Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer
Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer (ELTIKOM), with a registered number pISSN 2598-3245 and eISSN 2598-3288 is a peer review scientific journal published by P3M Politeknik Negeri Banjarmasin, Indonesia. ELTIKOM publishes original research articles and review papers in the fields of computer science, electrical and electronics engineering, and applied and interdisciplinary technologies. ELTIKOM is dedicated to promoting the advancement of science and technology by providing a scholarly platform for researchers, academics, and practitioners to publish their research findings and share knowledge with the scientific community. ELTIKOM welcomes submissions from researchers and encourages studies that emphasize applied research, technological innovation, system development, and real-world implementation including: Computer Science Software engineering, information systems, artificial intelligence, machine learning, computer vision, image processing, data mining, big data, cloud computing, intelligent applications, communication systems, signal processing, wireless and mobile communications, computer networks, network security, Internet of Things (IoT), and smart communication systems.. Electrical and Electronics Engineering Power systems, renewable energy, power electronics, electrical machines and drives, control systems, automation, instrumentation, embedded systems, and hardware design. Applied and Interdisciplinary Technologies Smart systems, ICT solutions, human–computer interaction, system integration, testing, and performance evaluation. All submissions to ELTIKOM undergo a rigorous double-blind peer-review process conducted by at least two independent reviewers. Editorial decisions are based on reviewer's recommendations and the journal's publication standards. ELTIKOM is published biannually, with regular issues released in June and December. Since Volume 7, No. 2 (2023), ELTIKOM has been accredited with grade SINTA 2 by the Ministry of Higher Education, Science, and Technology of the Republic of Indonesia, and this accreditation remains valid until 2028. ELTIKOM is committed to open-access publishing, aiming to make scientific research accessible to a wide audience, particularly researchers and students in developing countries.
All accepted papers will be published under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. Authors retain copyright and grant the journal right of first publication. CC-BY Licenced means lets others to Share (copy and redistribute the material in any medium or format) and Adapt (remix, transform, and build upon the material for any purpose, even commercially).
LSTM-Based Causal Attribution Modeling of the 2025 Sumatra Flash Flood Discourse on YouTube
Existing disaster sentiment analysis mainly focuses on emotional polarity classification, while often over-looking the causal reasoning that shapes public discourse on responsibility for disaster outcomes. This study proposes and assesses a Long Short-Term Memory (LSTM)-based causal attribution classification framework to examine YouTube comments related to the 2025 Sumatra flash flood. It compares LSTM performance with Sup-port Vector Machine (SVM) and Naïve Bayes baselines. A total of 17,503 publicly available comments were collected through the YouTube Data API v3 and processed into a final dataset of 12,299 comments. The com-ments were classified into two causal categories, human factor and nature/prayer factor, using lexicon-based scoring validated by three independent annotators (Cohen's κ = 0.81). The experimental results show that LSTM achieves 98.17% accuracy with strong stability (±0.25% standard deviation) under stratified five-fold cross-validation, substantially outperforming SVM (82.83%) and Naïve Bayes (75.04%). These findings indi-cate that sequence-based architectures can capture the contextual dependencies in causal attribution dis-course, offering a replicable framework for disaster risk communication monitoring systems.
Copyright (c) 2026 Kunti Najma Jalia, Adi Suwondo, Hidayatus Sibyan https://creativecommons.org/licenses/by/4.0
Detecting Research Evolution and Trends in The Computer Vision Domain using Topic Modeling and Large Language Models
Research evolution and trends in computer vision (CV) are important for understanding the field’s land-scape. Trends show which topics are gaining attention, while evolution reveals how those topics change over time. Understanding both helps researchers gain insight into CV and anticipate emerging areas of focus. How-ever, the rapid growth of publications makes such detection challenging. This paper aims to detect research evolution and trends in CV using topic modeling (TM) and large language model (LLM) techniques. The study applies TM and LLM approaches to papers from leading CV conferences, Computer Vision and Pattern Recog-nition (CVPR), International Conference on Computer Vision (ICCV), and Winter Conference on Applications of Computer Vision (WACV), published between 2013 and 2023, totaling more than 21,000 papers, using only abstracts and titles. The TM methods used are Latent Dirichlet Allocation (LDA) and Bidirectional Encoder Representations from Transformers for Topic Modeling (BERTopic), which generate keywords that represent topics. LLMs then refine these topics to support better analysis. The results show that research evolution and trends are easier to identify from abstracts than from titles, with BERTopic outperforming LDA in internal va-lidity based on coherence metrics and external validity based on human judgment. In addition, the topics evolved from traditional image processing tasks in earlier years to a stronger focus on deep learning and, more recently, generative approaches. Integrating TM techniques with LLMs enhances the detection of evolving re-search themes and trends in CV. This approach provides a clearer understanding of the field's development and helps anticipate future directions.
