 9.65%, a precision (97.8%), F1-score (98.9%), and outperformance of 6.69%. These metrics demonstrate Random DIP's excellent capacity to identify diabetes cases while minimizing false negatives (FPs) and providing reliable predictions for clinical use. Future work will focus on integrating real-time clinical data and expanding the framework to accommodate multi-disease prediction for broader healthcare applications.
Analyzing the key factors and perspectives of stakeholders in pavement maintenance Soni, Jaykumar; Gujar, Rajesh; Malek, MohammedShakil S.
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.pp336-344

Abstract

Road infrastructure is important for societal and economic development; therefore, it is crucial to maintain the durability and safety of the pavements. The present study investigates the domain of pavement maintenance by thoroughly analyzing the factors affecting the quality of pavement considering diverse groups of stakeholders. The study explored various flexible pavement defects (distress factors i.e., potholes, alligator cracks, longitudinal cracks, transverse cracks, hungry surfaces, streaking, shoving, rutting, and raveling). The opinions of stakeholders from various sectors such as public, private, and academia are collected through surveys, interviews, and detailed discussions. The collected data is analyzed using advanced statistical tools such as analysis of variance (ANOVA), post hoc test, criticality index, and Spearman rank correlation, which revealed patterns and correlations between stakeholder views. This study highlights diverse perspectives on pavement distress factors, providing valuable insights into the decision-making process. The findings of this research will help policymakers prioritize pavement maintenance based on the prevailing distresses, highlighting the importance of informed decision-making in pavement maintenance and management practices.
Crowdfunding platform integrated with cryptocurrency payment support Rosalina, Rosalina; Sahuri, Genta
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.pp598-608

Abstract

Crowdfunding platforms often face challenges such as high transaction fees, limited global accessibility, and reliance on traditional banking systems, which restrict participation and efficiency. These limitations hinder the full potential of crowdfunding, particularly for global contributors and projects. This research addresses these issues by proposing the development of a mobile crowdfunding platform integrated with cryptocurrency payment support. By incorporating cryptocurrency, the platform aims to reduce transaction costs, remove geographical barriers, and enhance transaction security through blockchain technology. The platform is built using a cross-platform mobile framework to ensure broad accessibility while integrating cryptocurrency gateways for decentralized financial transactions. This allows for real-time, secure, and low-cost payments, offering a transparent and efficient process for both contributors and fundraisers. Additionally, the platform's design supports scalability to accommodate various cryptocurrencies and an expanding user base. The findings demonstrate that cryptocurrency payment integration significantly improves transaction speed, reduces fees, and enhances security compared to traditional payment methods. It also fosters global participation, increasing engagement in crowdfunding initiatives.
Birth data clustering to segmentation delays in birth certificate registration Hasmin, Erfan; Rahman, Aedah Abd
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.pp513-522

Abstract

Timely and accurate birth registration is essential for ensuring access to vital public services. This study focuses on clustering birth data to identify patterns in registration delays, using data mining techniques such as the K-means algorithm. By clustering birth data from Makassar City, Indonesia, based on various demographic and birth-related criteria, the study segments the data into groups that reflect both timely and delayed registrations. The optimal number of clusters is determined using the elbow and silhouette methods. Results show that a three-cluster configuration effectively captures patterns in birth registration delays, offering critical insights for policymakers. These findings provide a foundation for improving birth registration processes, ensuring more timely registration, and guiding data-driven public policy decisions.
A gamified online learning environment with comprehensive assessments and software integration Shilaskar, Swati; Bhatlawande, Shripad; Deshpande, Rupali; Shinde, Shivam; Madake, Jyoti; Solanke, Anjali
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.pp416-429

Abstract

The National Achievement Survey (NAS), conducted by the Ministry of Education, India, highlighted a concerning decline in mathematics proficiency among students in Maharashtra as they advance through grades. This trend is further aggravated by the limited availability of online resources in Marathi, hindering their learning progress. To address this, a pilot study was proposed to develop a specialized online platform tailored for Marathi medium students, integrating gamification and artificial intelligence (AI)-driven feedback to enhance engagement and comprehension. The pilot project, conducted at a Marathi medium school with approval from the principal, focused on polynomial division tests for 8th-grade students over four days. Results revealed that despite the easy level test's higher difficulty, students scored higher on the medium level test, possibly due to an adjustment period to the online platform. Notably, some students performed better on the hard-level test, indicating the platform's potential to improve performance. While promising, the study's limitations, including a small sample size, highlight the need for further research with a larger cohort and the integration of automatic suggestions for concept-specific games and assessments in future iterations to optimize the platform's effectiveness.
A review on ischemic heart disease prediction frameworks using machine learning Bhende, Kabo Clifford; Sigwele, Tshiamo; Mokgethi, Chandapiwa; Maenge, Aone; Kuthadi, Venu Madhav
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.pp361-372

Abstract

Ischemic heart disease (IHD) is a leading cause of mortality worldwide, calling for advanced predictive models for timely intervention. Current literature reviews on machine learning (ML)-based IHD prediction frameworks often focus on predictive accuracy but lack depth in areas like dataset diversity, model interpretability, and privacy considerations. Existing IHD prediction frameworks face limitations, including reliance on small, homogenous datasets, limited critical analysis, and issues with model transparency, reducing their clinical utility. This review addresses these gaps through a systematic, comparative analysis of popular ML models, such as random forest (RF) and support vector machines (SVM), noting their strengths and limitations. Key contributions include a qualitative examination of prevalent tools, datasets, and evaluation metrics, identification of gaps in dataset diversity and interpretability; and recommendations for improving model transparency and data privacy. Major findings reveal a trend toward ensemble models for accuracy but highlight the need for explainable artificial intelligence (AI) to support clinical decisions. Future directions include using federated learning to enhance data privacy, integrating unstructured data for comprehensive prediction, and advancing explainable AI to build trust among healthcare providers. By addressing these areas, this review aims to guide future research toward developing robust, transparent ML frameworks that can be more effectively deployed in clinical settings.
Development of a digital-based fiber tensile testing apparatus to enhance fiber testing accuracy Iswar, Muhammad; Suyuti, Muhammad Arsyad; Nur, Rusdi; Muttaqin, Ahmad Nurul
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.pp552-561

Abstract

Natural fibers are increasingly used in various industries due to their eco-friendly properties and cost-effectiveness. However, current methods for testing the mechanical properties of these materials, such as tensile strength, often face limitations in accuracy and efficiency. This study aims to develop an innovative digital-based fiber tensile testing apparatus to enhance the precision of tensile testing. The research involves the design and construction of the apparatus, utilizing components such as ST37 steel, stepper motors, and Arduino technology. The apparatus was tested using two types of natural fibers, Cocos nucifera L. (coconut fiber) and Sansevieria, to assess their tensile properties. The results showed that although Sansevieria fibers have a smaller diameter, they exhibited higher tensile stress compared to coconut fibers. The developed digital testing apparatus enables more accurate and efficient fiber testing, contributing to the development of stronger and more sustainable materials for industrial applications. The findings of this study highlight the potential of advanced testing equipment in supporting the use of natural fibers in manufacturing and environmental sustainability.

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