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31,456 Article Results

Effect of fasteners variations on the performance of one-phase induction motors in bio-pellet production process

10.11591/ijaas.v15.i1.pp253-260
Ediwan Ediwan , Arnawan Hasibuan , Abubakar Dabet , Muhammad Daud , Fajar Syahbakti Lukman , Gandi Supriadi
Indonesia has many oil palm plantation areas. One of the negative impacts is the large amount of empty fruit bunch (EFB) waste. Utilizing EFB as a bio pellet as a renewable energy source is one of the solutions to reduce waste while supporting the green energy transition. EFB bio-pellets have the potential to replace fossil fuels, but face challenges in setting good quality standards. The production process of EFB bio-pellets uses a variety of binder contents. This study aims to analyze the influence of different levels of binder content on the quality of bio-pellet products. Statistical analysis of linear regression was performed to measure energy consumption and motor performance in the production process of EFB bio-pellets. This study provides recommendations to help maximize the quality and efficiency of the bio-pellet production process from palm oil EFB waste.
Volume: 15
Issue: 1
Page: 253-260
Publish at: 2026-03-01

Financial distress prediction for batik small and medium enterprises credit financing based on deep learning algorithm

10.11591/ijaas.v15.i1.pp245-252
Taryadi Taryadi , Bambang Sudiyatno , Robertus Basiya , Era Yunianto
One of the biggest obstacles that any finance provider has when evaluating a borrower's creditworthiness is the prediction of financial trouble. The credit decision-making process is made more difficult for small and medium enterprises (SMEs) due to their inherent ambiguity, which raises financing costs and lowers the chance of approval. In order to estimate a binomial classifier for predicting financial hardship using logistic regression (LR), extreme gradient boosting (XGBoost), and artificial neural network (ANN) techniques, this study examines data from batik SMEs in Pekalongan city. Financial ratios predict the first period and grow in a multi-period model based on temporal factors, credit history, and age. Financial distress is defined as a substantial obstacle to a business's capacity to pay its debts as opposed to the potential for bankruptcy. The long short-term memory (LSTM) algorithm with more variables yields the best prediction accuracy. The study's conclusion indicates that in order to guarantee the accuracy of financial distress prediction, the time at risk must be adjusted.
Volume: 15
Issue: 1
Page: 245-252
Publish at: 2026-03-01

Crop prediction in Tamil Nadu according to environmental and soil factors using hybrid machine learning architecture

10.11591/ijaas.v15.i1.pp405-415
Sundaraj Kannan Susee , Shenbagaramasubramanian Shenbaga Vadivu , Murugesan Senthil Kumar
Mathuranthagam, Tamil Nadu, India is the site of this research initiative that employs state-of-the-art hybrid machine learning (ML) architectures to forecast crop suitability in relation to environmental and soil characteristics. The model takes advantage of the strengths of linear support vector machine (SVM) classifier, bidirectional long short-term memory (BiLSTM), and convolutional LSTM (ConvLSTM) networks, and the data to capture complicated temporal and spatial correlations. To prepare the dataset for model training, it is normalized using min-max scaling and then feature selected using a Jaya optimization technique. The dataset contains variables such as humidity, rainfall, temperature, and pH. Both the BiLSTM and the ConvLSTM improve the model's comprehension of context from both previous and subsequent time steps. The ConvLSTM also records spatial dependencies. A powerful decision-making tool for differentiating across crop varieties is the linear SVM classifier. Comparing the hybrid model's performance to that of traditional LSTM approaches using measures such as recall, accuracy, precision, and F1-score shows that it performs much better. Using this approach can see how deep learning (DL) can supplement more conventional ML methods and see how important local environmental data is for agricultural policy and planning.
Volume: 15
Issue: 1
Page: 405-415
Publish at: 2026-03-01

DCNNVA: a deep convolutional neural network for volcanic activity classification using satellite imagery

