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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 223 Documents
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Diabetic Retinopathy Severity Level Detection Using Convolution Neural Network Firmansyah, Achmad Dinofaldi; Kahar, Saliyah Binti; Fitri, Zilvanhisna Emka
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 8 No. 1 (2024)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v8i1.1112

Abstract

Diabetic retinopathy is a common complication of diabetes mellitus, leading to damage and blockage of retinal blood vessels. Early and accurate detection of diabetic retinopathy severity levels is crucial for timely treatment and prevention of blindness. Diagnostic methods rely on manual examination and human interpretation, resulting in slower and less efficient treatment processes. As a branch of artificial intelligence, computer vision offers a potential solution to analyze retinal images quickly and accurately. The developed system employs image processing techniques and a CNN-based classification model to detect and classify the severity levels of diabetic retinopathy. By providing an automated and efficient approach, the system aims to assist doctors and optometrists in making informed decisions and reducing subjectivity in diagnosis. Early detection through this system can facilitate prompt treatment and improve patient outcomes. The developed system achieves promising results through experimentation and testing with various datasets, with accuracy ranging from 80% to 97%. This project's integration of artificial intelligence, machine learning, and image processing technologies demonstrates their potential in healthcare applications, particularly in diabetic retinopathy diagnosis.
K-Means Clustering Method For Customer Segmentation Based On Potential Purchases Baiq Nikum Yulisasih; Herman, Herman; Sunardi, Sunardi
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 8 No. 1 (2024)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v8i1.1137

Abstract

The rapid growth in customer data has driven companies to develop smarter and more effective marketing strategies. One efficient approach is customer segmentation, which involves dividing a market or group of customers into smaller segments based on similar characteristics or behaviors. Customer segmentation improves understanding of customer needs, preferences, and behavior. This study uses customer segmentation based on purchase potential at Fast Moving Consumer Goods (FMCG). Analyzing potential purchases can help identify market opportunities, implement more effective pricing, target promotions, manage stock and distribution, and develop new products to enhance customer satisfaction. The most commonly used segmentation method is the K-Means Clustering algorithm, which groups data into homogeneous clusters. This study aims to segment customers based on potential purchases using the K-Means Clustering method. The customer dataset in FMCG stores was divided into three clusters using seven attributes: Sex, Marital Status, Age, Education, Income, Occupation, and Settlement Size. The results, calculated in Microsoft Excel, concluded after four iterations with three clusters: k1 (Cluster 1) with 535 customers having low purchase potential, k2 (Cluster 2) with 685 customers having high purchase potential, and k3 (Cluster 3) with 7810 customers having medium purchase potential.
Analysis of Hybrid Learning Sentiment among Information Systems Students using The Naïve Bayes Classifier Indra, Dolly; Ramdaniah, Ramdaniah; Sukur, Widianti
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.v8i2.1144

Abstract

Hybrid learning, which combines online and face-to-face instruction, has gained significant attention. Particularly in the Faculty of Computer Science, student engagement in hybrid learning is a central concern that arises during implementation. Hybrid, or blended learning, integrates various teaching methods, such as face-to-face, computer-based, and mobile learning, and offers advantages by reducing the time required for meetings and information delivery. Sentiment analysis, a branch of text mining, aims to determine public opinion or sentiment on topics, events, or issues. This study surveyed 112 Information Systems students using an online questionnaire to assess their responses to hybrid learning, classified as positive, negative, or neutral using the Naïve Bayes classifier. The research stages included data collection, preprocessing, Naïve Bayes model training, model evaluation, and sentiment analysis. The study aimed to analyze hybrid learning’s impact on students' learning experiences and assess the accuracy of the Naïve Bayes method in classifying sentiments regarding this impact. The results indicated that the initial test had an accuracy of 60.87% without using the SMOTE up-sampling operator, while the second test achieved 80.65% accuracy with the operator.
Analysis of Digital Television Signal Reception in Combined Transmitter Antenna Systems Zahra, Hana Fatimah; Setyowati, Endah; Putri, Dewi Indriati Hadi
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.v8i2.1152