Copyright (c) 2026 Setio Basuki, Zamah Sari, Rizky Indrabayu, Masatoshi Tsuchiya https://creativecommons.org/licenses/by/4.0
Domain Adaptation of Bert Models for Biomedical Entity Extraction from Indonesian Health News
Health-related news articles play an increasingly important role in public health monitoring. However, their unstructured linguistic style complicates the automatic extraction of biomedical information. Indonesian health news shows high lexical variation by combining medical terms, colloquial expressions, borrowed Eng-lish words, and culturally specific symptom descriptions. This condition creates challenges for Named Entity Recognition (NER). To address the limited availability of domain-specific resources, this study compares four Transformer-based models, namely BERT, IndoBERT, RoBERTa, and BioBERT, for biomedical NER in Indone-sian health news. A new BIO-annotated dataset consisting of 272 manually labeled articles was constructed and validated, achieving strong inter-annotator agreement (Cohen’s Kappa = 0.88). To reduce data limita-tions, an additional 103 articles were automatically annotated using the best-performing model, RoBERTa, through a semi-supervised approach. All models were fine-tuned under identical settings and evaluated at both BIO and entity levels. The results show that RoBERTa achieves the highest weighted F1-score (0.9543). Howev-er, its macro F1-score (0.3873) indicates uneven performance across entity classes because of severe label im-balance, with non-entity tokens dominating the dataset. This finding highlights the importance of emphasizing macro-level evaluation to better reflect entity recognition performance. RoBERTa consistently outperforms the other models, which may be explained by its robust architecture and adaptability to diverse linguistic patterns. In contrast, BioBERT underperforms because of cross-lingual and domain mismatch, as it is pretrained on Eng-lish biomedical corpora and optimized for scientific text rather than journalistic language. The error analysis further identifies boundary inconsistencies and under-detection of low-frequency entities, especially in the drug and symptom categories.
Copyright (c) 2026 Maria Bernadette Chayeenee Norman, Ika Novita Dewi, Darnell Ignasius https://creativecommons.org/licenses/by/4.0
Lightweight Multi-Model CNN Fusion of ResNet50v2 and MobileNetv2 for Accurate Brain Tumor Classification on MRI Scans
Brain tumor classification remains a critical challenge in medical imaging because manual diagnosis from Magnetic Resonance Imaging is time-consuming and may produce inconsistent interpretations. Automated approaches using deep learning have shown promising results, although single-model methods may still face limitations in generalization and stability. This study introduces a lightweight multi model Convolutional Neural Network that combines MobileNetV2 and ResNet50V2 as dual-backbone feature extractors. Mo-bileNetV2 supports computational efficiency, while ResNet50V2 strengthens residual feature learning. The Bangladesh Brain MRI Dataset, which contains 6,056 images in three categories, Brain Glioma, Brain Menin-gioma, and Brain Tumor, was used in this study. All images were resized to 224 × 224 pixels before feature extraction, fusion, and classification. The proposed multi-model achieved 99.56% training accuracy and 93.37% validation accuracy, outperforming MobileNetV2 with 98.37% and 89.60 percent, and ResNet50V2 with 97.55% and 86.17 percent. On the test set, it reached 94.89% accuracy, 0.1536 loss, and 0.991 ROC AUC. These results show that integrating lightweight and deep architectures can improve robustness and accuracy while maintaining efficiency, making this approach suitable for real-world clinical support in brain tumor diagnosis.
Copyright (c) 2026 Abd Salam At Taqwa, Muhammad Fadhlullah, La Ode Fefli Yarlin https://creativecommons.org/licenses/by/4.0
Hybrid ResNet50 with Convolutional Block Attention Module (CBAM) for Image Classification using Fine-Tuning
Image classification is a crucial area in digital image processing that requires models capable of robust and stable feature representation. The main challenges in this study include variations between visual classes, di-verse image quality, and limited labeled data, which often hinder the model’s ability to generalize optimally. This study proposes a hybrid ResNet50-CBAM approach, which integrates the strengths of the ResNet50 archi-tecture in deep feature extraction with the Convolutional Block Attention Module (CBAM) attention mecha-nism to improve the model’s focus on the most informative areas of the image. The training process was carried out in two phases, namely transfer learning to utilize the initial representation from the ImageNet dataset, fol-lowed by fine-tuning to adjust the network weights to the image characteristics of the research dataset. The datasets were reorganized and split into 70% training, 15% validation, and 15% testing subsets to ensure a balanced distribution of samples. In addition, various augmentation techniques were applied to increase data diversity and improve the model’s generalization capability. The evaluation results showed that this hybrid approach achieved an overall accuracy of 99%, indicating very high and consistent performance across the entire dataset. The integration of CBAM into the ResNet50 architecture was proven to strengthen the feature extraction process by highlighting the most relevant areas, resulting in a more accurate, stable, and effective image classification model for a wide range of artificial intelligence image processing applications.
convolutional block attention module (CBAM)
Copyright (c) 2026 Aulya Rachma Dewi, Aris Thobirin, Sugiyarto Surono https://creativecommons.org/licenses/by/4.0
On-Off Control System for Temperature and Humidity in A Sweet Potato Chip Dryer using Heating Elements
Sweet potato chips are an innovative processed product that has been widely developed by Micro, Small, and Medium Enterprises in several regions. Most sweet potato chips are dried using traditional methods, such as direct sun drying. However, during the rainy season, solar heat cannot be used optimally, so the drying pro-cess tends to be slower. This study aims to create a temperature and humidity control system for a sweet potato chip drying machine using a dual air heater. Based on previous studies, temperature and humidity are crucial factors in food drying because they affect product quality. Earlier studies on drying ovens and temperature-controlled drying systems have shown that on-off control can maintain temperatures close to the setpoint with a simple design and low cost for small-scale applications. In addition, the development of microcontrollers such as the Arduino Uno has made it possible to design automatic control systems that can monitor and adjust temperature and humidity in real time. The method used in this study was the design of a laboratory-scale dry-ing oven equipped with a DHT22 sensor, an Arduino Uno, and an on-off control system using a heater and fan as actuators. System performance was evaluated under several operating conditions, namely with and without the control system. The results showed that the control system was able to maintain the dryer temperature with-in the range of 45°C to 55°C and relative humidity between 35% and 40%. In addition, increasing the drying time from 2 hours to 3 hours reduced the product moisture content from 39.9% to 7.7%.
