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29,758 Article Results

Markov-switching and noise-to-signal ratio approach for early detection of currency crises

10.11591/ijaas.v15.i1.pp42-54
Sugiyanto Sugiyanto , Muhammad Bayu Nirwana , Isnandar Slamet , Etik Zukhronah , Syifa’ Salsabila Gita Parahita
Economic instability can easily lead to a currency crisis. Therefore, observing a number of crisis indicators is crucial for building an early warning system (EWS). However, selecting the indicators most responsive to the crisis is the best choice. For this purpose, the noise-to-signal ratio (NSR) method was used. Monthly data from 1990-1925 were used in the autoregressive moving average (ARMA), generalized autoregressive moving average with generalized autoregressive conditional heteroscedasticity (GARMACH), and Markov-switching (MS)-GARMACH hybrid models to explain the crisis. Model interpretation indicates that there will be no crisis from May 2025-April 2026.
Volume: 15
Issue: 1
Page: 42-54
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

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

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

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

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

Temperature and pH effects on bioethanol production from wild cassava (Manihot glaziovii Muell. Arg) using simultaneous co-fermentation

10.11591/ijaas.v15.i1.pp227-235
Ida Ayu Pridari Tantri , Ida Bagus Wayan Gunam , Anak Agung Made Dewi Anggreni , I Gede Arya Sujana
Bioethanol is a clean alternative energy source, with wild cassava (Manihot glaziovii Muell. Arg) as a potential feedstock. Fermentation converts glucose from hydrolysis into ethanol. This study examines the effects of pH and fermentation temperature on bioethanol characteristics using a simultaneous saccharification and co-fermentation (SSCF) technique. A factorial randomized block design (RBD) was used with two factors: pH (4.5, 5.0, and 5.5) and fermentation temperature (30, 32.5, and 35 °C). Data were analyzed using variance and Duncan’s test. Results showed that pH and temperature significantly affected pH value, total soluble solids, reducing sugar, and ethanol content. The optimal conditions for bioethanol production were pH 4.5 and temperature 32.5 °C, yielding a pH of 3.55±0.07, total soluble solids of 9.3±0.57 °Brix, reducing sugar of 3.038±0.10 mg/mL, and ethanol content of 3.48±0.37 (%w/v). Based on the results of this study, wild cassava can be utilized as bioethanol by considering the effect of fermentation conditions.
Volume: 15
Issue: 1
Page: 227-235
Publish at: 2026-03-01

Digital platforms and cloud computing for smart cities: a review

10.11591/ijict.v15i1.pp30-38
William Christopher Immanuel , Anitha Juliette Albert , Limsa Joshi Jerald Jobitham , Roselene Rebecca Selvaraj , Benita Sharon Ruban , Bennet Vini Robin , Andria Morais Allen
The rapid urbanization of the modern world initiated the emergence of digital cities, where advanced technologies converge to optimize urban living and address the limitations of a rapidly growing population. Central to this transformation are digital platforms and cloud computing. These interconnected technologies aid in shaping the future of urban landscapes, fostering sustainability, efficiency, and improved quality of life. Digital platforms serve as the backbone of smart cities, enabling seamless integration and management of various urban services and systems. One significant application of digital platforms in smart cities is the implementation of intelligent transportation systems (ITS). By integrating real-time traffic data, public transit information, and ride-sharing services, these platforms facilitate efficient transportation management, reduce congestion, and decrease carbon emissions. Cloud computing serves as a key enabler for managing the massive data flows generated by smart city infrastructures. The scalability and flexibility offered by cloud-based solutions allow cities to manage their resources efficiently and access computing power on demand without the need for extensive physical infrastructure. Cloud computing enhances smart city development by enabling collaborative data access and interaction among diverse stakeholders, from government agencies to private firms and residents.
Volume: 15
Issue: 1
Page: 30-38
Publish at: 2026-03-01

Lightweight deep learning approach for retinal OCT image classification: A CNN with hybrid pooling and optimized learning

