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TELKOMNIKA (Telecommunication Computing Electronics and Control)
Published by Universitas Ahmad Dahlan
ISSN : 16936930     EISSN : 23029293     DOI : 10.12928
Core Subject : Science,
Computer Science & IT
Submitted papers are evaluated by anonymous referees by single blind peer review for contribution, originality, relevance, and presentation. The Editor shall inform you of the results of the review as soon as possible, hopefully in 10 weeks. Please notice that because of the great number of submissions that TELKOMNIKA has received during the last few months the duration of the review process can be up to 14 weeks. Communication Engineering, Computer Network and System Engineering, Computer Science and Information System, Machine Learning, AI and Soft Computing, Signal, Image and Video Processing, Electronics Engineering, Electrical Power Engineering, Power Electronics and Drives, Instrumentation and Control Engineering, Internet of Things (IoT)
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 3,452 Documents
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Low-cost ESP32-based sound data acquisition system with MATLAB integration for real-time noise monitoring Reymark-John Macapanas; Adrian P. Galido; Apple Rose B. Alce
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 2: April 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i2.27557

Abstract

This study presents the design and implementation of a low-cost ESP32-based sound data acquisition system (SDAS) for real-time noise monitoring. The system integrates a micro-electro-mechanical systems (MEMS) microphone for accurate acoustic data capture, an ESP-WROOM-32 microcontroller for signal processing and wireless data transmission, and MATLAB for real-time visualization and analysis. Designed and simulated in KiCAD 8.0, the SDAS includes a microSD module for local data backup and offline analysis. The system was tested in four indoor locations within Mindanao State University – Iligan Institute of Technology, recording mean noise levels ranging from 14.2 dB in laboratory environments to 32.1 dB in classrooms, with corresponding standard deviations of 1.2–7.0 dB. Expert evaluation from eight assessors confirmed the system’s usability, data reliability, and robustness. The system demonstrates effective monitoring for both quiet and dynamic settings. Limitations include single-node configuration, indoor-only testing, and MATLAB-based USB data transfer. Despite these, the proposed SDAS provides a scalable and reproducible model for smart campus and urban environmental monitoring, supporting sustainable development goals (SDG) 3, 9, and 11.
Progressive energy management technique for smart load control Timilehin Sanni; Mobolade Olawale; Amarachi Attah; Anthony Adoghe; Ayokunle Awelewa
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 5: October 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i5.25861

Abstract

Electricity demand is rapidly increasing in many countries especially because of the increase in smart appliances, smart cities, and communities. The use of electricity ranges from agricultural load, to commercial and industrial load, and also residential load which account for the largest contributor to the increase in peak demand. Thus, residential consumers play a massive role in the national demand for electrical energy and power. The increased demand puts increasing pressure on the energy suppliers who force consumers to cope with short peaks. The need arises for an energy management technique to reduce energy consumption by allowing for consumer load control. The method explores using a Raspberry Pi to collect and evaluate the energy consumed over time and allow consumers access to directly control their home appliances. A more accurate load curve is deduced using the new readings gotten from the end-users. The data obtained from the proposed system is sent to a progressive web application that allows users manage their energy consumption.
Driving cycle development methods: case study in Malaysia Nurru Anida Ibrahim; Siti Norbakyah Jabar; Arunkumar Subramaniam; Wan Nural Jawahir Wan Yussof; Salisa Abdul Rahman
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 2: April 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i2.24265

Abstract

Over the years, many models have been created to estimate pollution inventories and fuel usage. These models can be divided into two types: travel-based and fuel-based. One of the most used travel-based models for estimating emission inventories is driving cycles. It can be used for a variety of different things, such as establishing pollution regulations, traffic control, and calculating journey time. For these goals, researchers have previously attempted to use easily available, well-established driving cycles. In many ways, however, the local environment differs greatly from that of the driving cycle’s genesis. As a result, these cycles’ applications have failed to provide high-quality results. This research aims to analyse the various approaches utilised for driving cycle construction in various locations of Malaysia under varied operational situations.
Business intelligence system model to measure the performance of lecturers’ scientific publications Miwan Kurniawan Hidayat; Dedy Sugiarto; Rina Fitriana; Yun-Chia Liang
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 4: August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i4.26221

