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30,938 Article Results

Integration of phenomenon-based and play-based learning in primary science

10.11591/ijere.v15i4.39040
Prapatchit Techa , Sakda Swathanan , Wichaya Pewkam , Natad Assapaporn
Primary school students in Thailand continue to struggle with science process skills and creative thinking, a challenge underscored by Thailand’s lowest Programme for International Student Assessment (PISA) science scores in 20 years. Traditional instruction emphasizing content memorization while neglecting real-world application has been identified as a major contributing factor. Addressing this issue is critical, as both competencies are foundational for 21st-century learning. This study examined the effects of an integrated phenomenon-based learning and play-based learning (PhBL-PBL) innovation on Grade 2 students’ science process skills and creative thinking skills. A one-group posttest-only design was adopted with 41 students in an authentic Thai primary classroom. Data were collected using validated instruments and analyzed through descriptive statistics and one-sample t-tests against predefined performance criteria. Results showed that 92.68% of students achieved creative thinking skills at a good level or above, and 85.37% reached the same threshold for science process skills, with mean scores for both domains significantly exceeding the criterion level (p
Volume: 15
Issue: 4
Page: 3099-3110
Publish at: 2026-08-01

A model for flexible learning in graduate teacher education programs

10.11591/ijere.v15i4.39252
Marilyn U. Balagtas , Adonis P. David , Erminda C. Fortes , Arceli M. Amarles , Alvin B. Barcelona , Marla C. Pampango , Marjorie Naquita
This study aimed to develop a model for flexible learning (FL) appropriate to graduate teacher education programs (GTEP) based on the different practices of the graduate faculty and students in a teacher education institution (TEI) before and during the COVID-19 pandemic. A multimethods approach was employed, utilizing survey questionnaires, semi-structured interviews, and focus group discussions (FGD). Data were collected from 215 graduate students and 43 graduate faculty members who were selected through convenience sampling. The study resulted in the development of a model of FL for GTEP (MFL-GTEP), reflected in an outcome-based syllabus that highlights 10 areas of FL, all beginning with P: purpose, process, pedagogy, platform, people, place, pace, performance, product, and policy of learning. The MFL-GTEP promotes self-agency, self-regulation, and self-determination among education professionals pursuing GTEP. The challenges that graduate faculty and students experience in the implementation of FL are addressed in the (MFL-GTEP) to make the model more relevant, inclusive, and sustainable in a graduate teacher education program.
Volume: 15
Issue: 4
Page: 3193-3203
Publish at: 2026-08-01

Physics-based modeling of cobalt-doped nickel-zinc on-chip ferrite inductors

10.11591/ijece.v16i4.pp1805-1816
Bambang Mulyo Raharjo , Dicky Rezky Munazat , Sudirman Rohadi
The miniaturization of integrated voltage regulators (IVRs) for multi-core processors is fundamentally bottlenecked by the high-frequency magnetic losses of conventional inductor cores. This study presents a rigorous computational framework to optimize Cobalt-doped Nickel-Zinc ferrite (Ni_(1-x) Zn_0.4 Co_x Fe_2 O_4) for 10 MHz on-chip power delivery. Utilizing Landau-Lifshitz-Gilbert (LLG) relaxation dynamics and Maxwell-Wagner interfacial polarization, the complex electromagnetic dispersion was modeled and quantitatively validated against recent empirical literature. Furthermore, high-temperature power loss density was bounded using the trust region reflective (TRF) numerical curve fitting algorithm to validate an anisotropy-compensated "thermal valley" at 80 °C. A multi-objective sensitivity analysis identified a high-efficiency Cobalt "sweet spot" at a concentration of x=0.04. This specific formulation optimally stiffens domain walls, safely shifting the resonance frequency to 40 MHz and maximizing the quality factor (Q) at the 10 MHz operational target. When applied to a simulated 3.5 V to 1.0 V DC-DC buck converter, the optimized x=0.04 core demonstrated its adequacy for on-chip applications by maintaining >90% power efficiency under a rigorous 2.0 A load. These predictive results mathematically prove that precision Cobalt doping is a highly viable strategy for suppressing parasitic losses in next-generation 3D-IC power delivery networks.
Volume: 16
Issue: 4
Page: 1805-1816
Publish at: 2026-08-01