10.11591/ijaas.v15.i1.pp281-292
Yasir Hussein Shakir , Reem Ali Mutlag , Eshaq Aziz Awadh AL Mandhari , Mohamed Shabbir Abdulnabi
Monitoring and classifying volcanic activity are a critical task for disaster risk reduction and hazard management. Recent discoveries in machine learning and deep learning have proved excellent satellite image classification and volcanic anomaly identification capabilities, yet the majority of existing methods suffer from small datasets, particularly on solitary data modalities or particular cases, merely as examples. In this research work, we put forward develop deep convolutional neural network for volcanic activity (DCNNVA) classification specifically designed for satellite imagery on volcanic activity. We rigorously benchmarked DCNNVA model's strength against a total of eight state-of-the-art transfer learning models: ResNet50, NASNetLarge, DenseNet121, MobileNet, InceptionV3, Xception, VGG19, and VGG16. Comparative experimental results show that proposed DCNNVA framework's overall performance significantly surpasses its competitors with an accuracy of 99.33%, precision of 100%, recall of 98.67%, and F1-score of 99.33%, significantly beating existing state-of-the-art methods. Also, we create a deployable graphical user interface (GUI) system that is capable of real-time monitoring on volcanic activity and generates multi-modal alert processing that can make this research directly applicable for practical use on disaster management as well as in early warning systems. This research contributes a scalable, strong, as well as practical solution towards volcanic hazard identification as well as a baseline system toward developing future multi-modal as well as real-time geohazard tracking system frameworks.
Volume: 15
Issue: 1
Page: 281-292
Publish at: 2026-03-01

Enhancing service reliability in heavy-duty commercial vehicles industry

10.11591/ijaas.v15.i1.pp99-106
Jonny Jonny , Januar Nasution
Reducing breakdown lead time is a critical factor in ensuring customer productivity and sustaining competitiveness in the heavy-duty commercial vehicle (HDCV) industry. This was tackled by applying a methodology called define, measure, analyze, improve, and control (DMAIC), which stands for DMAIC. By deploying it, the breakdown lead time of an Indonesian HDCV company can be minimized. Before the initiative, the lead time was 4 days with 81.54% or 815,400 defects per million opportunities (DPMO) or less than 1 sigma with only 303 parts within target. The reduction target was 2 days as required by its customers, with 40% or 400,000 DPMO or less than 2 sigmas, with 658 parts within target. After following the methodology, the lead time was less than 2 days, meeting customer requirements with 31.2% or 312,000 DPMO, or about 2 sigmas. It shows an improved lead time, which is less than 2 days from 4 days, and a sigma level which is less than 2 sigmas from less than 1 sigma, with 908 parts within target. The study demonstrates how integrating digital applications, remanufactured spare parts, and a centralized command center significantly shortens breakdown handling.
Volume: 15
Issue: 1
Page: 99-106
Publish at: 2026-03-01

Innovative climate information services: a scoping review and bibliometric analysis for climate change decision-making

10.11591/ijaas.v15.i1.pp65-76
Jazimatul Husna , Imilia Ibrahim , Ika Wahyuning Widiarti
This research aims to develop innovative information services to strengthen decision-making in climate change mitigation through a scoping review and bibliometric analysis (ScoRBA). A systematic search of the Scopus database identified 1,214 publications from 2009 to 2023, with 383 meeting inclusion criteria. Using the patterns, advances, gaps, evidence, and recommendations (PAGER) framework, this research provides a transparent synthesis of evidence on climate information services (CIS). The analysis reveals four major thematic clusters: i) emerging technologies and innovations, ii) climate and environmental studies, iii) information systems and decision making, and iv) context awareness and applications. Technologies such as service-oriented architecture (SOA), internet of things (IoT), and cloud computing are key enablers for improving CIS accuracy and efficiency. Evidence shows that these technologies have been successfully applied in agriculture and aquaculture across Vietnam, Bangladesh, and Australia. North African countries have adopted IoT-based water management systems to address water scarcity, while India employs similar technologies to optimize agricultural resources. Integrating local knowledge with scientific data—particularly in Africa, Southeast Asia, and South America—has proven essential for effective adaptation strategies. This research advances theoretical and practical understanding of CIS, offering evidence-based insights to guide the development of adaptive and equitable climate information frameworks.
Volume: 15
Issue: 1
Page: 65-76
Publish at: 2026-03-01