Abstract

Scientific publication data is sourced from the SINTA website to measure the performance of journals, institutions, and researchers in Indonesia. Accessing and analyzing data for institutional needs is restricted, and lecturer development patterns based on lecturer characteristics remain untapped. The study aims to analyze and design business intelligence system models to measure the performance of scientific publications using dimensional models, clustering, on-line analytical processing (OLAP), and prototyping. Research methods are carried out through data and information needs analysis, data warehouse design, data mining and OLAP application, business intelligence system development, and system evaluation. The resulting dimensional models are the researcher index model, the researcher score model, the publication article model, and the research subject model. Measurements of data size and processing time show that the star schema has data of 336 KB and a processing time of 0.00554 seconds, is the best model compared to the snowflake’s schema, which has data of 368 KB and a processing time of 0.00611 seconds. Davies–Bouldin Index (DBI) measurements show the best clustering performance is the X-means algorithm with K as many as 5 clusters (Kmin=3, Kmax=5) and a DBI value of 0.537040.
HAR-MI method for multi-class imbalanced datasets H. Hartono; Yeni Risyani; Erianto Ongko; Dahlan Abdullah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14818

Abstract

Research on multi-class imbalance from a number of researchers faces obstacles in the form of poor data diversity and a large number of classifiers. The Hybrid Approach Redefinition-Multiclass Imbalance (HAR-MI) method is a Hybrid Ensembles method which is the development of the Hybrid Approach Redefinion (HAR) method. This study has compared the results obtained with the Dynamic Ensemble Selection-Multiclass Imbalance (DES-MI) method in handling multiclass imbalance. In the HAR-MI Method, the preprocessing stage was carried out using the random balance ensembles method and dynamic ensemble selection to produce a candidate ensemble and the processing stages was carried out using different contribution sampling and dynamic ensemble selection to produce a candidate ensemble. This research has been conducted by using multi-class imbalance datasets sourced from the KEEL Repository. The results show that the HAR-MI method can overcome multi-class imbalance with better data diversity, smaller number of classifiers, and better classifier performance compared to a DES-MI method. These results were tested with a Wilcoxon signed-rank statistical test which showed that the superiority of the HAR-MI method with respect to DES-MI method.
Classification of melanoma skin cancer using deep learning approach Maha Ali Hussien; Abbas H. Hassin Alasadi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 1: February 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i1.25428

Abstract

In this study, the authors propose a deep learning (DL) approach for classifying melanoma skin cancer (MSC). They introduce a convolution neural network (CNN) model that consists of 27 layers, which are carefully designed to extract features from skin lesion images and classify them into melanoma and non-melanoma classes. The proposed CNN model comprises multiple convolution layers that apply filters to the input image to extract features such as edges, shapes, and patterns. Batch normalization layers that normalize the output of the convolution layers to accelerate the learning process and prevent overfitting follow these convolution layers. The performance of the proposed CNN model was evaluated on publicly available datasets of skin lesion images, and the findings showed that it outperformed several state-of-the-art methods for melanoma classification. The authors also conducted ablation studies to analyze each layer’s contribution to the model’s overall performance. The proposed DL approach has the potential to assist dermatologists in the early detection of MSC, which can lead to treatment that is more effective and improves patient outcomes. It also demonstrates the effectiveness of DL techniques for medical image analysis and highlights the importance of carefully designing and optimizing CNN models for high performance. The accuracy of the proposed system is 99.99%.
Hybrid Kolmogorov-Arnold and convolutional neural network model for single-lead electrocardiogram classification Marlin Ramadhan Baidillah; Pratondo Busono; I Made Astawa; Syaeful Karim; Ronny Febryarto; I Putu Ananta Yogiswara; Chaerul Achmad; Nashrullah Taufik
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 5: October 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i5.26735