From climate time series to planting windows in chili (Capsicum frutescens): a SARIMA–SVM–XGBoost framework with balanced-accuracy thresholding

10.11591/ijece.v16i4.pp2210-2219
Efrans Christian , Nova Noor Kamala Sari , Ressa Priskila , Septian Geges
This study proposes a spatio-temporal decision-support framework that integrates Seasonal Autoregressive Integrated Moving Average (SARIMA), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) to derive adaptive planting windows for chili (Capsicum frutescens) at the sub-district level. The framework addresses key challenges in climate-sensitive agriculture, including spatial data leakage and class imbalance, by employing Leave-One-Group-Out (LOGO) cross-validation and Balanced Accuracy–based threshold optimization. The proposed system transforms heterogeneous environmental data into actionable recommendations by combining climate forecasting, land suitability assessment, and yield prediction within a unified pipeline. Experimental results indicate that the framework effectively captures seasonal climate dynamics and produces consistent planting recommendations aligned with agronomic conditions, enabling multiple planting cycles per year. The primary contribution of this work lies in a transparent and generalizable integration of statistical and machine learning models into a practical decision-support framework. The proposed approach bridges predictive modeling and real-world agricultural decision-making and can be extended to other crops and regions for climate-adaptive agricultural planning.
Volume: 16
Issue: 4
Page: 2210-2219
Publish at: 2026-08-01

Enhancement of YOLOv8 for object detection in adverse weather conditions using generative adversarial network

10.11591/ijece.v16i4.pp2230-2246
Talifhani Calvin Tshipota , Chunling Tu , Mukatshung Claude Nawej , Sempe Thom Leholo
Detecting objects in bad weather like rain, fog, snow, or low light is still difficult because visibility drops, noise increases, and contrast gets worse, all of which hurt detection accuracy. Most current methods either improve detector designs or use image preprocessing on their own. They usually focus on just one type of weather and do not use a common way to evaluate results. This paper introduces a YOLOv8 framework improved with a generative adversarial network (GAN) for image enhancement before detection. Instead of just making images look better, the GAN is trained to help the object detector work better in tough conditions, so it can extract features more effectively when images are degraded. The model was tested on datasets with different weather conditions using standard metrics like Precision, Recall, F1-score, and mean average precision (mAP). Results show that this method consistently improves performance, with up to a 6.5% increase in mAP@0.5 over YOLOv8-STE and 9.2% over IA-YOLO, especially in foggy and low-light situations. These results show that adding GAN-based preprocessing to YOLOv8 makes detection more reliable and still keeps the process fast. This framework offers a practical and scalable solution for real-world uses like self-driving cars, smart transportation, and surveillance.
Volume: 16
Issue: 4
Page: 2230-2246
Publish at: 2026-08-01

Mapping influential CLIL research in English language teaching: a bibliometric SWOT analysis

10.11591/ijere.v15i4.38986
Lim Seong Pek , Fatin Syamilah Che Yob , Azie Azlina Azmi , Desmond Sandum , Gilbert C. Magulod Jr. , Choiril Anwar
Content and language integrated learning (CLIL) has gained increasing attention in English language teaching (ELT); however, existing reviews often describe trends without critically examining both research impact and developmental challenges. This study addresses this gap by investigating the question: what directions and challenges characterize influential CLIL research in ELT? An integrative approach combining document-level bibliometric performance analysis and strengths, weaknesses, opportunities and threats (SWOT) analysis was employed. Using the Scopus database, the ten most-cited CLIL-related studies published between 2021 and 2025 were identified and systematically analyzed. The findings indicate that influential CLIL scholarship centers on pedagogical scaffolding, teacher cognition, learner motivation, and technology-enhanced learning environments. At the same time, recurring challenges include role ambiguity between content and language teachers, policy-practice misalignment in English-medium instruction (EMI) contexts, and limited longitudinal evidence of language outcomes. The study concludes that while CLIL research is increasingly shaped by digital innovation, sustainable implementation depends on pedagogical clarity, teacher preparation, and institutional alignment. By integrating bibliometric evidence with critical qualitative synthesis, this study provides theoretical and practical insights to guide future research, teacher education, and policy development aligned with sustainable development goal 4 (quality education).
Volume: 15
Issue: 4
Page: 3015-3024
Publish at: 2026-08-01