10.11591/ijict.v15i1.pp414-427
Parth R. Dave , Nikunj H. Domadiya
Optical coherence tomography (OCT) is a non-invasive technique through which a retina specialist can see the structure behind the eye. This technol ogy offers a key role to identify various abnormalities in the retina: Drusen, diabetic macular edema (DME) and choroidal neovascularization (CNV). However, manual analysis of OCT scans can be time-consuming and prone to variability among clinicians. To address this challenge, we present a lightweight and explainable deep learning-based approach for automatic classification of retinal OCT images. The primary goal of this research is a model that delivers high diagnostic accuracy. A computer-aided suggestive method can help retinal doctors automatically classify the anomalies with more confidence and precision. In this paper, we proposed a novel approach based on deep learning: a six-layer convolutional neural network (CNN) integrated with hybrid pooling for effective feature extraction. Data augmentation and exponential learning rate is implemented to handle data imbalance between classes and for stabilized learning consecutively. Our proposed approach achieved 98.75% of accuracy while testing on the dataset. To further enhance the interpretability of the model, we also integrate explainable AI (XAI) using class activation mapping (CAM) to visualize the critical regions in the retina that contribute to the classification decisions.
Volume: 15
Issue: 1
Page: 414-427
Publish at: 2026-03-01

Renewable energy optimization for sustainable power generation

10.11591/ijict.v15i1.pp365-373
Debani Prasad Mishra , Sarita Samal , Rohit Kumar , Arun Kumar Sahoo , Surender Reddy Salkuti
To improve sustainability in power generation, this study presents a thorough data-driven method for maximizing renewable energy sources. It employs measures like capacity utilization factor (CUF) and efficiency to evaluate the performance of solar and wind energy using historical weather and energy-generating data. The study offers practical suggestions for improving renewable energy systems, such as weather-energy correlation analysis and machine learning-based forecasting models. In addition, a comparative analysis is carried out to ascertain which energy source is better, and useful real-world data is provided, including a summary of all India’s total renewable energy generation (excluding large hydro) for June 2023 and a performance comparison year over year. A useful, data-driven approach for enhancing renewable energy is provided by this work, which advances the topic of sustainable energy.
Volume: 15
Issue: 1
Page: 365-373
Publish at: 2026-03-01

A high linearity low noise amplifier with modified differential inductor for bluetooth profiles

10.11591/ijict.v15i1.pp323-331
Ghattamaneni Usharani , Sourirajan Varadarajan
In today’s rapidly evolving communication landscape, electronic devices rely heavily on high-performance components to ensure seamless connectivity. A low-noise amplifier (LNA) is a critical front-end element in any receiver chain, where its performance significantly influences the overall system efficiency. As integrated circuits continue to shrink with advancements in technology, challenges such as linearity degradation have become increasingly prominent. This work presents a modified derivative (MD) narrowband common source low-noise amplifier (CSLNA) designed using 0.13 µm CMOS technology, offering improved linearity and frequency characteristics. The proposed design adopts a hybrid architecture, combining a folded cascode gain stage with a common-gate configuration. An optimized modified differential inductor is employed at the input for effective impedance matching and reduced noise figure (NF). The implemented LNA achieves a gain of 25.81 dB, an input return loss of –24.86 dB, and maintains a low NF of 0.3 dB at an operating frequency of 2.4 GHz. Furthermore, the linearity metrics-third-order input intercept point (IIP3) and 1 dB compression pointare significantly improved to –16.70 dBm and –21.89 dBm, respectively. These results highlight the LNA's suitability for Bluetooth and other shortrange wireless communication applications.
Volume: 15
Issue: 1
Page: 323-331
Publish at: 2026-03-01

A survey on fronthaul signaling of user-centric cell-free massive MIMO networks

10.11591/ijict.v15i1.pp302-312
Syed Tariq Ali , Anamika Singh
The mandate for high data rates in mobile communication is increasing and will continue to do so in the future. Although the latest network technologies can meet this demand, they result in more-dense networks. Networks like ultra-dense networks and massive multiple-input multiple-output provide very high data rates, but they cannot meet the future demand. The main issue with existing networks is inter-cell interference and variations in quality of service esp. at the cell edges, leading to research on new network architectures that offer intelligent coordination and collaboration capabilities are being researched, like user-centric cell-free (UC-CF) massive-multipleinput-multiple-output (mMIMO). This network combines the best of ultradense networks and mMIMO and eliminates cell edge problems. It is served by access points that cooperate and coordinate with each other. This paper reviews the challenges and opportunities in physical layer parameterfronthaul signaling for UC-CF mMIMO. We discuss the basics of the network, the importance of fronthaul signaling, and propose various approaches in the literature to address challenges and identify research gaps and provide future directions. Our aims to provide a comprehensive overview of the current state of fronthaul signaling and highlight the key issues that need to be addressed to realize its full potential.
Volume: 15
Issue: 1
Page: 302-312
Publish at: 2026-03-01