Abstract

The Indonesian broadcasting sector has undergone significant transformation with the implementation of the Analogue Switch Off (ASO) program. By July 2023, approximately 97.23% of television stations in Indonesia had migrated to digital broadcasting. However, this figure does not reflect an equitable distribution of normal broadcasts. Data from Transmission Station X indicate that 6.77% of broadcasts are blank, 12.24% experience freezing, and only 80.99% are classified as normal for channel X in the Jabodetabek region. These statistics suggest that the primary objective of the ASO program—providing an enhanced television viewing experience—has not yet been universally achieved. Therefore, efforts are required to ensure the equitable distribution of normal broadcasts. Transitioning from a lower transmitter antenna system to a combined transmitter antenna system is proposed as a potential solution. This study evaluates the Modulation Error Ratio (MER) values in the Jabodetabek region when channel X uses a combined transmitter antenna system. Measurements, conducted using the drive test method, reveal that 99.703% of MER data are above the threshold (normal broadcasts), 0.042% are at the threshold (freeze broadcasts), and 0.255% are below the threshold (blank broadcasts). These results demonstrate that adopting a combined transmitter antenna system can help address the uneven distribution of normal broadcasts in the Jabodetabek region.
Systematic Literature Review: Identifying Key Variables and Measuring Maximum Loan Limits Fadillah, Algies Rifkha; Fauzan, Mohamad Nurkamal
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.v8i2.1156

Abstract

This systematic literature review aims to identify key variables and measurement methods for determining maximum credit loan limits, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The complexity of setting an optimal credit limit to manage credit risk effectively presents a significant challenge. Establishing an efficient maximum loan limit is essential to mitigate credit risk, as an overly high limit increases default potential, while an excessively low limit restricts the financial institution's growth. This study identifies key variables and measurement methods, including Machine Learning techniques, Neural Networks, and traditional statistical approaches. Machine Learning models, such as Random Forest and Gradient Boosting, often surpass traditional methods in handling large, unstructured datasets due to their capacity for modeling complex, non-linear relationships. Conversely, traditional methods like logistic regression may be more suitable for smaller datasets, offering better interpretability and ease of use. The results indicate that systematic variable identification and the use of appropriate measurement methods can enable financial institutions to manage credit loan risk more effectively, supporting the development of sound credit policies.
Application of Faster R-CNN Deep Learning Method for Rice Plant Disease Detection Pujiono, Halim; Vitianingsih, Anik Vega; Kacung, Slamet; Lidya Maukar, Anastasia; Fitri Ana Wati, Seftin
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.v8i2.1165

Abstract

Plant diseases, particularly in staple crops like rice, significantly affect the stability of rice production in Indonesia. Crop failure caused by rice plant diseases present a critical challenge for farmers.  Early diagnosis is crucial for preventing and managing rice diseases, as it facilitates more effective preventive measures, reduces yield losses, and boosts overall agricultural production. This study aims to apply the Faster Region Convolutional Neural Network (Faster R-CNN), a deep learning approach, to detect rice plant diseases. The Grid Search method was employed as a hyperparameter tuning technique to identify the optimal parameter combination for enhancing algorithm performance. Experimental results demonstrate the model's performance, achieving an accuracy rate of 88%, recall and precision of 100%, and an F1 Score of 93%. These findings indicate that the Faster R-CNN method effectively recognizes and classifies rice plant diseases with a high degree of accuracy.
Enhancing Image Quality With Deep Learning: Techniques And Applications Zangana, Hewa Majeed; Mustafa, Firas Mahmood; Mohammed, Ayaz Khalid; Omar, Naaman
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.v8i2.1242