Copyright (c) 2026 Bobi Khoerun, Cahyono Putra, Rahajeng Kurnianingtyas https://creativecommons.org/licenses/by/4.0
Enhancing The Performance of LSB Steganography with RSA and Huffman
The growing sophistication of digital threats has exposed major vulnerabilities in traditional data protec-tion methods, particularly those that rely only on cryptography or steganography. These single-layer tech-niques often fail to ensure both data confidentiality and concealment, which makes information more vulnera-ble to detection and attack. This study addresses the limited effectiveness of existing security mechanisms in balancing secrecy, efficiency, and image quality. Therefore, this study aims to develop a multi-layer steganog-raphy system that integrates Huffman-based data compression, enhanced RSA encryption, and optimized Least Significant Bit (LSB) embedding to improve data protection and performance. The proposed model was imple-mented in Python with a graphical user interface to improve usability. The experimental results show high im-perceptibility, with a Peak Signal-to-Noise Ratio (PSNR) of 48.32 dB and a Structural Similarity Index (SSIM) of 0.9987, indicating minimal visual distortion. Security testing confirmed resistance to steganalysis and brute-force attacks, while performance evaluation showed stable processing efficiency. These findings indicate that the proposed system offers a secure, efficient, and practical framework for digital information hiding in modern communication environments.
Copyright (c) 2026 Muhammad Rifqy Abdul Gofur Al Fatah, Denar Regata Akbi, Bashor Fauzan Muthohirin https://creativecommons.org/licenses/by/4.0
Accuracy Comparison of Multivariate Newton-Raphson and Newton-Kantorovich Methods through Numerical Simulation in Nonlinear Systems
Nonlinear systems of equations often appear in various fields of science and generally cannot be solved analytically, so numerical methods are required. However, previous studies have not provided a direct comparison of the accuracy and efficiency of the Multivariate Newton-Raphson method and the Newton-Kantorovich method when applied to the same nonlinear system, creating a gap in understanding their relative performance. This study aims to analyze and compare the performance of two numerical methods, namely the Newton-Raphson method and the Newton-Kantorovich method, in solving nonlinear systems of equations numerically. The evaluation is based on the convergence rate, result accuracy, and iteration efficiency of each method. The nonlinear system used involves trigonometric, exponential, and polynomial functions. Simulations were conducted twice using three equations directly for each method. The error tolerance was set at 0.001, with a maximum of 100 iterations. The simulation results showed that the Multivariate Newton-Raphson method had the best performance, requiring only 7 iterations to achieve convergence with a very small error of 2.711×10^(-7). In contrast, the Newton-Kantorovich method required 21 iterations and produced an error of 6.770×10^(-5), indicating slower convergence and lower efficiency. Based on these results, it can be concluded that the Multivariate Newton-Raphson method is the more accurate and efficient method for solving nonlinear systems of equations through numerical simulation. This finding contributes to the selection of an appropriate numerical method and opens opportunities for further exploration in higher-dimensional systems.
Copyright (c) 2026 Syaharuddin, Hendi Hidayah, Mahsup Mahsup, Saba Mehmood, Wasim Raza https://creativecommons.org/licenses/by/4.0
K-Nearest Neighbor Algorithm for Intelligent Monitoring and Control System Integration in Renewable Energy Applications
A real-time biogas monitoring and control system was developed by integrating the K-Nearest Neighbor (KNN) algorithm into an IoT-based framework for methane pressure prediction and automated control. The system uses an ESP32 microcontroller connected to temperature, gas, and pressure sensors (DHT22, MQ-4, MPX5700) to continuously collect data, with cloud connectivity provided through Firebase and Blynk platforms. The predictive model operates within a live feedback loop, allowing immediate actuation based on forecasted methane conditions. With an optimal parameter of k=4, the KNN model achieved 93.33% accuracy, supported by a mean absolute error (MAE) of 0.18 and a root mean square error (RMSE) of 0.21. A comparative evaluation with Random Forest and Gradient Boosting algorithms showed that, although these models yielded slightly higher accuracy, KNN provided superior computational effi-ciency for embedded deployment. The system maintained stable operation during tests involving sensor anomalies, network interruptions, and data noise. However, redundancy mechanisms and improved vali-dation strategies are recommended to enhance robustness. The findings demonstrate that methane pro-duction can be effectively predicted using temperature and pressure data, with further accuracy im-provements possible through additional process variables such as pH and fermentation age.
Copyright (c) 2025 Mochammad Junus, Laily Nur Fa‘izah, Mohd Noor, Indra Lukmana Putra https://creativecommons.org/licenses/by/4.0
Comparative Analysis of Graph Neural Networks for Fraud Detection
Detecting financial fraud is a complex and evolving challenge, particularly because of the relational nature of transaction data, graph sparsity, and severe class imbalance. To the best of our knowledge, this study repre-sents one of the first systematic benchmarks of five prominent Graph Neural Network (GNN) architectures, GCN, GAT, GraphSAGE, GIN, and SGCN, for fraud detection under balanced and imbalanced conditions across multiple public datasets. We explicitly evaluate the impact of the Synthetic Minority Oversampling Technique (SMOTE) on graph-based fraud detection performance, an aspect that has rarely been addressed in prior research. The comparative analysis considers predictive performance (AUC, F1-Score, Precision, Re-call) and computational efficiency to provide actionable guidance for real-world development. The experimental results show that GraphSAGE offers the best trade-off between accuracy and execution time for laten-cy-sensitive environments, while GAT’s attention mechanism supports offline, interpretability-driven analysis. These findings provide empirical evidence to inform GNN selection strategies for scalable and effective fraud detection systems.
