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Bioelectricity generation and physicochemical evolution of a substrate with sheep compost in microbial fuel cells in a high Andean area

10.11591/ijece.v16i3.pp1085-1096
Joel Colonio , Elvis Carmen , Arlitt Lozano , Alizze Colonio
The recovery of organic waste, such as sheep compost, is a key strategy for energy valorization. This study evaluated its potential as a substrate in microbial fuel cells (MFCs) using zinc (anode) and copper (cathode) electrodes and analyzed the evolution of its physicochemical properties, using soil samples from a high Andean area of the Chacapampa district, Peru. Two configurations of ground-mounted MFCs in series were compared: C1 (16 reactors of 400 g) and C2 (8 reactors of 800 g), maintaining a total mass of 6.4 kg. The C2 configuration was significantly more efficient, generating a median power of 819.53 μW, more than double the 380.92 μW of C1 (p=0.002). The final physicochemical analysis revealed that the process transforms the substrate, increasing electrical conductivity and phosphorus availability, although potassium decreased. It is important to note that due to the use of reactive metal electrodes, the system operates as a hybrid microbial-galvanic cell, where the zinc anode is consumed. It is concluded that sheep compost is an effective substrate and that consolidating the volume in fewer reactors optimizes electrochemical performance, although long-term environmental impacts regarding zinc accumulation must be monitored.
Volume: 16
Issue: 3
Page: 1085-1096
Publish at: 2026-06-01

A risk-constrained SARSA–FIS hybrid decision architecture with adaptive exploration control

10.11591/ijece.v16i3.pp1531-1542
Joni Fat , Parwadi Moengin , Pudji Astuti , Sally Cahyati
Algorithmic trading systems operate in highly dynamic and uncertain environments where learning-based decision agents must balance adaptability with strict risk control. Reinforcement learning (RL) methods provide adaptive policy optimization but often suffer from unstable exploration and limited interpretability in financial markets. This study proposes a risk-constrained SARSA–FIS hybrid decision architecture with adaptive exploration control for algorithmic trading. The framework integrates a compact SARSA-based reinforcement learning environment with a Sugeno-type fuzzy inference system (FIS) that converts reinforcement signals into interpretable trading decisions. Exploration follows a decaying ε-greedy policy with a drawdown-triggered reset mechanism to maintain bounded risk exposure during learning. The system was implemented as a MetaTrader 5 Expert Advisor and evaluated on the GBPUSD currency pair using historical market data. Experimental results show that the hybrid framework improves trading performance compared with a rule-based baseline. During a six-month out-of-sample evaluation, the system achieved a net profit of 90 USD and a profit factor of 1.35, compared with 10 USD and 1.02 for the baseline. Extended one-year testing confirmed stable profitability and controlled drawdown behavior. The results demonstrate that integrating reinforcement learning, fuzzy decision mapping, and explicit risk constraints provides a practical approach for developing adaptive trading agents.
Volume: 16
Issue: 3
Page: 1531-1542
Publish at: 2026-06-01

Neuro-fuzzy control on a permanent magnet synchronous generator integrated in a wind system

10.11591/ijpeds.v17.i2.pp1304-1312
Mohammed Aoumri , Ibrahim Yaichi , Harrouz Abdelkader , Patrice Wira
This paper introduces a control strategy for a synchronous generator in a wind energy system using an adaptive neuro-fuzzy approach. The suggested controller, based on neuro-fuzzy logic (NFLC), is meant to govern a permanent magnet synchronous generator (PMSG) often utilized in wind power applications. The generator's output voltage phase, phase current, reactive power, active power, angular velocity, and DC voltage are all under control. The adaptive neuro-fuzzy controller efficiently stabilizes all variables in a brief amount of time, according to simulation results. The effectiveness and robust performance of the suggested control system are verified by a number of simulated scenarios. The resilience of fuzzy logic control (FLC) and NFLC systems was compared. The study carefully tested the performance of both control techniques under varied operating settings and disturbance situations to determine their relative stability, flexibility, and efficacy in sustaining desired system behavior.
Volume: 17
Issue: 2
Page: 1304-1312
Publish at: 2026-06-01

SMAC: System for monitoring and automatic control of water, nutrients, and pH in hydroponic nutrient film technique