Soft fuzzy partial metric and some results on fixed point theory under soft set

10.11591/ijaas.v15.i1.pp427-436
Rohini R. Gore , Renu P. Pathak
This research paper establishes a new concept of soft fuzzy partial metric spaces, combining soft sets, partial metric spaces, and fuzzy sets to handle uncertainty and imprecision. This paper's primary goal is to use soft fuzzy partial metric spaces to examine various fixed-point theory conclusions. A few fixed-point results are defined under the 𝛹 −contraction mapping on soft fuzzy partial metric space and the soft fuzzy contraction mapping. Also, illustrate the related example of fixed-point theorem. Soft fuzzy partial metric spaces have applications in various fields, including image processing, decision-making analysis.
Volume: 15
Issue: 1
Page: 427-436
Publish at: 2026-03-01

Application of fuzzy logic for the evaluation of student academic performance in biomedical subjects

10.11591/ijaas.v15.i1.pp236-244
Elda Maraj , Anila Peposhi , Aida Bendo
Conventional educational systems primarily use rigid assessment models that narrowly define student achievement through examination scores, categorizing outcomes into success or failure. Fuzzy logic, a mathematical approach derived from set theory, provides a more flexible framework capable of capturing uncertainty and gradations in performance. Initially applied in engineering and artificial intelligence, fuzzy logic has shown significant promise in educational contexts where nuanced evaluation is essential. This study applies a fuzzy logic-based methodology to the evaluation of biomedical course performance at the Sports University of Tirana, Faculty of Rehabilitation Sciences. Data were collected from fifty students enrolled in biomedical subjects and analyzed through both classical examination grading and fuzzy logic evaluation. Comparative analysis revealed that while classical assessment remains constrained by static calculations, fuzzy logic introduces dynamic adaptability. The findings highlight the superiority of fuzzy logic over traditional methods in providing a multidimensional picture of academic achievement. This approach not only refines evaluation accuracy but also supports fairer and more individualized assessment practices. Consequently, fuzzy logic emerges as a powerful tool for modernizing educational evaluation systems, particularly in biomedical disciplines where learning outcomes often extend beyond conventional metrics.
Volume: 15
Issue: 1
Page: 236-244
Publish at: 2026-03-01

Ensemble machine learning based model to estimate irrigation water requirement for wheat crop

10.11591/ijaas.v15.i1.pp142-154
Satendra Kumar Jain , Anil Kumar Gupta
India faces a serious water shortage issue, as its population grows faster than the percentage of fresh water available, with only 4% of the world's fresh water available to 18% of the world's population. Agriculture sector is more water-consuming sector in India. India's irrigation system still faces two significant problems: low irrigation efficiency and a lack of optimization during irrigation. To address these problems, agriculturists ought to be aware of the water requirements for crops beforehand. Innovative fields like machine learning, a branch of artificial intelligence, have a big potential to improve irrigation. Verifying the suitability of the gradient boosting regressor machine learning algorithm-based model for estimating irrigation water requirements (IWR) is the aim of this research. The experiment is conducted in Ludhiana, a city in Central Punjab, India, with a hot, semi-arid climate that features scorching summers and chilly winters. The results demonstrate the remarkably high accuracy rate with coefficient of determination (R2) =0.98 for predicting IWR. The suggested model, which is based on a gradient boosting regression, allows the stakeholders to accurately estimate the amount of water needed for irrigation, the number of irrigation applications for the growing season of wheat crops, and the interval between irrigations.
Volume: 15
Issue: 1
Page: 142-154
Publish at: 2026-03-01