Abstract

The emergence of deep learning has transformed numerous fields, particularly image processing, where it has substantially enhanced image quality. This paper provides a structured overview of the objectives, methods, results, and conclusions of deep learning techniques for image enhancement. It examines deep learning methodologies and their applications in improving image quality across diverse domains. The discussion includes state-of-the-art algorithms such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Autoencoders, highlighting their applications in medical imaging, photography, and remote sensing. These methods have demonstrated notable impacts, including noise reduction, resolution enhancement, and contrast improvement. Despite its significant promise, deep learning faces challenges such as computational complexity and the need for large annotated datasets. outlines future research directions to overcome these limitations and further advance deep learning's potential in image enhancement.
Optimization Of Solar Panel Usage In Grid-Connected Hybrid Energy Systems Using Fuzzy Method Maizana, Dina; Muhathir, Muhathir; Satria, Habib; Mungkin, Moranaim; Siregar, Muhammad Fadlan; Yahya, Yanawati Binti
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.v8i2.1278

Abstract

The hybrid grid-connected power generation system combines solar power, wind power, and the PLN grid to meet the electricity demands of facilities such as schools, laboratories, mosques, and kindergartens at MTs Parmiyatu Wassa'adah School. Due to insufficient wind speed below the turbine's operational threshold, wind turbines cannot contribute to electricity generation, making solar power the primary energy source. Solar power capacity is crucial for meeting the electricity needs of these facilities. This study applies the Fuzzy method to analyze the optimal utilization of solar panels in a grid-connected hybrid system for electricity demand. Simulation results indicate three levels of solar panel utilization, with the most optimal performance achieved when school electricity usage is low, and additional loads are minimized.
Optimal Power Flow using An Optimally Tuned Pattern Search Algorithm Budiman, Firmansyah Nur; Hidayat, Taufal; Uswarman, Rudi
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.v8i2.1290

Abstract

Optimal power flow (OPF) is a critical optimization application in power system planning and operation. Numerous studies employ metaheuristic techniques to address OPF problems of varying complexity. However, these techniques often suffer from slow convergence due to their dependence on the quality of initial solutions. To overcome this limitation, initial solutions must be optimally tuned to achieve good outcomes with faster convergence. This paper proposes an optimally tuned pattern search (OPS) algorithm to solve OPF problems in medium and large power systems. The tuning process, performed using the classical interior point method (IPM), provides optimal initial control variable values for the standard pattern search (PS) algorithm. The proposed technique is applied to three test systems: IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems. The OPF problem is formulated to minimize four objectives: total active power loss, total generator fuel cost, total generator emission, and total deviation in load bus voltage magnitude. The performance of the OPS algorithm is evaluated based on objective function values and computation times and is compared with IPM and two popular metaheuristic techniques, particle swarm optimization (PSO) and genetic algorithm (GA). Results indicate that the OPS algorithm's performance varies across test systems but generally balances optimization performance with computational efficiency.
Design of A Thermoelectric Generator for Battery Charging using Heat from A Steam Iron Base Sundari, Delta; Manfaluthy, Mauludi; Pratama, Legenda Prameswono; Dionova, Brainvendra Widi; Vresdian, Devan Junesco; Putri, Arisa Olivia; Al-Humairi, Safaa Najah Sahud; Mohammed, M. N.
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.v8i2.1307

Abstract

This study explores an alternative method of generating electrical energy using a thermoelectric generator that utilizes heat from the soleplate of a steam iron and six thermoelectric units connected in series. Based on the Seebeck effect, the thermoelectric modules convert the temperature difference into voltage. An increase in the heat source temperature leads to higher voltage production by the series-connected thermoelectric modules, although the electrical power output depends on the connected load. The power generator design includes thermoelectric modules, a buck-boost converter, an 18650 lithium-ion battery, and a 5-watt, 12-volt DC lamp. The study addresses key aspects such as the impact of temperature on power output in series-connected and parallel-connected thermoelectric circuits, and the efficient conversion of heat from the steam iron soleplate into electrical energy. The research objectives are threefold: to determine power and temperature values for series-connected thermoelectric circuits, to evaluate power and temperature values for parallel-connected thermoelectric circuits, and to utilize heat from the steam iron soleplate as a thermoelectric heat source for generating electrical energy. Testing involved a buck-boost converter connected to a battery, producing 12.35 volts with a temperature difference of 49°C. Design enhancements, such as integrating heatsinks or coolers on the cold side of the modules to maintain a significant temperature differential, are critical for optimizing performance.

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