Copyright (c) 2026 Ricky Maulana Fajri, Muhammad Gald Teary, Ni Wayan Pricila Yuni Praditya https://creativecommons.org/licenses/by/4.0
Optimization Model for Fake Account Detection on Twitter (X) Social Media using Feature Engineering and Machine Learning Approaches
Twitter (X) has become an important platform for community interaction, but this also creates serious challenges due to the proliferation of fake accounts that can harm users and undermine credibility. Previous studies have proposed detection methods but often lacked forensic analysis based on extracted feature information. This study utilizes labeled datasets and supervised evaluation metrics (precision, recall, and F1-score) to validate model performance. Extracting behavioral information from features is crucial for achieving accurate and reliable detection results. The study introduces a novelty in the form of engineered behavioral features that significantly enhance detection accuracy, achieving up to 99.94% using AdaBoost. The proposed approach detects fake accounts on Twitter (X) by extracting key feature information and developing an optimal detection method through machine learning algorithms, including Random Forest, SVM, and AdaBoost. Furthermore, the model is optimized using feature engineering techniques. The novelty of this work lies in the development of engineered features through distribution analysis based on data characteristics and the improvement of classification performance through feature engineering optimization. The initial experiment without feature engineering shows that Random Forest achieved the highest accuracy of 98.77%, followed by AdaBoost at 98.57% and SVM at 95.90%. After applying feature engineering, performance improved, with AdaBoost reaching 99.94%, Random Forest 99.69%, and SVM 99.32%. The proposed model can assist system analysts in detecting fake accounts and contribute to solving forensic cybercrime challenges, particularly in identifying fake social media profiles.
Copyright (c) 2025 Ni Nyoman Eny Perimawati, Roy Rudolf Huizen, Dandy Pramana Hostiadi https://creativecommons.org/licenses/by/4.0
The Synergy of Blockchain and Cybersecurity: Building Trust in Digital Environments
The rapid expansion of digital ecosystems has intensified concerns about data security, privacy, and trust. Blockchain technology, characterized by its decentralized, immutable, and transparent nature, offers a transformative approach to strengthening cybersecurity. This paper examines the synergy between blockchain and cybersecurity, emphasizing how blockchain’s cryptographic foundations, consensus mechanisms, and smart contracts can mitigate cyber threats, enhance authentication, and ensure data integrity. By analyzing emerging trends, challenges, and real-world applications, this study underscores the potential of blockchain to reinforce digital trust and resilience across diverse sectors. The findings contribute to the ongoing discourse on secure digital environments by proposing an integrated framework for blockchain-based cybersecurity solutions
Copyright (c) 2025 Hewa Majeed Zangana, Zina Bibo Sallow, Firas Mahmood Mustafa, Mamo Muhamad Husain https://creativecommons.org/licenses/by/4.0
Adaptive Power Management for Multi-User Indoor LiFi Communication Systems using Evolutionary Algorithms
Visible Light Communication (VLC) systems have emerged as a promising alternative to RF-based solutions, especially in electromagnetic-sensitive environments such as hospitals and aircraft cabins. This study presents a MATLAB-based simulation of an indoor VLC setup using corner and center LED array layouts in an emer-gency room scenario. The model supports variations in room length and user density and applies a genetic al-gorithm (GA) for dynamic LED current optimization to improve coverage fairness. This paper proposes an adaptive beam-shaping and power-optimization framework for multi-user indoor LiFi communication systems. The design is particularly suited for environments sensitive to electromagnetic interference (EMI), such as hospitals and emergency rooms, where RF-based systems may pose risks or interfere with medical equipment. Simulation results show that the corner configuration consistently outperformed the center configuration in terms of minimum and average received power, especially in larger rooms (10 m to 12 m) and with higher user numbers (6 to 8). For instance, in the corner case, the mean received power changed from 1.4075×10⁻⁶ to 1.3808×10⁻⁶ W when the number of users increased from 6 to 8, whereas in the center case it dropped from 1.0154×10⁻⁶ to 7.9926×10⁻⁷ W. Additionally, the optimal minimum power improved in larger rooms and with higher user densities, thus helping maintain communication even for the weakest users. The results confirm that GA-based current shaping improves energy efficiency and signal distribution, making this approach valu-able for robust and future-ready VLC applications in emergency scenarios.
Copyright (c) 2026 Saif Ahmed Abed, Nahla Abdul Jalil Salih, Ihsan Jabbar Hasan, Nadhir Ibrahim Abdulkhaleq https://creativecommons.org/licenses/by/4.0
Wound Depth Measurement System in Forensic Cases using Image Processing and Machine Learning
Accurate evaluation of wound depth is crucial in forensic investigations, as it significantly affects case assessments and outcomes. This study introduces a method for classifying wound depth using a Support Vector Machine (SVM) model and compares its performance with Decision Tree and Logistic Regression models. The classification was based on color features extracted from HSV and LAB color spaces. The da-taset consisted of 76 images categorized into three stages: stage 2 (36 images), stage 3 (12 images), and stage 4 (28 images). Model performance was evaluated using confusion matrices, precision, recall, and F1-score. The SVM model achieved an overall accuracy of 85%, demonstrating higher precision and re-call across all stages compared to the Decision Tree and Logistic Regression models, which achieved 50% and 70%, respectively. The results indicate that the SVM model performed particularly well in distinguish-ing stage 2 wounds, although differentiating between stages 3 and 4 remained challenging. Overall, the proposed system shows potential to enhance the accuracy and efficiency of forensic wound evaluation by providing a rapid and objective classification tool. However, as the system was tested on a limited dataset under controlled conditions, further research should expand the dataset, incorporate additional features, and explore other machine learning algorithms to improve robustness and applicability in real forensic contexts.