Remaining useful life estimation for predictive battery maintenance with improved recurrent singular spectrum analysis algorithm

10.11591/ijece.v16i4.pp1817-1831
Chutipongse Boonyakitmaitree , Suchada Sitjongsataporn
As the global electric vehicle (EV) battery market is projected to reach a valuation of over USD 100 billion by the end of 2026, the demand for sophisticated battery management systems (BMS) has become more critical than ever. Accurate remaining useful life (RUL) prediction is essential for ensuring vehicle safety, optimizing maintenance, and evaluating retired batteries for second-life applications. However, existing prognostic methods often struggle to balance computational efficiency with predictive accuracy, especially during the early stages of battery usage. This research proposes combined weighted similarity-based and recurrent singular spectrum analysis (CWS-RSSA), a hybrid forecasting framework that integrates RSSA with a similarity-based approach through a weighted logistic switching mechanism. The algorithm is designed to be computationally lightweight, making it suitable for resource-constrained BMS hardware. The proposed method was validated using NASA and a large-scale dataset from MIT-Stanford consisting of 124 lithium-ion cells. Experimental results demonstrate that CWS-RSSA is capable of early-stage prediction with a relative error of 19.8%, whereas existing methods are unable to provide predictions. In later stages, once sufficient data becomes available, the algorithm achieves near-perfect accuracy with a negligible relative error on the NASA dataset and an average relative error of only 0.14% across the 124 MIT-Stanford batteries. Furthermore, the algorithm demonstrates robust performance in handling capacity regeneration phenomena. These findings suggest that CWS-RSSA represents a scalable and practical advancement for battery health management, supporting the transition toward a sustainable circular energy economy and providing a reliable foundation for second-life battery certification.
Volume: 16
Issue: 4
Page: 1817-1831
Publish at: 2026-08-01

Intravenous immunoglobulin resistance prediction in Kawasaki disease using oversampled transformer embeddings

10.11591/ijai.v15.i4.pp3944-3954
Namitha Thattarassery Nanappan , Raghavendra Srinivasaiah , Vinith Rejathalal
Kawasaki disease (KD) is a leading cause of acquired heart disease in children under five. Although intravenous immunoglobulin (IVIG) treatment is usually effective, 10–20% of cases are resistant and at higher risk for coronary complications. Early prediction of IVIG resistance is critical but difficult due to the rarity of KD and imbalanced clinical data. To address this, we propose a novel technique called sentence transformer embeddings with synthetic minority over-sampling technique (SMOTE) oversampling (STESO), which leverages the complementary strengths of transformer-based representation learning and synthetic oversampling. Pretrained models such as paraphrase-MiniLM-L3-v2 are used to convert tabular clinical data into dense text-based embeddings, capturing deeper semantic relationships across features. By coupling these rich embeddings with SMOTE, we balance class distributions directly in the semantic space, enabling traditional machine learning (ML) models to more effectively detect minority (resistant) cases. This synergy yielded substantial improvements in sensitivity and F1-score, with random forest (RF) combined with STESO (RF-STESO) achieving the highest overall performance. Among the models evaluated, our proposed model attained best result as accuracy of 0.85, a sensitivity of 0.81, a specificity of 0.89, and an F1-score of 0.85. Our results underscore that the joint use of transformer embeddings and oversampling is more effective than either approach in isolation, offering a promising pathway for rare disease prediction tasks such as IVIG resistance prediction in KD.
Volume: 15
Issue: 4
Page: 3944-3954
Publish at: 2026-08-01

Efficient deep learning for automated corneal ulcer severity classification from fluorescein images

