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Hybrid deep learning approach for Indonesian hoax detection: a comparative evaluation with IndoBERT

10.11591/ijaas.v15.i1.pp322-332
Siti Mujilahwati , Moh. Rosidi Zamroni , Miftahus Sholihin
The spread of hoaxes in Indonesia has escalated significantly, with over 12,547 cases recorded between 2018 and 2023. Low public literacy and uncontrolled information flow contribute to the rapid dissemination of false content that fuels disinformation and social unrest. Previous studies have utilized artificial intelligence (AI) approaches such as Indonesia bidirectional encoder representations from Transformers (IndoBERT) and deep learning models like long short-term memory (LSTM), bidirectional LSTM (BiLSTM), convolutional neural network (CNN), and Transformer-based methods. However, most relied on a single modeling paradigm and did not address the trade-offs between classification performance and computational efficiency. This study proposes a hybrid architecture that integrates IndoBERT with bidirectional gated recurrent unit (BiGRU) and BiLSTM to enhance Indonesian hoax detection. Using 4,312 news articles and 10-fold cross-validation, we compare the performance of IndoBERT–BiGRU, IndoBERT–BiLSTM, and the proposed hybrid IndoBERT–BiGRU BiLSTM model. Evaluation metrics include accuracy, precision, recall, F1 score, and training time. The hybrid model achieved the best performance with 98.73% accuracy, 99.01% recall, 98.04% precision, and 98.98% F1 score, while also reducing training time compared to single models. These findings demonstrate that combining BiGRU and BiLSTM within the IndoBERT framework effectively balances performance and efficiency, making it a robust solution for Indonesian text classification.
Volume: 15
Issue: 1
Page: 322-332
Publish at: 2026-03-01

Adaptive sugarcane monitoring in Mojokerto using a hybrid powered IoT multi-sensor system and machine learning

10.11591/ijaas.v15.i1.pp384-395
Sekar Sari , Oktavia Citra Resmi Rachmawati , Tole Sutikno
This study develops a hybrid-powered IoT multi-sensor system integrated with machine learning for sugarcane monitoring in Mojokerto. Four sensors—soil moisture, pH, LM35 temperature, and LDR light—are connected to an Arduino UNO R4 WiFi microcontroller. A hybrid power supply (mains electricity and solar panels) and dual data storage (real-time transmission to Google Sheets and local SD backup) ensure resilience and reliability under field conditions. Sensor data are normalized and smoothed prior to analysis using K-Means clustering to map environmental states and a Random Forest classifier to predict crop health. Field validation demonstrates soil moisture as the most influential parameter, followed by temperature, pH, and light intensity. The Random Forest model achieved 93.01% accuracy, 93.88% precision, 99.02% recall, and a 96.38% F1-score on held-out data. By combining hybrid power, multi-sensor integration, dual storage, and machine learning, the system provides robust, data-informed monitoring that supports timely irrigation and management decisions in sugarcane cultivation.
Volume: 15
Issue: 1
Page: 384-395
Publish at: 2026-03-01

Design and development of an enhanced U-shaped microstrip antenna for super wideband applications in next-generation wireless systems

10.11591/ijres.v15.i1.pp204-213
Mani Periyasamy , Shankar Sharma Karthikeyan Jayalakshmi
The proposed enhanced U-shaped microstrip antenna is conceived with the aim of meeting the emerging needs of super wideband (SWB) applications in contemporary wireless communication systems. An efficient upgraded U-shaped patch design, in combination with substrate enhancements and impedance matching methods, is introduced in this work to remarkably increase the operational bandwidth, gain, and radiation efficiency of antenna. The antenna aims SWB achievement with the help of optimized dimensions and it is designed in such a way that it minimizes ground wave losses. It maximizes the impedance matching over a frequency range of 2 MHz to 20 GHz. Through various simulation outputs and experimental verifications, the antenna designed demonstrates excellent performance with a broad impedance bandwidth greater than 100% and the radiation patterns that are stable beyond entire frequency band. This work illustrates that the enhanced U-shaped microstrip antenna can attain the needs of next-generation communication technologies with specific criteria, and it establishes an efficient solution to SWB systems without sacrificing performance, cost, or size issues.
Volume: 15
Issue: 1
Page: 204-213
Publish at: 2026-03-01