10.11591/ijra.v15i2.pp365-376
Frengki Simatupang , Istas Pratomo Manalu , Eka Stephani Sinambela , Marojahan Mula Timbul Sigiro , Gerry Italiano Wowiling
Manual regulation of nutrient solution parameters in hydroponic systems often causes instability and delayed corrective actions. This study presents an Internet of Things (IoT) based system monitoring and automatic control (SMAC) for a nutrient film technique (NFT) hydroponic system to automatically regulate pH, total dissolved solids (TDS), and temperature. The proposed system integrates real-time sensors, automatic actuators, and dual-microcontroller architecture, in which an Arduino Uno performs local control while an ESP32 enables wireless IoT monitoring. An experimental systems engineering approach was applied for system design and performance evaluation. Automatic temperature compensation (ATC) was incorporated into pH and TDS measurements to improve reliability under varying thermal conditions. Experimental results indicate that the temperature sensor achieved an average error of 0.13 °C. The control algorithm corrected pH deviations gradually by approximately ±0.31 pH units (pH Up) and ±0.38 pH units (pH Down) per cycle without overshoot. Nutrient concentration control increased TDS by about 75 ppm per cycle under low-TDS conditions. Stability testing confirmed that pH and TDS remained within optimal ranges after disturbances, while safety mechanisms operated reliably under abnormal temperatures. The results demonstrate that the proposed SMAC system provides accurate, stable, and adaptive control suitable for precision and sustainable hydroponic applications.
Volume: 15
Issue: 2
Page: 365-376
Publish at: 2026-06-01

Engineering intelligence for a sustainable and resilient future: from foundations to real-world impact toward the SDGs

10.11591/ijece.v16i3.pp1075-1084
Tole Sutikno
The June 2026 issue of this journal presents a comprehensive body of research advancing efficient engineering intelligence from foundational theory to real-world deployment, with strong alignment to the Sustainable Development Goals (SDGs). A significant cluster addresses SDG 7 (Affordable and Clean Energy) and SDG 9 (Industry, Innovation, and Infrastructure) through innovations in microbial fuel cells, high-voltage insulation reliability, artificial intelligence (AI) based battery management systems, and energy-efficient LoRa/LoRaWAN frameworks. These works emphasize energy sustainability, system resilience, and infrastructure optimization. A second cluster focuses on advanced electronics, control, and communication systems, including memcapacitor design, hybrid model predictive control, reflectarray antennas, and embedded intelligence for autonomous systems, demonstrating efficiency-driven engineering across hardware and system levels. A dominant cluster highlights SDG 3 (Good Health and Well-being), with applications in medical imaging, sepsis detection, breast cancer classification, and mental health analysis, leveraging deep learning, transformers, and hybrid AI models. Finally, contributions aligned with SDG 4 (Quality Education) explore gamified learning systems, virtual reality adoption, and SDG-integrated educational information systems, while complementary studies in agriculture, finance, and Internet of Things (IoT) further demonstrate the societal impact of intelligent systems. Collectively, these works reinforce the role of efficient, scalable, and data-driven engineering in addressing global challenges.
Volume: 16
Issue: 3
Page: 1075-1084
Publish at: 2026-06-01

AMAC-LW: Adaptive medium access control for long range wide area network with energy-aware routing

10.11591/ijece.v16i3.pp1626-1644
Sowmya M. , S. Meenakshi Sundaram , Pandiyanathan Murugesan , Santhosh Kumar K. S. , Tejaswini R. Murgod
To enhance the performance of long range wide area network (LoRaWAN), a routing algorithm and a novel medium access control (MAC) layer protocol are required. In addition to addressing scalability and security issues, the protocol seeks to improve communication efficiency, dependability, and power consumption. It presents a dynamic routing method that reduces energy consumption by utilizing machine learning processes, adaptive routing tactics, and route optimization approaches. Simulations in a range of deployment situations are used to assess the suggested solutions. These results imply that the suggested protocol and routing scheme have the potential to greatly enhance the sustainability, energy efficiency, and performance of LoRaWAN-based Internet of Things networks. The effectiveness of the proposed solutions is evaluated through extensive simulations across diverse deployment scenarios. The results demonstrate that the proposed MAC protocol achieves a throughput of 350 bps, outperforming conventional protocols that typically reach only 220 bps. Latency is reduced to 50 ms from 85 ms, energy consumption is decreased to 2.5 joules from 4.5 joules, and the packet delivery ratio (PDR) is improved to 95%, compared to 75% in existing approaches. These findings highlight the potential of the proposed protocol and routing scheme to significantly enhance the performance, energy efficiency, and sustainability of LoRaWAN-based IoT networks.
Volume: 16
Issue: 3
Page: 1626-1644
Publish at: 2026-06-01

