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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
 276   277   278   279   280 
Integration of IoT and chatbot for aquaculture with natural language processing M. Udin Harun Al-Rasyid; Sritrusta Sukaridhoto; Muhammad Iskandar Dzulqornain; Ahmad Rifai
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.v18i2.14788

Abstract

The development of internet of things (IoT) technology is very fast lately. One sector that can be implemented by IoT technology is the aquaculture sector. One important factor in the success of aquaculture is a good and controlled water quality condition. But the problem for the traditional aquaculture farmers is to monitor and increase the water quality quickly and efficiently. To resolve the above-mentioned problem, this paper proposes a real-time monitoring system for aquaculture and supported with chatbot assistant to facilitate the user. This system was composed of IoT system, cloud system, and chatbot system. The proposed system consists of 7 main modules: smart sensors, smart aeration system, local network system, cloud computing system, client visualization data, chatbot system, and solar powered system. The smart aeration system consists of NodeMCU, relay, and aerator. The smart sensors consist of several sensors such as dissolved oxygen, pH, temperature, and water level sensor. Natural language processing is implemented to build the chatbot system. By combining text mining processing with naive Bayes algorithm, the result shows the very good performance with high precision and recall for each class to monitor the quality of water in aquaculture sector.
Fall incidence prediction system for elderly people based on IoT and classification techniques Narayanan Essakipillai; Jayashree Ramakrishnan
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.v22i1.25248

Abstract

Health monitoring systems based on the internet of things (IoT) improve patient well-being and reduce mortality risks. Machine learning techniques are most helpful in early fall prediction and detection. In this paper, fall prediction analysis and decision-making are done with existing benchmark clinical records. Classification techniques are incorporated to track the consistency and precision of data acquired by the IoT-based remote health monitoring for elderly people, especially those who are living alone. This work undertakes two approaches to early predicting a patient’s acute illness. The first approach has analyzed the existing benchmark patient activity data with different features. This approach builds the classification model for fall incidence with the help of machine learning models. In second approach, we collect real-time sensor data such as blood pressure and heart rate from IoT sensor gadgets which are transmitted to the prediction model for early prediction. Experimental results prove that the random forest (RF) classifiers and XGBoost provides the maximum accuracy.
Enhancing realism in hand-drawn human sketches through conditional generative adversarial network Imran Ulla Khan; Depa Ramachandraiah Kumar Raja
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.v23i4.26856

Abstract

This research focuses on enhancing the realism of hand drawn human sketches through the use of conditional generative adversarial networks (cGAN). Addressing the challenge of translating rudimentary sketches into highfidelity images, by leveraging the capability of deep learning algorithms such as cGANs. This is particularly significant for applications in law enforcement, where accurate facial reconstruction from eyewitness sketches is crucial. Our research utilizes the Chinese University of Hang Kong Face Sketches (CUFS) dataset, a paired dataset of hand drawn human faces sketches and their corresponding realistic images to train the cGAN model. Generator network produces realistic images based on input sketches, where as discriminator network evaluates authenticity of these generated images compared to the real ones. The study involves careful preprocessing of the dataset, including normalization and augmentation, to ensure optimal training conditions. The model performance assessed through both quantitative metrics, such as frechet inception distance (FID), and qualitative evaluations, including visual inspection of generated images. The potential applications of this research extend to various fields, such as agencies of law enforcement for finding suspects and locating missing persons. Future work exploring advanced techniques for further realism, and evaluating the model’s performance across diverse datasets.
MILA: Low-cost BCI framework for acquiring EEG data with IoT Rolly Maulana Awangga; Syafrial Fachri Pane; Dzikri Ahmad Ghifari; Tri Angga Dio Simamora; Mochamad Yusuf Asyhari
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.v18i2.14884