Artificial intelligence-powered image recognition retail checkout systems

10.11591/ijaas.v15.i1.pp187-197
Malyssa Alias , Dhaifina Saidi , Lim Jia Huey , Lee Qing Fang , Durghaashini S. Ragunathan , JosephNg Poh Soon , Phan Koo Yuen , Lim Jit Theam , Wong See Wan
The integration of artificial intelligence (AI) with big data analytics leads to substantial transformations in the retail sector. This research explores the impact of AI-powered image recognition checkout systems on the retail industry, focusing on operational efficiency, customer experience, and resource waste. Employing a mixed-methods approach, this study combines usability testing and data analytics to assess the viability of this technology in attaining automation and accuracy in retail operations. The study focuses on the creation of robust, resource-efficient systems that foster long-term industrial growth. The findings show that AI-powered solutions not only speed the checkout process but also contribute to sustainable infrastructure by reducing resource consumption and increasing energy efficiency. This report offers significant information, like the impact of AI-powered image recognition checkout systems on operational efficiency, customer experience, and the role of AI in promoting sustainable infrastructure for retailers and governments looking to advance the digitalization of the retail industry.
Volume: 15
Issue: 1
Page: 187-197
Publish at: 2026-03-01

Hybrid energy storage systems as a sustainable energy source

10.11591/ijaas.v15.i1.pp219-226
Muhammad Adam , Suwarno Suwarno , Catra Indra Cahyadi
The use of fossil fuel power plants will contribute to emissions and environmental pollution, which has an impact on air and environmental pollution. Applying hybrid energy systems can help reduce the emission footprint and improve the stability of local electricity networks, especially in services with high energy consumption. Hybrid optimization of multiple energy resources (HOMER) is a simulator that simulates using renewable energy with the hybrid renewable energy systems (HRES). The simulation produces a system with the most appropriate combination of photovoltaic (PV), wind power (WP), and converter. The combination of PV-WP produces an economical choice for providing electrical energy in a particular location. The hybrid PV-WP model can save about 40.8% less than the current condition. The investment can be returned in 10.11 years, which is recommended for similar conditions in other areas. This positive impact can provide incentives for policymakers in the implementation of a hybrid system that can neutralize emissions and environmental pollution.
Volume: 15
Issue: 1
Page: 219-226
Publish at: 2026-03-01

Analysis of railway accidents in Nigeria: a decade of insights

10.11591/ijaas.v15.i1.pp19-28
Aliyu Mani Umar , Mohd Khairul Afzan Mohd Lazi , Sitti Asmah Hassan , Hanini Ilyana Che Hashim , Yinggui Zhang , Nura Shehu Aliyu Yaro , Adam Ado Sabari , Surajo Abubakar Wada
This study provides insights into the patterns and dynamics of railway accidents in Nigeria over the past decade. Findings indicate that Nigeria's rail network experiences fewer but more severe accidents than the United States of America (USA) and United Kingdom (UK), with significantly higher fatalities and injuries per million train kilometers 92% and up to 95% more, respectively, in 2023. A top-down approach was employed to establish a risk tree, revealing six railway accident categories recorded over the last decade. The established risk tree could provide a framework for conducting the rail network's comprehensive safety risk assessment. Finally, a root cause analysis of railway intrusion accidents, the most occurring railway accident category in the Nigerian rail network, was conducted. Six immediate and eleven underlying causes (UC) of railway intrusion accidents were identified. About 62% of all intrusion accidents were caused by negligence of road users. Several actionable preventive measures (PM) have been proposed for each identified UC based on best practices in developed rail networks. Infrastructure upgrades and safety awareness campaigns have been identified as the potentially most effective PM for railway intrusion accidents in Nigeria.
Volume: 15
Issue: 1
Page: 19-28
Publish at: 2026-03-01

State evolution approach for the axion conversion probability in magnetosphere of a neutron star

