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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.
Arjuna Subject : -
Articles 233 Documents
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Optimizing Goods Placement in Logistics Transportation using Machine Learning Algorithms based on Delivery Data Syawab, Moh Husnus; Arief, Yunifa Miftachul; Nugroho, Fresy; Kusumawati, Ririen; Crysdian, Cahyo; Almais, Agung Teguh Wibowo
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 8 No. 2 (2024)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v8i2.1321

Abstract

This study addresses the challenge of predicting the optimal placement of goods for expeditionary transportation. Efficient placement is crucial to ensure that goods are transported in a manner that maximizes space and minimizes the risk of damage. This study aims to develop a prediction system using the K-Nearest Neighbor (KNN) method, which is based on expert data from expedition vehicles. To evaluate the effectiveness of the KNN method, the researcher compared it with the Support Vector Machine (SVM) method. By doing so, they sought to determine which method delivers more accurate predictions for the optimal placement of goods. The test results revealed that the KNN method outperformed SVM, achieving a higher accuracy of 95.97% compared to SVM's 92.85%. Additionally, KNN demonstrated a lower Root Mean Square Error (RMSE) of 0.18, indicating more precise predictions, while SVM had an RMSE of 0.271. These findings suggest that KNN is the more effective method for predicting the optimal placement of goods in expeditionary transportation.
Disease Detection in Tropical Tomato Leaves via Machine Learning Models Kommey, Benjamin; Tamakloe, Elvis; Opoku, Daniel; Crispin, Tibilla; Danquah, Jeffrey
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 8 No. 2 (2024)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v8i2.1340

Abstract

This study addresses the significant threat of tomato diseases to production in Ghana, which has led to substantial yield and quality losses, adversely affecting the livelihoods of local farmers and the availability of this essential dietary staple. Traditional disease identification methods are time-consuming and rely on subjective visual inspections, hindering early detection and control. This study develops a machine learning model capable of accurately identifying tomato plant diseases through image processing. The methodology involves processing a dataset of tomato plant images displaying healthy and diseased symptoms. The proposed model employs the YOLOv5 architecture and is deployed on a mobile platform for accessible disease identification. The model achieved a validation mAP@.5 of 0.715, demonstrating strong performance during live, on-site testing. This system provides a swift, accurate, and automated solution for detecting tomato diseases, supporting the sustainability of tomato production in Ghana.
Radial Basis Function Model for Obesity Classification Based on Lifestyle and Physical Condition Razak, Farhan Radhiansyah; Biddinika, Muhammad Kunta; Yuliansyah, Herman
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 8 No. 2 (2024)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v8i2.1347

Abstract

Obesity is a chronic condition affecting millions worldwide, influenced by genetic predispositions, environmental factors, lifestyle habits, and excessive caloric intake surpassing energy expenditure. widespread prevalence, existing studies lack a comprehensive exploration of classification models that effectively address the complex interplay between lifestyle and physical attributes. This study tackles the absence of an optimal machine learning model for accurately classifying obesity based on these multifaceted factors. To address this gap, the study evaluates the performance of three machine learning algorithms: Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel, Naïve Bayes, and K-Nearest Neighbor (KNN). The primary objectives are to identify the most accurate classification approach, analyze the strengths of these algorithms, and highlight the importance of lifestyle and physical attributes in obesity prediction. Experimental findings show that SVM with RBF kernel achieves the highest accuracy at 89%, surpassing the performance of the other models. This study advances the field of obesity classification by offering a detailed comparative analysis of machine learning algorithms and underscoring the critical role of integrating lifestyle and physical factors into predictive modeling.
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.

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