Copyright (c) 2025 Elvira Sukma Wahyuni, Kern Cesarean Ahnaf, Firdaus Firdaus, Nurul Ashikin Abdul-Kadir, Nor Aini Zakaria, Idha Arfianti Wiraagni, Diwangkoro Aji Kadarmo https://creativecommons.org/licenses/by/4.0
Explainable AI-Driven Convolution Neural Network for Quality Grading of Soybean Seeds
This study developed a soybean seed grading system based on Explainable Artificial Intelligence (XAI). Traditional soybean quality assessment is time-consuming, and limited research has applied explainable AI methods to the grading process. To address these issues, this study employed classification and XAI methods through several stages. First, it examined five main categories of soybean seed characteristics: broken, immature, intact, skin-damaged, and spotted. Second, it used the Soybean Seeds Dataset contain-ing 5,513 images. Third, data preprocessing was carried out, including image normalization and data division for training and testing. Finally, a Convolutional Neural Network (CNN) model based on the VGG-16 architecture was used for classification experiments. Three XAI methods, namely Shapley Additive Explanations (SHAP), Local Interpretable Model Agnostic Explanations (LIME), and Layerwise Relevance Propagation (LRP), were applied to evaluate model performance and interpretability. The VGG-16 model achieved an accuracy of 91%, with precision, recall, and F1-score values of 0.91, 0.91, and 0.90, respectively. The interpretability analysis using SHAP, LIME, and LRP showed that the model consistently identified key features such as seed shape and surface texture, demonstrating that the system is transparent and reliable in determining soybean seed quality.
explainable artificial intelligence (XAI)
Copyright (c) 2025 Valencia Sefiana Putri, Setio Basuki https://creativecommons.org/licenses/by/4.0
Classification of Diabetes Mellitus using Decision Trees
Diabetes Mellitus is a global health concern, with its prevalence and incidence rising sharply world-wide, including in Indonesia. Several factors contribute to the onset of diabetes mellitus, such as heredity, age, weight, and blood pressure. Managing blood sugar levels, maintaining a balanced diet, exercising regularly, and undergoing early screening when necessary are among the key measures to prevent and control this disease. Early diagnosis is essential to reduce both the number of cases and the associated risks. This study aims to detect diabetes mellitus using classification techniques. The method involves several subprocesses within the classification procedure. The first stage, data preprocessing, includes feature selection and data cleaning. The resulting preprocessed data are then used in the classification stage, specifically the learning subprocess, to generate a decision tree model. Model construction employs pruning, followed by training and performance evaluation. The study utilizes a diabetes dataset obtained from kaggle.com, consisting of 768 records. The dataset includes attributes such as Pregnancies, Glucose, Blood Pressure, Skin Thickness, Insulin, Body Mass Index (BMI), Diabetes Pedigree Function, Age, and the label Outcome. Testing was conducted using decision trees with maximum depths of 3, 5, 7, 10, and 15. The results show that the highest accuracy (88.56%) occurred at a maximum depth of 5, while the highest recall (100%) was achieved at a depth of 3. The highest precision (47.37%) and specificity (95.85%) were also obtained at a depth of 3.
Copyright (c) 2025 Rinci Kembang Hapsari, Abdullah Harits Salim, Leonardo Fahsi Oktavian, Aldy Ramadhan Fitra https://creativecommons.org/licenses/by/4.0
Design and Performance Evaluation of A Portable Low-Head Pico-Hydro System using A Rewound Axial Generator for Rural Energy
This study evaluates the performance of a pico-hydro system installed on a river with low head and discharge. The system was assessed under no-load and varying load conditions (25–100%). The results indicate that the generator performs according to the initial design, despite some fluctuations in output parameters. Under no-load conditions, the generator maintains a stable output voltage between 12–14 VAC, with a rotational speed of 590–600 RPM, a system frequency of 59–60 Hz, and zero current. The step-up transformer successfully raises the voltage to 220–222 V with high stability, although minor ripple is observed in the output signal. Under load, the generator voltage slightly decreases to 12–14 V as the load increases. The rotational speed also declines (560–590 RPM), affecting frequency stability, which drops from 59 Hz at 25% load to 56 Hz at full load. The current rises proportionally with the load, from 0.10 A at 25% to 0.45 A at 100%. The observed performance drop under load highlights the effect of load on generator speed and overall system output. The primary impacts of the 25–100% load range are evident in generator speed, frequency stability, and waveform quality. Overall, the system performs satisfactorily for low-head pico-hydro applications with a power capacity of up to 100 Watts, suitable for rural street lighting.
Copyright (c) 2025 Aripriharta Aripriharta, Ahmad Dhaffa Nibrosoma, Arif Nur Afandi, Mohamad Rodhi Faiz, Nur Aini Syafrina Rahmadhani, Muhammad Cahyo Bagaskoro, Norzanah Rosmin https://creativecommons.org/licenses/by/4.0
A VGG16 CNN-based Method for Multiclass Lung Cancer Classification using CT Imaging
Lung cancer is the leading cause of death worldwide among all types of cancer. Early detection and accurate classification are essential to prevent disease progression and improve patient survival rates. One effective approach is the use of computer-aided diagnosis (CAD) systems based on medical imaging, particularly CT scans, which provide high-resolution and non-invasive visualization of lung structures, including blood vessels, soft tissues, and lesions or nodules. This study proposes a VGG16 CNN-based multiclass classification method for lung cancer. Unlike previous studies that primarily focus on binary classification, this research addresses four distinct classes of lung nodule CT images to better reflect complex clinical needs. The modified VGG16 architecture incorporates additional layers, including Flatten, Dense, and Dropout, along with the Softmax activation function, effectively improving classification performance and reducing overfitting risk. An ablation experiment was also conducted by replacing ReLU with LeakyReLU to address the potential “dying ReLU” issue. However, the results indicated that LeakyReLU did not provide significant improvement over the standard ReLU. The proposed model achieved an accuracy of 90.72%, precision of 91.5%, sensitivity of 89.25%, specificity of 96.76%, F1-score of 90%, and a low loss value of 0.37. Furthermore, the modified VGG16-CNN outperformed other CNN architectures, including ResNet50, EfficientNetB1, MobileNetV2, and AlexNet, in multiclass lung cancer image classification. The results demonstrate that the proposed method is effective for diagnosing lung nodules from CT scans and has the potential to support medical professionals in making accurate and timely diagnoses.