10.11591/ijaas.v15.i1.pp355-371
Bilal Ahmad , Shehreyar Ali
Neutron stars (NS), with their extreme gravitational and magnetic fields, provide an exceptional astrophysical laboratory for studying axion dark matter (DM). Through the Primakoff effect, axions can convert into photons within the magnetospheres of NS, a process that may produce observable radio and X-ray signals. In this work, we investigate axion-photon conversion using a novel, time-dependent state evolution formalism, moving beyond the commonly used stationary-path approximations. We derive a generic analytical expression for the conversion probability and calculate the associated radiated power. Our analysis demonstrates that this approach allows NS to strongly constrain the axion-photon coupling constant, reaching sensitivities of gaγγ ≃ 10−14 −10−15 GeV−1 for axion masses of ma ≃ 10−3 −10−10 eV. These results establish a new pathway to constrain gaγ via NS observations. Future campaigns using powerful observatories like the James Webb Space Telescope (JWST), Green Bank Telescope (GBT), and More Karoo Array Telescope (MeerKAT) array will be ideally suited to probe the distinct spectral signatures predicted by our model across multiple frequency domains.
Volume: 15
Issue: 1
Page: 355-371
Publish at: 2026-03-01

Google Play review analysis on Sharia pawnshop applications in Indonesia

10.11591/ijaas.v15.i1.pp86-98
Azhar Alam , Adityo Wiwit Kurniawan , Muhammad Sholahuddin
Digital transformation opens opportunities for Sharia pawnshops to develop innovative application-based services following Sharia principles. This study analyzes the perception and experience of users of the Sharia pawnshop application on the Google Play Store using a netnography approach. It collects and analyzes 395 user reviews between June and December 2024, which consist of 219 positive reviews and 176 negative reviews. The analysis shows that 59.82% of users gave positive reviews regarding satisfaction with using the application, especially regarding transaction security and ease of use. As many as 17.35% of positive reviews emphasized the benefits of the application in transforming Islamic financial services. The main challenges identified included update system problems (35.23%), technical and server problems (30.11%), and registration complexity (5.68%). There was also a discrepancy between numerical ratings and review content. Important concerns include service problems (9.66%) and limited choice of Islamic banks (5.11%). This research provides important insights for the development of digital Islamic finance applications in the future, especially in the aspects of improving technological infrastructure, simplifying processes, and improving the quality of customer service. The results of this study contribute to a better understanding of user needs in the context of the digitization of Islamic financial services in Indonesia.
Volume: 15
Issue: 1
Page: 86-98
Publish at: 2026-03-01

A bibliometric review of critical chain project management in construction

10.11591/ijaas.v15.i1.pp272-280
Dhiraj S. Bachwani , MohammedShakil S. Malek , Deep Shaileshkumar Upadhyaya , Neetu Yadav
This study offers an extensive bibliometric analysis of critical chain project management (CCPM) research over the past twenty years, seeking to elucidate the discipline’s developmental trajectory and pinpoint emerging research frontiers. A comprehensive review of the literature revealed fundamental principles of CCPM, highlighting essential components such as buffer management strategies and resource-constrained scheduling methodologies. This initial analysis established the theoretical framework for the quantitative study and facilitated the identification of suitable metrics to integrate both foundational theories and contemporary advancements in CCPM scholarship. The study examined approximately 1,800 academic publications, including journal articles, conference proceedings, review papers, and book chapters published from 2000-2022, obtained from the Scopus database. The analytical framework encompassed various bibliometric dimensions, including performance metrics, relationship indicators, conceptual frameworks, publication characteristics, and VOSviewer network analysis, as essential elements of the data examination process. The developed framework has two main goals: it helps researchers find important publications, potential collaborators, and new areas of research, and it gives practitioners a structured place to store information about how to use CCPM methods in complicated projects with few resources.
Volume: 15
Issue: 1
Page: 272-280
Publish at: 2026-03-01
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