Hybrid deep learning (ILeS-Net) for lung cancer classification in cloud-IoT healthcare systems

10.11591/ijece.v16i3.pp1588-1607
Affrose Affrose , Cheruku Sandesh Kumar , Archek Praveen Kumar
This study presents a cloud–Internet of Things (cloud-IoT) based intelligent decision support framework for lung cancer classification and treatment recommendation, centered on a hybrid deep learning model termed ILeS-Net. Computed tomography (CT) images from a benchmark dataset are first preprocessed using Gaussian filtering to enhance image quality. Cancerous regions are identified using an Improved BIRCH (I-BIRCH) segmentation approach, followed by feature extraction using shape descriptors, color features, and Improved local Gabor XOR pattern (I-LGXP) textures. The extracted features are classified using ILeS-Net, which integrates Improved LeNet-5 and SqueezeNet architectures to achieve improved classification performance with reduced overfitting. Based on the classification results, the framework provides supportive recommendations to assist clinical decision-making. Experimental results demonstrate that the proposed ILeS-Net model achieves a maximum accuracy of 0.951, outperforming several conventional and state-of-the-art methods. The cloud–IoT integration further enables scalable, real-time, and secure data processing, highlighting the framework’s potential for practical computer-aided lung cancer diagnosis.
Volume: 16
Issue: 3
Page: 1588-1607
Publish at: 2026-06-01

Retrieval-augmented generation in enterprise knowledge systems: architecture, benefits, and applications

10.11591/ijece.v16i3.pp1407-1416
Mohammad Baqar
This paper presents an adaptive retrieval-augmented generation (RAG) framework for enterprise knowledge systems that combines multi-source ingestion, semantic indexing with Hugging Face embeddings and Facebook artificial intelligence similarity search (FAISS), metadata-aware retrieval, and grounded large language model generation. The research addresses a persistent enterprise gap: critical knowledge is distributed across documentation, tickets, code repositories, and collaboration tools, while static keyword search and periodically retrained language models cannot keep pace with rapidly changing operational data. The proposed approach contributes a privacy-preserving architecture, a retrieval-and-feedback loop that improves ranking quality over time, and a unified workflow that links evidence retrieval to solution recommendation. In an evaluation over a 1.2 million-document corpus and a six-week pilot, the framework improved Precision@10 from 0.58 to 0.81, reduced documentation retrieval latency from 45.6 s to 12.3 s, and shortened average bug-resolution time from 18.4 h to 7.2 h. These findings indicate that enterprise RAG can materially improve troubleshooting speed, knowledge reuse, and decision support while maintaining stronger control over sensitive organizational data. The broader implication is that adaptive, governed RAG systems can serve as a practical foundation for future enterprise artificial intelligence (AI) assistants, analytics platforms, and compliance-aware decision workflows.
Volume: 16
Issue: 3
Page: 1407-1416
Publish at: 2026-06-01

Variance-k-means++: A deterministic centroid initialization method based on variance for enhanced clustering stability

10.11591/ijece.v16i3.pp1434-1448
Widodo Widodo , Jiel Vayyad Ramadhan , Muhammad Ficky Duskarnaen , Via Tuhamah Fauziastuti , Chelsea Zaomi Pondayu , Mada Rekadarma Septianda
K-means++ is developed to improve the performance of k-means when choosing a starting centroid. However, both algorithms in clustering still select an initial centroid randomly. Randomly selecting initial centroids has the potential to produce unstable clusters. This paper proposes a deterministic centroid initialization method called variance-k-means++, which utilizes statistical properties—mean and variance—to generate pseudo-centroids and derive initial centroids. The method aims to improve clustering stability and reduce the number of iterations. For the initial study, we used low-dimensional data to conduct the experiment series. Then, we employed two baseline methods for benchmarking, k-means and k-means++. The results show that variance-k-means++ outperformed the baseline method on average. Evaluating in Davies-Bouldin Index (DBI) and convergence analysis, we obtained DBI values at 0.756 and 0,771 for vertical and horizontal variance k-means++ with Iris dataset. At the same time, baseline methods have 0.802 and 0.830 for k-means++ and k-means, respectively. In convergence analysis, the results are 5.158 for vertical and 5.474 for horizontal, while baseline methods are 9.000 and 8.842. The primary contribution of this study lies in its achievement of minimizing the number of iterations while enhancing cluster stability.
Volume: 16
Issue: 3
Page: 1434-1448
Publish at: 2026-06-01