Abstract

The brain is a vital organ in the human body that acts as the center of the human nervous system. Brain-computer interface (BCI) uses electroencephalography (EEG) signals as information on brain activity. Hospitals usually use EEG as a diagnosis of brain disease. Combining EEG as part of IoT (Internet of Things) with high mobility is challenging research. This research tries to make a low-cost BCI framework for motorcycle riders. Analysis of brain activity from EEG data when motorcycle riders turn left or turn right. Therefore, the method of further installation must produce the right features to obtain precise and accurate brainwave characteristics from EEG signals. This research uses the concept of IoT with software engineering to recording human brain waves so that it becomes a practical device for the wearer. The purpose of this study is to create a low-cost BCI framework for obtaining EEG data.
Comparative analysis of call admission control techniques for efficient resource utilization and QoS in IEEE 802.16e network Ifeanyi Chinaeke-Ogbuka; Bonaventure Ekengwu; Christantus Nnamani; Joy Eneh
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 2: April 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i2.25591

Abstract

In order to provide solution to limited network resources in heterogeneous wireless networks supporting different applications with distinct quality of service (QoS) requirements, call admission control (CAC) schemes are implemented. This work is aimed at investigating three mostpopular CAC schemes employedin mobile worldwide interoperability for microwave access (WiMAX), namely dynamic CAC with bandwidth reservation (DCACBR), QoS-aware CAC (QoSACAC), and QoS guaranteed CAC (QoSGCAC) to identify their shortfalls which will form the focus of future research. A general platform is developed and simulated. The simulation was based on the following KPIs: blocking rate, dropping rate, and throughput for new and handoff connections. Simulation results for new connection shows that QoSGCAC outperforms scheme DCACBR and QoSACAC, having 26.9% and 8.56% improvements in throughput and 63.11% and 24.17% in blocking rate respectively. For handoff connection, QoSACAC showed the best performance having 13.25% and 47.84% improvements in throughput and 6.8% and 49.3% in blocking rate as compared to the DCACBR and QoSGCAC, respectively. Result analysis shows that QoSACAC has the best performance however, it admits new connections and degrade existing connections but failed to consider the delay-intolerant service classes. It is recommended that the QoSACAC be further improved.
Performance of piezoelectric energy harvesters at various angles Adhes Gamayel; Mohamad Zaenudin; Djoko Setyo Widodo
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 6: December 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i6.26860

Abstract

Piezoelectric materials are capable of generating electricity in response to mechanical strain, making them suitable for energy harvesting applications. Piezoelectric energy harvesters (PEHs) are promising alternatives for renewable energy generation, particularly because mechanical strain can be induced in various ways, including utilizing wind flows. This study investigates the performance of a PEH integrated with a laboratory-scale wind-driven micro-windmill. The experiment is carried out by rotating blades of the windmill intermittently; thus, it contacts the PEH, inducing oscillatory motion and generating strain, which finally produces electricity. The configuration angle is varied with 30°, 45°, and 60° to produce variation of power output analyzed in this study. The results demonstrate that a lower configuration angle, specifically 30°, produces the highest voltage near 1.4 V. This is due to the alignment of the applied force with the natural bending direction of the cantilever, resulting in greater induced strain and increased voltage output. Conversely, increasing the configuration angle reduces the effectiveness of force induced to PEH, diminishing strain induction and electrical generation, which only about 1.2 V. The finding of this study can potentially contribute to advance the design and optimization of PEHs for renewable energy applications, particularly in powering microelectronic devices.
Using triple-layer remote phosphor structures LaVO4:Eu3+ and ZnS:Cu,Sn to improve the chromatic uniformity and luminous quality of WLEDs Phuc Dang Huu; Dieu An Nguyen Thi; Anh Minh D. Tran
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 5: October 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i5.24081

Abstract

This research paper investigates the novel triple remote phosphor layer for improving the remote phosphor’s angular chroma uniformity (ACU) of down-light lamps by using remote micro-patterned phosphor layers (RMPP). In addition, introducing the triple-layer (TL) RMPP is introduced to offer the potential approach to this objective. This analysis also measures the optical efficiency of the layers and the angle distribution of angular correlated color temperature (ACCT). Drawing a comparison between the traditional dual-layer (DL) RMPP and the proposed TL is furthermore critical to this study. According to the findings, the triple-layer phosphor configuration can achieve greater hue consistency while having a correlating colour temperature (CCT) variance merely measured at 441 K. Results in the single RMPP layer are 1390 K of the remote phosphor (RP) sheet setting and 556 K for the ACCT deviation. The recreation employing finite-difference time-domain (FDTD) as well as the approach of ray-tracing ensures an increase in angular color uniformity (ACU). The structure of DL and TL RMPPs results in a 6.68 % and 4.69 % gain in luminous efficiency, respectively, with the standard RMPP layer at a currently driving of 350 mA. The micro-patterned layer’s scattering characteristic and mixing effect may account for the increased ACU and luminous efficiency.
A new hybrid dyadic-tent chaotic function for secure digital image encryption Anymore Majere; Hiento Suharja; Suryadi MT
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 4: August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i4.27703