Copyright (c) 2025 Sekar Sari, Muna Afdi Muniroh, Kevin Ilham Apriandy https://creativecommons.org/licenses/by/4.0
Smart Hydroponic Nutrient Monitoring and Control System using Fuzzy Logic and IoT
Hydroponic farming offers an efficient and sustainable solution for modern agriculture, although main-taining stable nutrient levels remains a key challenge. Previous systems often exhibited high overshoot and were unable to adapt to external changes or disturbances, and no existing research has adaptively regulated nutrient levels based on the plant’s growth stage. Therefore, this study aims to develop a smart nutrient monitoring and control system for hydroponics using a Sugeno-type fuzzy logic controller inte-grated with an IoT-based application. Unlike prior systems that rely on fixed setpoints or only address nutrient deficiency, this system dynamically adjusts nutrient and water levels based on real time sensor data and plant growth phase. The system utilizes nutrient, water level, and temperature sensors connected to an ESP32 microcontroller, with fuzzy logic determining solenoid valve activation duration. The control system achieved stable regulation with zero overshoot, a settling time of 840 seconds, and effective recov-ery from nutrient disturbances. Growth tests on celery showed a 102.6% improvement in height, 275% in stem diameter, and 112.5% in leaf width compared to manual control. IoT monitoring via a mobile appli-cation ensured real time visibility of hydroponic parameters. These results demonstrate the system’s ca-pability to maintain optimal nutrient levels, improve control precision, and enhance plant productivity.
Copyright (c) 2025 Muhammad Alimussadad, Dyah Lestari, Wahyu Mulyo Utomo https://creativecommons.org/licenses/by/4.0
Imbalanced Text Classification on Tourism Reviews using Ada-boost Naïve Bayes
Hidden paradise is a term that aptly describes the island of Madura, which offers diverse tourism potential. Through the Google Maps application, tourists can access sentiment-based information about various attractions in Madura, serving both as a reference before visiting and as evaluation material for the local government. The Multinomial Naïve Bayes method is used for text classification due to its simplicity and effectiveness in handling text mining tasks. The sentiment classification is divided into three categories: positive, negative, and mixed. Initial analysis revealed an imbalance in sentiment data, with most reviews being positive. To address this, sampling techniques—both oversampling and undersampling—were applied to achieve a more balanced data distribution. Additionally, the Adaptive Boosting ensemble method was used to enhance the accuracy of the Multinomial Naïve Bayes model. The dataset was split into training and testing sets using ratios of 60:40, 70:30, and 80:20 to evaluate the model’s stability and reliability. The results showed that the highest F1-score, 84.1%, was achieved using the Multinomial Naïve Bayes method with Adaptive Boosting, which outperformed the model without boosting, which had an accuracy of 76%.
Copyright (c) 2025 Ika Oktavia Suzanti, Fajrul Ihsan Kamil, Eka Mala Sari Rochman, Huzain Azis, Alfa Faridh Suni, Fika Hastarita Rachman, Firdaus Solihin https://creativecommons.org/licenses/by/4.0
Efficient Strategy for Distribution Transformer Replacement: A Study on Replacement Methods in Power Systems
A dependable and efficient electric power distribution system is increasingly required due to growing industrial and population demand. Many distribution transformers currently in use are outdated, which increases operating expenses and reduces efficiency. This study examines the issue of determining the most cost-effective time to replace aging distribution transformers to optimize operational expenses and enhance service performance. The purpose of this study is to calculate the annual operating costs of dis-tribution transformers at PT PLN (Persero) Makassar Branch and to identify the optimal replacement time to support more effective decision-making. Using an economic feasibility analysis based on the annuity method and linear regression, this study compares the annual operating costs of old transformers with those of new ones. The findings indicate that the annual cost of old transformers is higher than that of new ones, suggesting that replacing old transformers is more economical. The results show that operating expenses can be reduced and distribution efficiency improved through a systematic transformer replace-ment approach. Based on economic engineering analysis, this study provides a practical and relevant model for transformer replacement decision-making that can assist asset management and investment planning in the power industry.
Copyright (c) 2025 Nur Asmi Rahmawati https://creativecommons.org/licenses/by/4.0
Fine-Grained Plant Classification using Vision Transformers with Optimized MLP Heads
Automatic plant species classification is crucial for advancing education and biodiversity conserva-tion. Deep learning models, such as Vision Transformer (ViT), have demonstrated strong performance in plant species classification tasks. However, limited research explored the impact of hyperparameters in the Multi-Layer Perceptron (MLP) head of ViT models for plant-species classification. This study investi-gated the influence of learning rates, number of neurons, and activation functions on model performance. It also evaluated efficiency in both CPU and GPU environments. The objective was to determine the opti-mal configuration by analyzing accuracy, F1-score, and computation time. Two ViT models, ViT-B/16 and ViT-L/16, were tested using the VNPlant-200 dataset, which contains 200 plant species. Thirteen activa-tion functions, multiple learning rates, and neuron configurations were examined. The results showed that the Tanh activation function, combined with a learning rate of 10-4 and 1024 neurons, yielded the best performance on the ViT-B/16 model, achieving an accuracy of 0.9692 and F1-score of 0.9684. Meanwhile, the Hard Tanh activation function, with a learning rate of 10-4 and 256 neurons, delivered the best results on the ViT-L/16 model, achieving an accuracy of 0.9855 and an F1-score of 0.9854. Computational analy-sis showed that ViT-B/16 achieved an average inference time of 0.0159 seconds on a GPU and 0.8902 seconds on a CPU, while ViT-L/16 took 0.0492 seconds on a GPU and 2.8335 seconds on a CPU. These findings highlight the importance of selecting suitable activation functions, learning rates, and neuron configurations to optimize model performance while maintaining computational efficiency.