Synaptic shield: fusion of ResNext–50 and long short-term memory for enhanaced deepfake detection

10.11591/ijres.v15.i1.pp224-235
Amit Mishra , Prajwal Chinchmalatpure , Govinda B. Sambare , Viomesh Kumar Singh , Atul Gulabrao Pawar , Rahul Prakash Mirajkar , Priyanka K. Takalkar , Kuldeep Vayadande
Recent developments in deepfakes have created much anxiety about the authenticity of any digital content and thus, calls for implementing detection mechanisms that will work accordingly. This paper uses Synaptic Shield, a innovative deep learning (DL) framework which is customized to detect alterations by deepfakes with high precision levels. It employs both convolution neural networks (CNNs) as well as modules for time feature extractions to test spatial and motion indicators from video data. High-level preprocessing pipelines in combination with confidence scoring mechanism help make Synaptic Shield adaptive toward manipulation techniques such as FaceSwap and DeepFake. The accuracy of our model surpasses other deepfake detection models with a high accuracy of 98.3%. The above results are based on exhaustive experimentation on standard datasets like FaceForensics++, DeepFake detection challenge (DFDC), and Celeb DeepFake (Celeb-DF). Synaptic Shield is shown to be the best with outstanding results that maintain a higher confidence score equivalent to its precision and reliability. Scalability in having the capacity to accommodate various manipulation techniques and levels of video quality indicates robustness in offering an effective method toward ensuring integrity in digital media. The work is an important move forward in addressing the problems created by DeepFake technologies.
Volume: 15
Issue: 1
Page: 224-235
Publish at: 2026-03-01

Improving voltage stability in isolated renewable energy microgrids using virtual synchronous generators

10.11591/ijpeds.v17.i1.pp683-695
Ahmad Supawi Osman , Aidil Azwin Zainul Abidin
The integration of renewable energy systems (RES) and distributed generation (DG) into microgrids introduces significant challenges in maintaining voltage stability due to intermittent generation and reduced rotational inertia. This systematic review critically examines advanced control strategies aimed at enhancing voltage resilience in isolated RES-driven microgrids. Particular focus is placed on virtual synchronous generators (VSGs), which emulate electromechanical dynamics of synchronous machines via state-space modeling, and model predictive control (MPC), which enables real-time control optimization under multi-constraint scenarios. The review synthesizes literature on coupling–decoupling behavior, impedance sensitivity, and dynamic voltage response under varying load conditions. Additionally, it evaluates the role of hardware-in-the-loop (HIL) platforms and Runge-Kutta-based simulations in validating control models for real-time deployment. A structured framework is proposed, aligning VSG-based inertia emulation with predictive control to address voltage dips, oscillations, and transient instabilities. The findings highlight both theoretical gaps and implementation opportunities for achieving robust voltage stabilization in next-generation microgrids.
Volume: 17
Issue: 1
Page: 683-695
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

Corporate social responsibility by listed commercial banks in Vietnam: practice and financial performance

10.11591/ijaas.v15.i1.pp261-271
Viet Ha Nguyen , Thi Minh Nguyet Dang
This study examines the impacts of financial performance (FP) on corporate social responsibility (CSR). The article investigates whether FP, as measured by return on assets (ROA) and net interest margin (NIM), influences the likelihood of CSR disclosure, drawing on stakeholder theory and legitimacy theory. The analysis employs binary logistic regression models and an unbalanced panel dataset comprising 26 listed banks between 2014 and 2024. If the bank discloses its CSR practices in its annual or sustainability report, the code for CSR disclosure is 1; otherwise, it is 0. The results show that, while NIM shows a negative relationship, ROA significantly improves CSR. Furthermore, there is a positive correlation between bank size (TA), equity to asset (EA), and CSR; a negative relationship of loan to deposit ratio (LDR) with CSR, and no significant statistical correlation was observed between debt to equity (DTE) and CSR. The study adds to the body of knowledge on CSR in developing nations and offers recommendations for sustainability and bank governance.
Volume: 15
Issue: 1
Page: 261-271
Publish at: 2026-03-01

