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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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Contraction control factor-based gorilla troop optimizer for features in intrusion detection systems Sharma, Shalini; Khaitan, Supriya; Hegde, Gayatri; Rohatgi, Divya; Rafique, Nusrat Parveen Mohammad; Lawand, Suhas Janardan
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.pp373-383

Abstract

Internet of things (IoT) has evolved into a large-scale network due to the increasing number of connected devices and massive amount of data they generate. IoT networks produce massive amounts of heterogeneous data from various devices, making it difficult to identify relevant features for intrusion detection. Hence, this research proposes the contraction control factor-based gorilla troop optimizer (CCF-GTO) for feature selection and multiple parametric exponential linear units based long short-term memory (MPELU-LSTM) approach for classification of intrusion detection system (IDS) in IoT. CCF-GTO. It uses adjustable parameters to prioritize relevant information while eliminating unnecessary features, making the model more efficient and resulting in better classification accuracy. The experimental results demonstrate that the MPELU-LSTM approach achieves better accuracy of 99.56% on the UNSW-NB15 dataset as compared to the earlier approaches like convolutional neural network with LSTM (CNN-LSTM) and optimized deep residual convolutional neural networks (DCRNN). These findings suggest that the MPELU-LSTM method significantly enhances the accuracy and robustness of IDS in IoT environments by addressing issues like the identification of relevant features and feature redundancy, contributing to more effective and secure systems. This research has valuable implications for enhancing the security bearing of IoT infrastructure.
A course review analysis using bidirectional long short-term memory model Tatavarthy, Murthy Venkata Surya Narayana; Vadlamani, Naga Lakshmi
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.pp580-589

Abstract

In recent years, sentiment analysis and online review analysis have gained popularity as critical components in the growth and development of educational courses. An innovative method has been created to increase the quality of learning experiences by rapidly collecting relevant data from course comments. This technique leverages bidirectional encoder representation from transformers (BERT) for word vector training. When combined with a learning mechanism, the recommended BERT accurately predicts the sentiment of online course reviews. Additionally, a dual-channel model based on Bi-directional long short-term memory (Bi-LSTM) is employed to improve sentiment data and semantics. Following data collection from the Coursera dataset, preprocessing approaches such as tokenization, stop words removal and sentence metric creation are applied to convert input data into word vectors and identify fundamental text units using text segmentation. The results demonstrate the proposed approach’s superiority over existing methods, offering an accuracy of 81.45%, recall of 94.9%, precision of 93.7%, and F-score of 93.7%.
Usability evaluation of ToAksara as Balinese script learning mobile application Indrawan, Gede; Sariyasa, Sariyasa; Dewi, Luh Joni Erawati; Gitakarma, Made Santo; Gunawan, I Made Agus Oka; Pranata, Putu Ade
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.pp490-501

Abstract

ToAksara application transliterates Latin text into Balinese script and has been used in high school teaching and learning activities in Buleleng Regency, Bali, Indonesia. This application was expected to provide comfort and satisfaction for students while learning the Balinese language and script. To measure the comfort and satisfaction level, a usability evaluation was carried out that focused on the application's end user. This research used a combination of concurrent think-aloud (CTA) and user experience questionnaire (UEQ) to evaluate ToAksara. In CTA, data collection involved nine respondents given a task scenario and expressing their problems or input. In UEQ, data collection involved 385 respondents who chose the value closest to their impression of 26 statements. Based on the analysis results, CTA produced several recommendations for improving the application regarding navigation, functionality, and errors. Based on the analysis, the user satisfaction results showed that all aspects were included in the excellent category. The aspects of attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty each produced a value of 2.144, 2.220, 2.385, 2.345, 2.139, and 2.101. The excellent category shows that ToAksara was included in the range of the top 10% of products compared to the UEQ benchmark.
Energy efficient direct transesterification of Nannochloropsis sp. using hydrodynamic cavitation Nirmalasari, Jiran; Setyawan, Martomo; Jamilatun, Siti; Pitoyo, Joko; Hakika, Dhias Cahya
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.pp394-405