Copyright (c) 2025 Koko Yuardi, Gusti Ahmad Fanshuri Alfarisy, Ramadhan Paninggalih https://creativecommons.org/licenses/by/4.0
Application of Natural Language Processing and LSTM in A Travel Chatbot for Medan City
The tourism sector plays a vital role in economic growth and regional development. Medan, a major city in North Sumatra, offers rich religious, historical, and cultural attractions. However, fragmented and inconsistent information presents challenges for both tourists and destination managers, often complicating travel planning. To address this issue, this study proposes the development of an AI-based chatbot aimed at enhancing the tourism experience in Medan. By integrating Natural Language Processing (NLP) and Long Short-Term Memory (LSTM), the chatbot is designed to deliver accurate, contextual, and conversational responses tailored to users' tourism-related queries. It was trained on a comprehensive dataset compiled from various sources concerning Medan’s tourism. The training ran over 100 epochs, achieving an accuracy of 84.31% and a loss of 0.7594. Validation testing yielded an accuracy of 77.14% and a loss of 2.4233, indicating good generalization to unseen data. End-to-end testing with 312 queries covering all defined intents resulted in a testing accuracy of 75.64%, confirming the model’s practical effectiveness. The findings demonstrate that the chatbot can accurately interpret user input, classify information, and enhance user interaction. supports the digital transformation of Medan’s tourism sector by introducing a reliable, AI-driven tool for seamless travel planning and engagement.
Copyright (c) 2025 Syarifah Atika, Mahara Bengi, Shekainah Kim A. Sardeng https://creativecommons.org/licenses/by/4.0
Prediction of Telkomsel 4G LTE Card Sales using The K-Nearest Neighbor Algorithm
Accurate sales prediction is a critical challenge in business decision-making, as factors such as data imbalance, outliers, and overfitting may compromise the reliability of predictive models. This study aims to develop a precise model for predicting card sales using the K-Nearest Neighbor (KNN) algorithm and to offer recommendations for improving prediction quality by addressing issues related to data imbalance and overfitting. The KNN algorithm is applied to analyze a card sales dataset, with preprocessing steps that include detecting missing values, handling outliers, and converting the target attribute into a categorical format. The optimal value of k is identified using the elbow method to determine the model's best accuracy. Findings indicate that the KNN model with k = 1 achieves 100% accuracy, though it shows signs of overfitting, which may hinder its generalizability to new data. Handling outliers and transforming data contributed to improving the model's performance. However, to enhance robustness, further testing with different k values and the use of cross-validation are recommended. Moreover, balancing the dataset and incorporating external variables such as promotional activities or market trends could support more reliable future predictions.
Copyright (c) 2025 Alfiana Fontes Martins, Yasinta Oktaviana Legu Rema, Debora Chrisinta, Alejandro Jr. V. Matute, Krisantus Jumarto Tey Seran https://creativecommons.org/licenses/by/4.0
An Intelligent Fuzzy Logic-Controlled IoT System for Efficient Hydroponic Plant Monitoring and Automation
This paper addresses the challenges of optimizing environmental conditions in hydroponic farming by integrating an Intelligent Fuzzy Logic-Controlled IoT System. The research problem lies in the inefficiency of traditional hydroponic monitoring systems, particularly in maintaining ideal conditions for plant growth while minimizing resource waste. This study aims to develop a system that leverages IoT technology and fuzzy logic to monitor and automate hydroponic processes more efficiently. Using sensors, the system continuously tracks key environmental parameters such as temperature, humidity, soil moisture, pH levels, and total dissolved solids (TDS). A fuzzy logic controller (FLC) triggers actions based on predefined rules. During testing, the system showed effective performance—for example, activating fans when temperature (31.2°C) and humidity (60%) indicated a need for cooling, and adjusting nutrient levels when pH (5.8) and TDS (450 ppm) were suboptimal. The system offers practical benefits through real-time adaptation using defuzzification and aggregation, ensuring precise resource control, improving efficiency, and reducing waste. This study highlights the system's potential to support sustainable agriculture by providing scalable solutions that enhance plant growth and optimize resource use, especially for small-scale farmers and urban farming initiatives.
Copyright (c) 2025 Arvita Agus Kurniasari, Pramuditha Shinta Dewi Puspitasari, Lukie Perdanasari, Dia Bitari Mei Yuana, Jumiatun Jumiatun https://creativecommons.org/licenses/by/4.0
Deep Learning Approach for Pneumonia Prediction from X-Rays using A Pretrained Densenet Model
Pneumonia remains a major global health concern, particularly affecting young children and older adults, contributing to significant morbidity and mortality. Traditional diagnostic methods using chest CT scans are time-consuming and prone to errors due to the reliance on manual interpretation. This study investigates the application of DenseNet architectures DenseNet121, DenseNet169, and DenseNet201—for automated pneumonia detection from chest X-ray images. The dataset, obtained from the Guangzhou Women and Children’s Medical Center, consists of 5,216 training images and 624 testing images categorized into normal and pneumonia cases. Data augmentation techniques, including rotation, normalization, and shear, were applied to improve training efficiency. The DenseNet models were pre-trained on ImageNet and fine-tuned by adding fully connected layers with 256 neurons and sigmoid activation. The models were trained for 20 epochs using the Adam optimizer and binary cross-entropy loss function. Performance evaluation revealed that DenseNet201 outperformed the other models, achieving a precision of 0.99 and a recall of 0.61 for normal cases (F1-score of 0.75) and a precision of 0.81 with a recall of 0.99 for pneumonia cases (F1-score of 0.89). These findings demonstrate that DenseNet201 provides a reliable and effective solution for automated pneumonia detection, offering improved diagnostic efficiency and accuracy compared to traditional methods.