Integrating swarm intelligence with CMIP climate models for ecocritical environmental analysis

10.11591/ijaas.v15.i1.pp168-177
Pavithra R. , S. Mahadevan
This research establishes a cohesive swarm intelligence framework used for climate simulations derived from the coupled model intercomparison project phase 6 (CMIP6), obtained from the earth system grid federation (ESGF). The study examines essential environmental variables such as near-surface air temperature (tas), sea-level pressure (psl), precipitation (pr), surface shortwave radiation (rsds), and longwave radiation (rlds). The system specifically evaluates a global mean surface temperature rise of 1.72 °C, a psl range of 980-1,030 hPa, pr anomalies averaging ¹1.3 mm/day, rsds values fluctuating between 140-280 W/m², and rlds values reaching a maximum of 350 W/m² for high-emission shared socioeconomic pathways (SSP)5-8.5 scenarios. The characteristics served as inputs for decentralized particle swarm architecture aimed at identifying ecological stress signs via geographic anomaly divergence, entropy deviation, and signal intensity thresholds. The model simulated swarm behavior across temporal CMIP grids, effectively capturing changes in climatic feedback and highlighting areas of ecological instability. The swarm framework dynamically analyzes pattern-based fluctuations in model output, facilitating ecocritical evaluation of environmental risk. This hybrid method integrates physically based climate data with adaptive artificial intelligence (AI) modeling, providing an ecologically contextual understanding of earth system changes and improving predictive insights for sustainability and policy formulation.
Volume: 15
Issue: 1
Page: 168-177
Publish at: 2026-03-01

ELLMW: an enhanced vision–language model for reliable text extraction from manually composed scripts

10.11591/ijres.v15.i1.pp194-203
Dhivya Venkatesh , Brintha Rajakumari Sivaraj
While conventional optical character recognition (OCR) systems can digitize text, they struggle with diverse handwriting styles, noisy inputs, and unstructured layouts, limiting their effectiveness. This study proposes enhanced large language model whisperer (ELLMW), a vision–language framework for accurate text extraction (TE) from fully handwritten scripts. The methodology integrates advanced preprocessing (noise reduction, binarization, and skew correction), deep learning–based handwriting recognition convolutional neural network–long short-term memory (CNN–LSTM), and LLM-based post-correction to ensure context-aware and structurally coherent outputs. The system converts scanned images, portable document formats (PDFs), and irregularly formatted answer sheets into machine-readable text, while automatically correcting errors in spelling, grammar, and layout. Experimental evaluation on a curated dataset of handwritten examination answer scripts (HEAS) demonstrates that ELLMW achieves 97.8% accuracy, 1.04%-character error rate (CER), and 3.24%-word error rate, outperforming widely used OCR tools including Tesseract, EasyOCR, Google Cloud Vision (GCV), PaddleOCR, ABBYY FineReader, and Transym OCR. The results highlight the model’s robustness across varying handwriting styles, noisy backgrounds, and complex document structures.
Volume: 15
Issue: 1
Page: 194-203
Publish at: 2026-03-01

Online method for identifying Thevenin model parameters of Li-ion batteries and estimating SOC using EKF