Integrating BERT fine-tuning and genetic algorithm for superior depression detection in social media

10.11591/ijece.v16i3.pp1474-1484
Abd Allah Aouragh , Mohamed Bahaj , Fouad Toufik
Early detection of depression is crucial for minimizing its adverse effects on mental and physical health. Recent advancements in natural language processing facilitate the large-scale analysis of social media texts to identify depressive tendencies. Our study introduces a novel approach by integrating a genetic algorithm for hyperparameter tuning, optimizing the classification performance beyond conventional methods. We provide a comprehensive comparison of vectorization techniques, including term frequency-inverse document frequency (TF-IDF), Word2Vec, and a fine-tuned bidirectional encoder representation from transformers (BERT) model specifically adapted to our dataset. Using a dataset of 7,731 entries, we implemented standard pre-processing steps such as stop word removal and lemmatization before vectorizing the text. Five machine learning algorithms—decision tree, logistic regression, random forest, gradient boosting, and support vector machine—were evaluated, with hyperparameter tuning performed using a genetic algorithm. The highest accuracy (95.99%) and F1-score (95.91%) were achieved with the combination of fine-tuned BERT, support vector machine, and genetic algorithm optimization. This study demonstrates the advantages of integrating BERT fine-tuning with genetic optimization, outperforming traditional TF-IDF and Word2Vec approaches in depression detection.
Volume: 16
Issue: 3
Page: 1474-1484
Publish at: 2026-06-01

Designing and evaluating a community-based digital dictionary system for the Balinese language: An IT innovation adoption study

10.11591/ijece.v16i3.pp1369-1381
Cokorda Pramartha , Madek Jeani Purnama , Ida Bagus Gede Sarasvananda , I Wayan Arka , Ni Luh Watiniasih
Regional and vulnerable languages increasingly depend on digital tools to remain visible and usable in everyday life, yet many dictionary initiatives are described mainly in terms of content or interface features rather than evaluated as information-system innovations. This paper presents an exploratory design science study of a community-based Balinese digital dictionary that supports bidirectional Balinese-Indonesian lookup, Latin and Balinese Unicode script, speech-level information, part-of-speech tagging, related-word search, and role-based contribution workflows. The platform is implemented as a web-based system with a three-tier architecture and relational database. To evaluate adoption readiness, 40 users completed representative tasks and then responded to an adapted Moore and Benbasat IT innovation adoption instrument covering seven constructs. The results show high ease of use, relative advantage, and compatibility, indicating strong functional value and fit with user routines. Image and visibility are moderate, while result demonstrability and visibility show lower reliability and are therefore interpreted as exploratory indicators. The study contributes both a documented digital-dictionary artefact for Balinese language support and a reusable evaluation approach for other early-stage community-facing information and communication technology (ICT) systems. The findings suggest that wider uptake depends not only on technical quality, but also on institutional visibility, outreach, and continued content enrichment.
Volume: 16
Issue: 3
Page: 1369-1381
Publish at: 2026-06-01

A survey of retrieval algorithms in ad and content recommendation systems

10.11591/ijece.v16i3.pp1518-1530
Yu Zhao , Fang Liu , Yuan Yuan , Yifan Dang
This paper presents a survey of retrieval algorithms used in advertising recommendation and organic content recommendation systems. Modern digital platforms rely on retrieval-based models to efficiently match users with relevant advertisements or personalized content. This survey reviews key techniques including inverted index methods, collaborative filtering, content-based filtering, hybrid recommendation models, and the two-tower neural network architecture widely used in large-scale recommendation systems. The paper compares the objectives, data utilization strategies, and evaluation metrics of ad targeting and organic retrieval systems. Practical challenges such as cold-start problems, data quality, scalability, and privacy considerations are also discussed. This survey further highlights the growing connection between industrial recommendation pipelines and emerging retrieval mechanisms used in large language model (LLM) systems. This survey provides insights into the design principles of modern retrieval systems and outlines future research directions at the intersection of recommendation systems and LLM.
Volume: 16
Issue: 3
Page: 1518-1530
Publish at: 2026-06-01