Copyright (c) 2025 Ahmad Zein Al Wafi, Febry Putra Rochim, Aisya Fathimah https://creativecommons.org/licenses/by/4.0
Multi-Label Classification for Opinion Mining in The Presidential Election using TF-IDF with NB And SVM
Public opinion plays a crucial role in presidential elections, shaping voter choices and influencing outcomes. Most sentiment analysis studies focus on binary (positive vs. negative) or multiclass (positive, negative, neutral) classification, which limits their ability to capture opinions that express multiple sentiments simultaneously. In presidential elections, a single opinion may support one candidate while criticizing another. This study proposes a MultiLabelBinarizer model to classify candidate and sentiment labels simultaneously—an approach that remains underexplored. The model combines Naïve Bayes (NB) and Support Vector Machine (SVM) for opinion mining using public data and TF-IDF for feature extraction, applying Multinomial and Linear kernels. Performance is evaluated using Accuracy, Precision, Recall, and F1-score. The study is conducted in two stages: developing a multi-label analysis model for presidential candidates and testing the effectiveness of cross-validation. Results show that multi-label classification is effective for both candidate and sentiment categories. Cross-validation with NB and SVM yields high accuracy. NB achieves 0.89 for candidate labels and 0.86 for sentiment labels. SVM performs better, with 0.93 for candidate labels and 0.94 for sentiment labels. While SVM provides higher accuracy, NB offers faster implementation with still competitive results.
Copyright (c) 2025 Ricy Ardiansyah, Herman Yuliansyah, Anton Yudhana https://creativecommons.org/licenses/by/4.0
No-Reference Video Quality Assessment based on The Dover Framework using A Transfer Learning Method
No-reference Video Quality Assessment (VQA) presents a critical challenge in digital multimedia. This study explores video quality measurement using the DOVER framework combined with a transfer learning method. While existing approaches often rely on end-to-end fine-tuning that requires substantial computational resources, this study introduces and validates a more efficient implementation. The model was built using Google Colab and Python, with the KoNViD-1k dataset as the training base. A head-only transfer learning approach was employed, using the DOVER framework as its foundation. This approach addresses a key research gap in resource-efficient no-reference VQA, as many state-of-the-art models remain impractical for real-world deployment due to high computational demands. The training process was conducted over 10 epochs with resource efficiency in mind. The head-only transfer learning technique allows for GPU memory optimization, showing minimal accuracy differences (1%–2%) compared to full end-to-end fine-tuning. Unlike previous studies that compromise performance for efficiency, this approach maintains competitive accuracy while significantly lowering computational costs. The results show that the proposed method delivers accurate and efficient video quality assessments, confirming the potential of the DOVER framework in no-reference VQA. This study highlights a practical balance between computational efficiency and assessment accuracy using transfer learning techniques.
Copyright (c) 2025 Ardhi Muda Ariska, Tubagus Maulana Kusuma https://creativecommons.org/licenses/by/4.0
IoT-MQTT Protocol-Based Water Sensor System to Monitor Citarum River Water Quality using Arduino Uno R4 Wifi
River water quality is critical for sustaining life, necessitating advanced monitoring technologies. This study presents a novel IoT-based water monitoring system using the Arduino Uno R4 WiFi and the MQTT protocol, offering significant improvements in real-time data acquisition, reliability, and accessibility. Unlike conventional systems, this approach uniquely integrates advanced microcontroller capabilities and efficient data transmission to address limitations in accuracy and usability in water quality monitoring. The system measures key indicators, including pH, temperature, total dissolved solids (TDS), and turbidity, and provides real-time updates via a solar-powered web interface. Using an exploratory sequential design, the study developed, calibrated, and tested the system, achieving high accuracy with relative errors of 2.50% for pH, 4.15% for temperature, 4.73% for TDS, and 3.08% for turbidity. Feedback from 59 residents near the Citarum River underscores the system's effectiveness and societal relevance, highlighting its potential to enhance public health, support sustainable environmental management, and set a new standard in water monitoring technology.
Message Queuing Telemetry Transport (MQTT) Protocol
Copyright (c) 2025 Winata Tegar Saputra, Eka Cahya Prima, Muhammad Cahyana Bintang Fajar, Muhammad Fahru Rozi, Nady Artan Destanto, Azzahra Siti Hadjar, Muhamad Nur Yasin Amadudin, Rizki Maulana Ashidiq https://creativecommons.org/licenses/by/4.0
Integration of PM1200 and IoT for Electrical Energy Monitoring with Web-Based Map Visualization
This study aims to integrate the PM1200 device with Internet of Things (IoT) technology using the Modbus protocol to enable real-time monitoring of electrical energy. The current challenge lies in the limited flexibility of energy monitoring, which is typically restricted to local access and lacks map-based visualizations. To address this, the system integrates interactive maps to provide a clearer and more comprehensive view of energy distribution across different locations. This study seeks to offer an effective energy monitoring solution with data visualized through maps on an interactive web platform. The methodology includes reading data from the PM1200 device via the Modbus protocol, transmitting it to an IoT platform using the MQTT protocol, and displaying the data as maps on a web interface. The findings are expected to support effective energy monitoring and enhance energy management efficiency.
Copyright (c) 2025 Syafriyadi Nor, Zaiyan Ahyadi https://creativecommons.org/licenses/by/4.0