Abstract

Image encryption is significant for protecting visual data in modern digital communication systems. The sensitivity of chaotic functions to initial conditions and strong pseudo-randomness has led to their usefulness as cryptography techniques. This study developed a new hybrid chaotic function which combines the Dyadic Transformation Map and the Tent Map to create a stronger and more secure mechanism for image encryption. To further enhance keystream unpredictability, SHA-256 hashing was used as a whitening layer. The chaotic characteristics of the proposed function were evaluated using the Lyapunov exponent, confirming its usefulness in cryptography. The hybrid function was tested on both grayscale and color images to evaluate its performance in entropy, histogram uniformity, pixel decorrelation, and resistance to statistical attacks, and the effect of SHA-whitening. Results show that the hybrid function provides strong pixel diffusion and confusion while maintaining low computational cost, making it a lightweight yet highly secure encryption method. Thus, the proposed approach offers an effective and reliable solution for image protection in resource-constrained environments.
Electroencephalography-based wheelchair navigation control using convolutional neural network method Khairul Anam; Satrio Marta Wicaksono; Muchamad Arif Hana Sasono; Bima Wahyu Maulana; Fatkhul Mubarok; Ananta Pinsentius Rahmat Pamungkas; Moch. Rijal Fatoni
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 1: February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i1.25988

Abstract

Artificial intelligence refers to a computer-based system capable of learning human activities. For instance, in medical technology, AI can be used for a thought-controlled wheelchair. This study discusses the use of deep learning, specifically convolutional neural network (CNN), in predictiong of the user intention to navigate a wheelchair. The training data was collected from an EEG sensor and included the wheelchair’s movements - turning right, turning left, moving forward, moving backward, and idle. The signals were then sampled and feature-extracted using root mean square (RMS). In CNN classification, both raw and RMS data were used. This study compared two different CNN architectures. The first architecture has three convolutional layers and three pooling layers, while the second has two of each. The research compares the accuracy and loss values of CNN predictions using architecture 1 and 2 on both raw and RMS data. The experimental results indicate that when using raw data, the first CNN architecture achieved an accuracy of 85.12%, and the second model achieved 91.04%. However, when using RMS data, the first architecture achieved an accuracy of 76.47%, and the second achieved 73.74%. The study concludes that the movement of the wheelchair is better in real-time when using raw data compared to using RMS data.
Competent scene classification using feature fusion of pre-trained convolutional neural networks Thirumaladevi Satharajupalli; Kilari Veera Swamy; Maruvada Sailaja
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 4: August 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i4.24463

Abstract

In view of the fact that the development of convolutional neural networks (CNN) and other deep learning techniques, scientists have become more interested in the scene categorization of remotely acquired images as well as other algorithms and datasets. The spatial geometric detail information may be lost as the convolution layer thickness increases, which would have a significant impact on the classification accuracy. Fusion-based techniques, which are regarded to be a viable way to express scene features, have recently attracted a lot of interest as a solution to this issue. Here, we suggested a convolutional feature fusion network that makes use of canonical correlation, which is the linear correlation between two feature maps. Then, to improve scene classification accuracy, the deep features extracted from various pre-trained convolutional neural networks are efficiently fused. We thoroughly evaluated three different fused CNN designs to achieve the best results. Finally, we used the support vector machine for categorization (SVM). In the analysis, two real-world datasets UC Merced and SIRI-WHU were employed, and the competitiveness of the investigated technique was evaluated. The improved categorization accuracy demonstrates that the fusion technique under consideration has produced affirmative results when compared to individual networks.

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