Abstract

The increasingly limited supply of fossil fuels requires renewable fuel as an alternative source. Nannochloropsis sp. is a microalgae species containing a lipid content of between 12 and 53%, which can be converted to biofuel as an alternative source of fossil fuels through a transesterification process. Up to this date, the literature has reported no studies on biodiesel production from Nannochloropsis sp. via direct transesterification with catalyst using hydrodynamic cavitation. The direct transesterification process introduced 7.5 g of microalgae, 40 ml of methanol, 90 ml of hexane, and 0.0225 g of sodium hydroxide into the sample chamber. These mixtures were passed within the cavitation using a pressure driver and transformed into fatty acid methyl ester (FAME). The catalytic hydrodynamic cavitation method produces a higher extract yield than the stirring one. Regarding the FAME composition, the catalytic hydrodynamic cavitation method is dominated by saturated fatty acid (palmitic), while the stirring catalytic method is dominated by monounsaturated fatty acid (oleic). The hydrodynamic cavitation method provides a lower average degree of unsaturation and shorter chain length than the stirring catalytic method.
Advanced classification techniques for weed and crop species recognition using machine learning algorithms Rajendran, Sathya; KS, Thirunavukkarasu
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.pp300-309

Abstract

This study proposes an intelligent machine learning framework integrating image analysis and environmental data for precision weed management. The framework leverages efficient feature extraction techniques combined with supervised machine learning algorithms to accurately classify multiple species. Features such as color, texture, and shape characteristics are utilized for model training, enabling high-precision classification while maintaining low computational complexity. The experimental results demonstrate the robustness of the approach, achieving an average classification accuracy of 94.3% across ten weed and crop species in diverse agricultural environments. The system also achieved a 90% reduction in herbicide application compared to traditional methods, showcasing its potential for sustainable farming. Real-time testing confirmed the framework’s efficiency, processing images in under 1.5 seconds per frame, making it suitable for deployment in drones and autonomous farming equipment. These results underscore the practical and scalable nature of the proposed system in automating weed management and advancing sustainable agricultural practices.
Security analysis of Indonesia e-commerce platform against the risk of phishing attacks Saskara, Gede Arna Jude; Permana, Made Ody Gita; Sunarya, I Made Gede
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.pp533-541

Abstract

This research analyses the security of e-commerce platforms in Indonesia against the risk of phishing attacks using the social-engineer toolkit (SET) application. Of the 31 platforms tested, it was found that 22 platforms have a low-security level because they can be easily replicated to carry out phishing attacks. In contrast, 9 platforms showed a high level of security, as they implemented the step-wise authentication and embedded login methods, which proved effective in protecting the platform from phishing attacks. The effectiveness rate of the SET application in conducting tests was recorded at 70.9%; the percentage is included in the high category. This research also identified that most low-security platforms still use the single-page login method or a special URL for login, making them very vulnerable to phishing attacks. The action research method was used as the research framework, involving five stages: diagnosis, planning, action, evaluation, and learning. The results of this study provide important guidance for platform owners to improve security mechanisms, how to build a login page to avoid the risk of misuse by cybercrime actors to conduct phishing attacks, and for users as a reference to choose a more secure e-commerce platform.
Combining XGBoost and hybrid filtering algorithm in e-commerce recommendation system Sinaga, Vincentius Loanka; Wibowo, Antoni
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.pp618-626

