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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
 1 
Search results for , issue "Vol. 9 No. 1 (2025)" : 10 Documents clear
IoT-MQTT Protocol-Based Water Sensor System to Monitor Citarum River Water Quality using Arduino Uno R4 Wifi Saputra, Winata Tegar; Prima, Eka Cahya; Fajar, Muhammad Cahyana Bintang; Rozi, Muhammad Fahru; Destanto, Nady Artan; Hadjar, Azzahra Siti; Amadudin, Muhamad Nur Yasin; Ashidiq, Rizki Maulana
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 1 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i1.1335

Abstract

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.
Integration of PM1200 and IoT for Electrical Energy Monitoring with Web-Based Map Visualization Nor, Syafriyadi; Ahyadi, Zaiyan
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 1 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i1.1363

Abstract

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.
No-Reference Video Quality Assessment based on The Dover Framework using A Transfer Learning Method Ariska, Ardhi Muda; Kusuma, Tubagus Maulana
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 1 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i1.1398

Abstract

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.
Multi-Label Classification for Opinion Mining in The Presidential Election using TF-IDF with NB And SVM Ardiansyah, Ricy; Yuliansyah, Herman; Yudhana, Anton
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 1 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i1.1432

Abstract

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.
Deep Learning Approach for Pneumonia Prediction from X-Rays using A Pretrained Densenet Model Wafi, Ahmad Zein Al; Rochim, Febry Putra; Fathimah, Aisya
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 1 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i1.1457

Abstract

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.
An Intelligent Fuzzy Logic-Controlled IoT System for Efficient Hydroponic Plant Monitoring and Automation Kurniasari, Arvita Agus; Puspitasari, Pramuditha Shinta Dewi; Perdanasari, Lukie; Yuana, Dia Bitari Mei; Jumiatun, Jumiatun
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 1 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i1.1475

Abstract

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.
Prediction of Telkomsel 4G LTE Card Sales using The K-Nearest Neighbor Algorithm Martins, Alfiana Fontes; Rema, Yasinta Oktaviana Legu; Chrisinta, Debora; Matute, Alejandro Jr. V.; Seran, Krisantus Jumarto Tey
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 1 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i1.1476

Abstract

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.
Application of Natural Language Processing and LSTM in A Travel Chatbot for Medan City Atika, Syarifah; Bengi, Mahara; Sardeng, Shekainah Kim A.
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 1 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i1.1481

Abstract

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.
Design and Performance Evaluation of A Portable Low-Head Pico-Hydro System using A Rewound Axial Generator for Rural Energy Aripriharta, Aripriharta; Nibrosoma, Ahmad Dhaffa; Afandi, Arif Nur; Faiz, Mohamad Rodhi; Rahmadhani, Nur Aini Syafrina; Bagaskoro, Muhammad Cahyo; Rosmin, Norzanah
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 1 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i1.1489

Abstract

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.
Imbalanced Text Classification on Tourism Reviews using Ada-boost Naïve Bayes Suzanti, Ika Oktavia; Kamil, Fajrul Ihsan; Rochman, Eka Mala Sari; Azis, Huzain; Suni, Alfa Faridh; Rachman, Fika Hastarita; Solihin, Firdaus
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 1 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i1.1496

Abstract

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%.

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