10.11591/ijres.v15.i1.pp54-67
Mouhssine Lagraoui , Ali Nejmi , Mouna Lhayani , Mohamed Benfars , Ahmed Abbou
Accurate state of charge (SOC) estimation is critical for the reliable operation of battery management systems (BMS) in electric vehicles (EVs) and energy storage applications. This paper presents a method for online identification of Thevenin model (TM) parameters and SOC estimation using the extended Kalman filter (EKF). The objective is to improve estimation accuracy by precisely characterizing the SOC-dependent variations of model parameters, including open-circuit voltage (VOCV), internal resistance R1, polarization resistance R2, and capacitance C2. These parameters are identified using least squares regression based on experimental discharge data from a 1.83 Ah lithium-ion (Li-ion) battery. The resulting model is validated under pulsed discharge conditions, achieving a mean absolute error (MAE) of 0.0059 V and root mean square error (RMSE) of 0.0074 V, indicating high modeling accuracy. Subsequently, an EKF algorithm is implemented using the identified model to estimate SOC in real time. Experimental results show excellent performance with an SOC estimation MAE of 0.059% and RMSE of 0.0798%, demonstrating high precision, fast convergence, and stability. The method effectively combines empirical parameter identification with a recursive filtering technique, offering a practical and embeddable solution for BMS applications. The study concludes that accurate parameter modeling significantly enhances EKF-based SOC estimation, providing a robust foundation for real-time battery monitoring and control. 
Volume: 15
Issue: 1
Page: 54-67
Publish at: 2026-03-01

Multi-modal sensor integration in chicken-fish-vegetable greenhouse agriculture based on internet of things

10.11591/ijres.v15.i1.pp138-149
Muhammad Risal , Pujianti Wahyuningsih , Suwatri Jura , Irmawaty Iskandar , Abdul Jalil
Integrated chicken-fish-vegetable farming is a type of agriculture that combines the benefits of them within a single ecosystem. The objective of this study is to develop a control and monitoring system for integrated greenhouse-based chicken-fish-vegetable farming using the internet of things (IoT). The monitoring method employs the integration of multi-modal sensors in the greenhouse, consisting of a camera, water level, DHT11, pH, TDS, DS18B20, light dependent resistor (LDR), and infrared (IR) sensor. The camera functions as a visual monitoring tool for the farm, water level sensor detects hydroponic water levels, DHT11 measures air temperature and humidity, pH sensor measures water acidity, TDS sensor detects water nutrients, DS18B20 measures pond water temperature, LDR detects weather conditions, and IR sensor measures sunlight intensity. The processing units used to control the sensors and output devices are the ESP32 and Raspberry Pi. The system outputs include a relay for pump control, an LCD for text messages, and IoT information visualization using the Blynk platform. The results of this study demonstrate that the multi-modal sensor device can effectively monitor the conditions of integrated greenhouse-based chicken-fish-vegetable farming, achieving an accuracy of up to 96%, with an average data transmission time of 6 seconds through the Blynk IoT platform.
Volume: 15
Issue: 1
Page: 138-149
Publish at: 2026-03-01

Inquisitive biometric feature analysis and implementation for recognition tasks using camouflaged segmentation with AI and IoT

10.11591/ijres.v15.i1.pp119-129
Mahesh Shankarrao Patil , Harsha J. Sarode , Abhijit Banubakode , Prakash Tukaram Patil , Nutan Patil , Vijayakumar Varadarajan , Deshinta Arrova Dewi
A vital role in reconfigurable and embedded systems which are deployed in smart environements and healthcare monitoring applications is played by human activity recognition (HAR). However, the potential leakage of sensitive user attributes raises serious privacy issues due to collection of data from the end devices and it needs to be transmitted to more powerful platforms for inference. Addressing this key challenge is principally crucial for resource-constrained embedded systems where efficiency of energy is a chief design requirement. The aim of this paper is present an energy-aware, privacy-preserving HAR framework appropriate for low-power embedded platforms. A machine learning–based camouflaged signal segmentation technique is proposed to transform the data collected from the sensor by eliminating sensitive information while preserving activity-relevant features. For characterization of trade off between the energy consumption and accuracy of recognition, parameters are extensively tuned by careful optimization in this proposed model. Experimental evaluations demonstrate that the method significantly reduces the inference of sensitive attributes such as gender, age, height, and weight, with minimal impact on HAR accuracy. Furthermore, the system supports configurable trade-offs between energy usage and classification performance, making it suitable for implementation on low-power embedded devices.
Volume: 15
Issue: 1
Page: 119-129
Publish at: 2026-03-01