Abstract

This study proposes a hybrid filtering algorithm (HFA) that combines extreme gradient boosting (XGBoost), content-based filtering (CBF), and collaborative filtering (CF) to improve recommendation accuracy in electronic commerce (e-commerce). XGBoost first leverages demographic data (e.g., age, gender, and location) to address cold start conditions, producing an initial product prediction; CBF refines this prediction by measuring product similarities through term frequency-inverse document frequency (TF-IDF) and cosine similarity, while CF (implemented via singular value decomposition) further incorporates user interaction patterns to enhance recommendations. Experimental results across multiple datasets demonstrate that HFA consistently outperforms standalone XGBoost in key metrics, including precision, F1-score, and hit ratio (HR). HFA’s precision often exceeds 90%, indicating fewer irrelevant recommendations. Although recall levels remain modest, HFA exhibits stronger adaptability under cold start scenarios due to its reliance on demographic features and user-item interactions. These findings highlight the efficacy of combining advanced machine learning with hybrid filtering techniques, offering a more robust and context-aware solution for e-commerce recommendation systems.
Comparative study on fine-tuning deep learning models for fruit and vegetable classification Mamat, Abd Rasid; Mohamed, Mohamad Afendee; Kadir, Mohd Fadzil Abd; Rawi, Norkhairani Abdul; Aziz, Azim Zaliha Abd; Awang, Wan Suryani Wan
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.pp384-393

Abstract

Fruit and vegetable recognition and classification can be a challenging task due to their diverse nature and have become a focal point in the agricultural sector. In addition to that, the classification of fruits and vegetables increases the cost of labor and time. In recent years, deep learning applications have surged to the forefront, offering promising solutions. Particularly, the classification of fruits using image features has garnered significant attention from researchers, reflecting the growing importance of this area in the agricultural domain. In this work, the focus was on fine-tuning hyperparameters and the evaluation of a state-of-the-art deep convolutional neural network (CNN) for the classification of fruits and vegetables. Among the hyperparameters studied are the number of batch size, number of epochs, type of optimizer, rectified unit, and dropout. The dataset used is the fruit_vegetable dataset which consists of 36 classes and each class contains 1,000 images. The results show that the proposed model based on the batch size=64 and the number of epochs=25, produces the most optimal model with an accuracy value (training) of 99.02%, while the validation is 95.73% and the loss is 6.06% (minimum).
Optimization of cashew apple extract as a tomato sauce substitute in chicken steak marinades Susanti, Siti; Lestari, Fatma Puji; Setiadi, Agus; Hartoyo, Budi; Al-Baarri, Ahmad Ni'matullah
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.pp590-597

Abstract

This study aims to optimize the use of cashew apple extract (CAE) as a substitute for tomato sauce in marinades and evaluate its effects on the chemical and sensory qualities of chicken steak. Four different marinade formulations containing varying concentrations of CAE (0, 5, 10, and 15%) were applied to chicken steak samples. Chemical analyses measured protein, fat content, and polycyclic aromatic hydrocarbon (PAH) levels, while sensory evaluations were conducted to assess tenderness, juiciness, aroma, and overall preference using a semi-trained panel. The results demonstrated that increasing CAE concentrations significantly elevated protein content (p<0.05) and reduced fat levels. PAH levels were below detectable limits in all samples, suggesting the marinade’s potential in reducing PAH formation. Sensory analysis revealed that the 5% CAE marinade was the most preferred, particularly for tenderness and juiciness. These findings suggest that CAE is a viable alternative to tomato sauce in marinades, offering both nutritional benefits and consumer acceptability.
Study of a model of a satellite structure that meets the necessary criteria for stability and rotation in space Idan, Mahmoud Fadhel; Hussein, Osamah Mahmood
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.pp502-512

Abstract

The study aimed to create a model of a satellite structure that meets the necessary criteria for stability and rotation in space. The satellite being analyzed has an octagonal shape, with a diameter of 110 cm and a height of 85 cm. A dynamic modeling approach was used to analyze the structural properties, and the finite element method (FEM) was employed for computational analysis. This method allowed for a comprehensive evaluation of stress, displacement, and vibration distribution throughout the structure, providing insight into the behavior of the communications satellite in space. The test model frame consists of plates and bars arranged in an octagonal shape. The analysis utilized the von Mises stress (σvM) criterion to assess the yield strength or brittleness of the chosen material, 7,057 aluminum alloy. The study revealed that the structure demonstrates stability in six different modes but also exhibits deformation due to modifications in the basic arrangement. Additionally, transient fluctuations in the spacecraft's position over a 24-hour cycle result in changes in torque. The structure remains stable within a specified frequency range starting at 150 Hz when subjected to vibration stimuli, and no external instability was detected within this range.

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