Sepsis detection using biomarkers and machine learning

10.11591/ijece.v16i3.pp1286-1297
Tuan Anh Vu , Dang Hoai Bac , Minh Tuan Nguyen
Life-threatening dysfunction of organs, known as sepsis, is caused by an imbalanced response of host to infection. In this work, an efficient algorithm is proposed to address vital biomarkers for identification of sepsis using immune-related differential expression genes. A total of 16 gene datasets are processed for the extraction of a gene intersection between different gene datasets and the immune-related gene group, which improve the generalization of the final detection algorithm due to diversity of the input data. A novel gene selection method using sequential forward gene selection, machine learning, and ranked genes based on their importance calculated by a random forest model. A subset of 36 potential immune-related genes, which are identified as the biomarkers from 560 input genes, show an efficiency of the proposed gene selection algorithm. The biomarkers are validated the performance using various machine learning and deep learning related to sepsis diagnosis. The highest statistical performance is shown for the random forest model using the biomarkers as the input with an accuracy of 96.83%, sensitivity of 98.86%, specificity of 86.70%, and AUC of 98.67%. The proposed detection algorithm includes a random forest model and 36 biomarkers, which is simple, effective, and reliable for the applications in clinic environments.
Volume: 16
Issue: 3
Page: 1286-1297
Publish at: 2026-06-01

Optimization of transfer learning for facial emotion classification on the FER-2013 dataset

10.11591/ijece.v16i3.pp1213-1226
Nida Muhliya Barkah , Shofwatul ‘Uyun
Facial expressions play a key role in non-verbal communication by naturally reflecting human emotions. Facial emotion recognition (FER) using computer vision has gained attention with advances in deep learning. However, deep learning models require large datasets to perform well, posing a challenge for FER tasks with limited data. Transfer learning is a promising approach to address this issue, but a standardized method for FER is yet to be established. This study optimizes three transfer learning models ResNet-50, Inception V3, and Xception on the FER-2013 dataset. Experiments include testing input image sizes, hyperparameter tuning, data augmentation, layer addition, and training methods. Results show each model requires different input sizes for best accuracy. Hyperparameter tuning improves accuracy by 6.35%, 4.69%, and 1.04% for ResNet-50, Inception V3, and Xception, respectively. Augmenting only the disgust class yields better accuracy than augmenting all classes. The freeze fine-tuning method is less effective than fine-tuning alone on datasets with thousands of samples but outperforms the freeze layer method. The best accuracies achieved are 64.89% (ResNet-50), 65.83% (Xception), and 66.40% (Inception V3). These findings provide insights into freeze fine-tuning limitations and guidance for optimizing transfer learning in FER with limited data.
Volume: 16
Issue: 3
Page: 1213-1226
Publish at: 2026-06-01

Analyzing learners' perceptions of engagement and learning interaction in gamified massive open online courses for TVET using SEM-PLS

10.11591/ijece.v16i3.pp1319-1328
Azizul Mohd Yusoff , Sazilah Salam , Siti Nurul Mahfuzah Mohamad , Rujianto Eko Saputro
The introduction of gamified massive open online courses (G-MOOCs) represents a novel advancement in technical and vocational education and training (TVET). The use of gamification in education has been shown to increase engagement and motivation, which are crucial for effective learning. However, there is limited research on the specific impacts of G-MOOCs on learner outcomes in TVET. A key feature of G-MOOCs is the integration of gamification elements to enhance learner engagement and interest. This research employs structural equation modelling with partial least squares (SEM-PLS) to examine learners' perceptions of their participation and learning experiences in G-MOOCs for TVET. Specifically, the study aims to identify how gamification approaches such as fun, engagement, and learner interaction influence knowledge acquisition, skills development, satisfaction, and overall learning outcomes. The analysis reveals that G-MOOCs have a strong positive correlation (0.505) with learning engagement. Additionally, learning engagement significantly moderates learning outcomes (p=0.002). Interaction also has a significant impact (p=0.381) on learning outcomes. Overall, the findings indicate a significant positive relationship between learners' activities and their performance in G-MOOCs.
Volume: 16
Issue: 3
Page: 1319-1328
Publish at: 2026-06-01
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