Portable verification IP: a UVM-based approach for reusable verification environments in complex IP and SoC verification

10.11591/ijres.v15.i1.pp78-85
Harinagarjun Chippagi , Vangala Sumalatha
Reusable and portable verification techniques are becoming more and more necessary due to the growing complexity of system-on-chip (SoC) designs and the need for quick time-to-market. In order to facilitate cross-project reusability, automation, and scalability in SoC verification, this paper introduces a portable verification IP (PVIP) framework based on the universal verification methodology (UVM). The suggested framework improves coverage efficiency and verification portability across heterogeneous platforms by integrating UVM with the portable stimulus standard (PSS). In comparison to traditional UVM-based methods, experimental evaluation shows that the PVIP framework achieves 92% functional coverage, enhances reusability by 87%, and shortens verification cycle time by 27%. These findings demonstrate how PVIP can greatly speed up verification closure, minimize engineering effort, and assist in the development of the next generation of intelligent, scalable, and industry-ready SoC verification environments.
Volume: 15
Issue: 1
Page: 78-85
Publish at: 2026-03-01

Energy-efficient reconfigurable architectures for Edge AI in healthcare IoT: trends, challenges, and future directions

10.11591/ijres.v15.i1.pp1-20
Tole Sutikno , Aiman Zakwan Jidin , Lina Handayani
The integration of Edge artificial intelligence (AI) with internet of things (IoT) technologies is transforming healthcare applications, including wearable monitoring, telemedicine, and implantable medical devices, by enabling low-latency and intelligent data processing close to patients. However, stringent requirements on energy efficiency, reliability, real-time responsiveness, and data privacy continue to hinder scalable and long-term deployment in resource-constrained healthcare environments. Energy-efficient reconfigurable architectures—such as field-programmable gate arrays (FPGAs), coarse-grained reconfigurable arrays (CGRAs), and emerging memory-centric and heterogeneous platforms—have emerged as promising solutions to address these challenges by balancing flexibility, adaptability, and power efficiency. This review systematically examines recent advances in reconfigurable Edge AI architectures for healthcare IoT, highlighting key trends in hardware–software co-design, AI-assisted design automation, memory-centric optimization, and domain-specific overlays. It further identifies critical challenges, including energy–performance trade-offs, runtime reconfiguration overheads, security and privacy vulnerabilities, limited standardization, and reliability concerns in dynamic clinical settings. Finally, future research directions are outlined, emphasizing self-optimizing and context-aware architectures, secure and trustworthy reconfiguration mechanisms, unified frameworks for heterogeneous healthcare workloads, and sustainable, carbon-aware edge computing. Collectively, this review positions energy-efficient reconfigurable architectures as a foundational enabler for next-generation Edge AI in IoT-enabled healthcare systems.
Volume: 15
Issue: 1
Page: 1-20
Publish at: 2026-03-01

FPGA implementation and bit error rate analysis of the forward error correction algorithms in voice signals

10.11591/ijres.v15.i1.pp86-96
Ramjan Khatik , Afzal Shaikh , Shraddha Sawant , Pritika Patil
The idea of codes (VITERBI) is broadly utilized as a part of the wireless communication system as a result of their less complex nature in the decoding of transmitted message. This paper attempts to develop a performance analysis of the decoder by methods for bit error rate (BER) examination. The Galois field based decoder calculation is only utilized as a part of the communication systems. The decoder calculation-based Viterbi based decoder is carried out using field programmable gate arrays (FPGA) and MATLAB. This paper looks at the execution examination of both the calculations. The reconfigurable processor called Microblaze on the Spartan 3E FPGA is utilized for this purpose. MATLAB based code is used to see the BER analysis after the FPGA implementation output.
Volume: 15
Issue: 1
Page: 86-96
Publish at: 2026-03-01
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