A unified intelligent AI platform for resolving citizens' queries related to beneficiary service using AI -Powered chatbots a practical apparoach

10.11591/ijict.v15i1.pp267-275
Parveen Mehta , Shweta Bansal
The daily many rural citizens visit government offices to inquire about beneficiary services that support poor and illiterate citizens. However, without proper knowledge, many eligible citizens fail to benefit from these services. In the artificial intelligence (AI) era, AI-powered chatbots, such as AI agents, can provide valuable support to the villagers and provide them with complete information at their door step. In this paper, a proposed framework, using a chatbot, to reduce the communication gap between citizens and government officials to improve service delivery performance. This chatbot is developed by using a built large language model, python libraries, fast API, and mongodb data base. Our findings demonstrate the challenges of imbalanced data and suggest improvements for future implementations. The system enhances service delivery by automating eligibility checks and reducing office visit frequency by up to 60%.
Volume: 15
Issue: 1
Page: 267-275
Publish at: 2026-03-01

Towards efficient fog computing in smart cities: balancing energy consumption and delay

10.11591/ijict.v15i1.pp332-342
Ida Syafiza Md Isa , Nur latif Azyze Mohd Shaari Azyze , Haslinah Mohd Nasir , Vigneswara Rao Gannapathy , Ashwini Jayadevan Naidu
In this work, we propose fog-based energy-delay optimization (F-EDO) approach and benchmark its performance against the cloud-based energydelay optimization (C-EDO) method, focusing on energy consumption and delay. Unlike previous studies that optimize energy or delay separately, FEDO minimizes both metrics simultaneously, achieving up to 52.2% energy savings with near-zero delay. Additionally, increasing the number of users also leads to energy savings. This is due to the optimized placement of fog servers at the access layer which reduces network energy consumption compared to C-EDO. F-EDO also significantly reduces delay, with negligible delay compared to C-EDO due to fog servers are placed closer to the users which minimized the transmission distances. Besides, the results also show that the energy saving in F-EDO compared to the C-EDO increased as the processing capacity of the processing server increased while maintaining its minimal delay. Overall, F-EDO proves to be a more energyefficient and lower-delay solution for IoT networks, offering a better alternative to cloud-based offloading.
Volume: 15
Issue: 1
Page: 332-342
Publish at: 2026-03-01

Development of machine learning techniques for automatic modulation classification and performance analysis under AWGN and fading channels

10.11591/ijict.v15i1.pp287-301
P. G. Varna Kumar Reddy , M. Meena
Automatic modulation classification (AMC) is essential in modern wireless communication for optimizing spectrum usage and adaptive signal processing. This study explores the use of various machine learning (ML) methods for AMC, focusing on their performance in additive white Gaussian noise (AWGN) and fading channels. This study evaluates of ML classifiers such as support vector machines (SVM), K-nearest neighbors (KNN), decision trees (DT), and ensemble methods with a dataset spanning signalto-noise ratios (SNRs) from -30 dB to +30 dB. Higher-order statistical features including moments and cumulants are used to train the classifiers for AMC. Performance is measured in terms of classification accuracy and computational efficiency across different SNR levels. The findings show that linear SVM, fine KNN, and fine trees consistently achieved high classification accuracy, even at low SNRs. From the analysis, it is observed that linear SVM and fine KNN achieve over 96% accuracy at 0 dB SNR. These classifiers demonstrate significant robustness, maintaining performance in challenging noise conditions. The research highlights the promise of ML techniques in improving AMC, providing a detailed comparison of classifiers and their strengths.
Volume: 15
Issue: 1
Page: 287-301
Publish at: 2026-03-01
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