10.11591/ijai.v15.i4.pp3603-3613
Rodiah Rodiah , Indah Sinthya Permata Sari , Matrissya Hermita , Sarifuddin Madenda , Diana Tri Susetianingtias
Corneal ulcers can cause permanent vision loss if not diagnosed and managed promptly, particularly in settings with limited access to ophthalmology services. This study aims to develop an automated deep learning approach for classifying corneal ulcer severity from fluorescein slit-lamp images. An EfficientNetV2-S–based model is employed, incorporating corneal area masking to suppress non-relevant regions and class distribution–based augmentation to address data imbalance. To improve evaluation reliability, a leakage-aware data splitting strategy is applied before and after augmentation. Experimental results show that the proposed approach achieves a maximum validation accuracy of 95.93% under non-leakage conditions for the category classification scenario, while maintaining high training efficiency. These results demonstrate that the proposed method provides a robust and efficient solution for automated corneal ulcer severity assessment and has the potential to support clinical decision-making in ophthalmic practice.
Volume: 15
Issue: 4
Page: 3603-3613
Publish at: 2026-08-01

An empirical comparison of clustering approaches for recency, frequency, and monetary customer segmentation

10.11591/ijai.v15.i4.pp3402-3410
Upekkha Lau , Meditya Wasesa
This study evaluates effectiveness of three clustering techniques—k-means, hierarchical clustering, and density-based spatial clustering of applications with noise (DBSCAN)—applied to the recency-frequency-monetary (RFM) model for customer segmentation in the retail sector. Using sales transaction data from a distributor of computer accessories and printing products. The results show that k-means achieved the best clustering validation scores and effectively identified high-value customers, hierarchical clustering generated less meaningful groupings than k-means, and DBSCAN misclassified key customers as noise. These findings highlight k-means as the most suitable technique for RFM-based segmentation in this retail business context. The study offers practical insights for retail and distribution businesses aiming to adopt data-driven customer strategies and suggests future research to enhance segmentation robustness and refine the RFM framework.
Volume: 15
Issue: 4
Page: 3402-3410
Publish at: 2026-08-01

The role of big data in precision medicine and healthcare monitoring using the MapReduce framework

10.11591/ijai.v15.i4.pp3852-3864
Meenakshi Sankarasubramanian , Meena Chavan , Govindan Manoharan Karthik , Jhansi Pandiri , Arumalla Nagaraju , Idimadakala Madhavilatha
Data analytics has become a cornerstone of precision medicine by enabling doctors and scientists to extract meaningful insights from vast, complex data sets. Most healthcare data are high-dimensional data that not only require longer computational time but also affect the accuracy of analysis. In order to overcome these issues, the MapReduce based big data healthcare monitoring framework is proposed. The proposed work comprises preprocessing, the MapReduce framework, and data classification. The preprocessing can be done using improved min-max normalization, and the big data can be handled using the improved support vector machine (SVM)-recursive feature elimination (RFE) method. Finally, the classification can be done using a deep Q-network (DQN). The performance of the proposed method is analyzed in terms of accuracy, precision, F-measure, and Matthew's correlation coefficient (MCC).
Volume: 15
Issue: 4
Page: 3852-3864
Publish at: 2026-08-01

A novel embedded approach to face recognition using multi-threaded controller based on weightless neural network

10.11591/ijai.v15.i4.pp3903-3918
Ahmad Zarkasi , Hadipurnawan Satria , Anggina Primanita , Deris Stiawan
This research presents a high efficiency embedded face recognition system based on the weightless neural network-face recognition algorithm (WNN-FRA) integrated with a multi-thread controller to enhance execution time and recognition accuracy under limited hardware resources. The system implements a center-scan feature alignment model to address resolution discrepancies between reference and input facial images. The multi-threaded architecture divides processing into three concurrent threads front, left, and right facial orientations each handling approximately 20 facial patterns. Experimental evaluation on a dataset of 60 facial images demonstrated a maximum recognition accuracy of 96.83% and an average execution time ranging from 16 to 34 milliseconds per dataset, confirming real-time performance. Comparative analysis shows that the multi-threaded approach reduced the execution time by over 67% compared to single-thread processing 0.09 second vs. 0.271 second, while maintaining balanced workload distribution across threads. Memory analysis revealed that the entire system required only 24 KB from the available 512 KB flash capacity, indicating efficient resource utilization. The results confirm that integrating WNN-FRA with multi-threading provides a robust, low-cost, and scalable solution for real-time facial pattern recognition in embedded environments.
Volume: 15
Issue: 4
Page: 3903-3918
Publish at: 2026-08-01

