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Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer
Published by Politeknik Negeri Banjarmasin
ISSN : 25983245     EISSN : 25983288     DOI : -
Core Subject : Science, Engineering,
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
We are the Editor of Jurnal ELTIKOM, invites Mr. / Ms Lecturer, researcher and practitioner to be able to publish your paper on topics covering Electrical Engineering, Electronics Engineering, Telecommunications Engineering, Computer Engineering, Information Technology.
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Articles 10 Documents
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Search results for , issue "Vol. 9 No. 2 (2025)" : 10 Documents clear
Classification of Diabetes Mellitus using Decision Trees Hapsari, Rinci Kembang; Salim, Abdullah Harits; Oktavian, Leonardo Fahsi; Fitra, Aldy Ramadhan
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1461

Abstract

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.
A VGG16 CNN-based Method for Multiclass Lung Cancer Classification using CT Imaging Sari, Sekar; Muniroh, Muna Afdi; Apriandy, Kevin Ilham
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1483

Abstract

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.
Smart Hydroponic Nutrient Monitoring and Control System using Fuzzy Logic and IoT Alimussadad, Muhammad; Lestari, Dyah; Utomo, Wahyu Mulyo
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1494

Abstract

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.
Fine-Grained Plant Classification using Vision Transformers with Optimized MLP Heads Yuardi, Koko; Alfarisy, Gusti Ahmad Fanshuri; Ramadhan Paninggalih
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1500

Abstract

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.
Efficient Strategy for Distribution Transformer Replacement: A Study on Replacement Methods in Power Systems Rahmawati, Nur Asmi
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1526

Abstract

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.
K-Nearest Neighbor Algorithm for Intelligent Monitoring and Control System Integration in Renewable Energy Applications Junus, Mochammad; Fa‘izah, Laily Nur; Noor, Mohd; Putra, Indra Lukmana
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1565

Abstract

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.
Explainable AI-Driven Convolution Neural Network for Quality Grading of Soybean Seeds Putri, Valencia Sefiana; Basuki, Setio
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1566

Abstract

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.
Wound Depth Measurement System in Forensic Cases using Image Processing and Machine Learning Wahyuni, Elvira Sukma; Ahnaf, Kern Cesarean; Firdaus, Firdaus; Abdul-Kadir, Nurul Ashikin; Zakaria, Nor Aini; Wiraagni, Idha Arfianti; Kadarmo, Diwangkoro Aji
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1636

Abstract

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.
The Synergy of Blockchain and Cybersecurity: Building Trust in Digital Environments Zangana, Hewa Majeed; Sallow, Zina Bibo; Mustafa, Firas Mahmood; Husain, Mamo Muhamad
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1701

Abstract

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
Optimization Model for Fake Account Detection on Twitter (X) Social Media using Feature Engineering and Machine Learning Approaches Perimawati, Ni Nyoman Eny; Huizen, Roy Rudolf; Hostiadi, Dandy Pramana
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1727

Abstract

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.

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