Abstract

This study proposes a hybrid Kolmogorov-Arnold networks (KANs) and convolutional neural networks (CNN) to classify electrocardiogram (ECG) signal abnormalities in one lead ECG data of wearable telemedicine. The hybrid model combines CNN to extract hierarchical features from sequential data and KANs to model non-linear relationships with fewer parameters as an efficient classification. The study explores the model’s capacity to balance accuracy, computational efficiency, and memory usage as critical factors for real-time health monitoring in resource-constrained environments on the single-lead MIT-Beth Israel hospital (MIT-BIH) Supraventricular Arrhythmia database with five different class labels. For comparison, standalone CNN and KAN models were also trained on the same balanced dataset. The CNN model achieved an accuracy of 96.62%, precision of 96.81%, and recall of 96.53%. The KAN model, while computationally efficient, performed less effectively, with an accuracy of 94.15%, precision of 95.01%, and recall of 92.57%. In contrast, our hybrid KAN-CNN model outperformed both, attaining an accuracy of 97.53%, precision of 97.66%, recall of 97.40%, and a low loss of 0.0840. The study also explores the impact of quantization and compression on model performance, revealing that both CNN and Hybrid KAN-CNN models retained high accuracy post-quantization, whereas the KAN model exhibited a more significant drop in performance.
Sentiment analysis of public response to measurable fishing capture policy using LDA and LSTM methods Januar Agung Wicaksono; Retno Kusumaningrum; Eko Sediyono
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 6: December 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i6.25935

Abstract

Illegal, unreported, and unregulated (IUU) fishing poses a significant threat by depleting fish stocks, damaging marine ecosystems, jeopardizing economic livelihoods, and undermining long-term environmental sustainability. To address this, the government has implemented a public policy of measured fishing within the blue economy framework. Given the involvement of numerous stakeholders, it is crucial for the government to gauge public sentiment through tweets on social media platforms to evaluate and refine the policy’s implementation for greater effectiveness. While the long short-term memory (LSTM) method for sentiment analysis is adept at handling text sequences and context, it struggles with capturing contextual semantic correlations. Conversely, the latent Dirichlet allocation (LDA) method excels in identifying these correlations and uncovering dominant topics. This study shows that integrating LDA for topic modeling with LSTM for sentiment analysis enhances overall performance, providing more accurate and comprehensive insights into public responses and identifying key topics discussed in social media tweets.
Robot operating system based autonomous navigation platform with human robot interaction Rajesh Kannan Megalingam; Vignesh S. Naick; Manaswini Motheram; Jahnavi Yannam; Nikhil Chowdary Gutlapalli; Vinu Sivanantham
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 3: June 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i3.24257

Abstract

In emerging technologies, indoor service robots are playing a vital role for people who are physically challenged and visually impaired. The service robots are efficient and beneficial for people to overcome the challenges faced during their regular chores. This paper proposes the implementation of autonomous navigation platforms with human-robot interaction which can be used in service robots to avoid the difficulties faced in daily activities. We used the robot operating system (ROS) framework for the implementation of algorithms used in auto navigation, speech processing and recognition, and object detection and recognition. A suitable robot model was designed and tested in the Gazebo environment to evaluate the algorithms. The confusion matrix that was created from 125 different cases points to the decent correctness of the model.
Water quality monitoring using soft computing techniques in Udupi Region, Karnataka, India Krishnamurthy Nayak; Sumukha K. Nayak; Supreetha Balavalikar Shivaram
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 5: October 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i5.26228

Abstract

A monitoring of water quality index parameters using soft computing technology is the current research focus as the main challenge of which is to design a soft computing algorithm with the highest accuracy and less computation time. For the secondary dataset obtained by the government database, this research proposes a water quality prediction and classification method based on decision tree algorithm. The comparative analysis is made for the different highest accuracy algorithms like decision tree algorithm with support vector machine (SVM), k-nearest neighbour (KNN) classifier, linear discriminant analysis, Naïve Bayes classifier and logistic regression. Decision tree algorithm had the highest accuracy compared to other algorithms. The KNN algorithm used as clustering algorithm to plot the two classes good and bad. The trend analysis of the water quality is performed with various water quality parameters like pH, fluoride and total dissolved solids (TDS) test results are plotted and observed for the variations of the values with respect to increase in time. The performance is measured with statistical indices and the prediction accuracy of 0.99 and mean squared error of 0.05. The results prove that the KNN algorithm found to be better for clustering purposes.

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