Android-based tomato leaf disease classification using a lightweight MobileNetV2 convolutional neural network

10.11591/ijai.v15.i4.pp3318-3325
Andi Riansyah , Irfan Eka Mahdy , Mochamad Abdul Basir , Noorminshah A. Iahad
Early screening of tomato leaf diseases is important because foliar symptoms can reduce plant vigor and delay appropriate crop management. This study develops an Android-based tomato leaf disease classification system using MobileNetV2 as a lightweight convolutional neural network (CNN) architecture. The contribution of this work is the integration of model training, independent testing, and on-device Android deployment that supports camera and gallery inputs without relying on server-side computation. The dataset consisted of 1,200 balanced tomato leaf images from five classes: bacterial spot, late blight, target spot, tomato yellow leaf curl virus, and healthy leaf. Images were resized, normalized, augmented for training, and divided into training, validation, and independent testing subsets. The model obtained 94.12% training accuracy, 93.00% validation accuracy, and 89.00% independent test accuracy. The confusion matrix showed that tomato yellow leaf curl virus was classified without error, whereas bacterial spot, late blight, target spot, and healthy leaves produced several misclassifications because of similar lesion and discoloration patterns. The results show that MobileNetV2 is suitable for lightweight mobile disease screening, although larger field datasets, cross-validation, model comparison, and explainability analysis are still needed for broader deployment.
Volume: 15
Issue: 4
Page: 3318-3325
Publish at: 2026-08-01

Performance of majority voting transfer learning deep learning monkeypox disease detection

10.11591/ijai.v15.i4.pp3782-3791
Nur Nafiiyah , Muhammad Nurul Huda
Global health concerns have been raised by the advent of monkeypox following the COVID-19 epidemic, highlighting the need for reliable automated systems to support early skin disease screening. This study proposes a monkeypox skin disease classification framework using a majority voting ensemble based on transfer learning. The ensemble combines predictions from multiple pretrained convolutional neural networks (CNNs) to improve classification robustness. A publicly accessible dataset comprising four classes: monkeypox, chickenpox, measles, and normal skin was used for the experiments. The results indicate that while a single ResNet50 model achieved the highest overall accuracy (99.15%), the majority voting approach yielded higher precision than several individual models, demonstrating improved reliability in distinguishing monkeypox cases. These findings suggest that ensemble-based majority voting can enhance the robustness of monkeypox skin disease classification and may support computer-aided screening systems.
Volume: 15
Issue: 4
Page: 3782-3791
Publish at: 2026-08-01

Hybridization of hybrid-ARIMA-EM and XGBoost for enhanced price predictive modeling

10.11591/ijai.v15.i4.pp3131-3143
Isam Ahmed M. Yaqoob , Khairul Azhar Kasmiran , Teh Noranis Mohd Aris , Nor Azura Husin , Mohd Yunus Sharum
Managing finance entails the art and science of distributing available and potential funds among various competing needs. Government expenditures fund programs that provide a wide range of services to different population segments. As a result, the demand for enhanced and additional services often surpasses the government's financial capacity. Firstly, the price forecasting procedures for the extreme gradient boosting (XGBoost), gated recurrent unit (GRU), and hybrid-ARIMA-EM models will be summarized. Secondly, the accuracy of the models will be assessed on two real datasets collected from Kaggle (Crude_Oil_Price and KL_apartment). This study then proposes combining the hybrid-ARIMA-EM model with XGBoost to enhance the price forecasting performance in terms of time series analysis. Experimental results show that the suggested combination outperforms other selected models in price forecasting accuracy.
Volume: 15
Issue: 4
Page: 3131-3143
Publish at: 2026-08-01
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