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Nizirwan Anwar
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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
 323   324   325   326   327 
Moving-horizon estimation approach for nonlinear systems with measurement contaminated by outliers Moath Jamal Awawdeh; Tarig Faisal; Anees Bashir; Abdel Ilah Nour Alshbatat; Rana T. H. Momani
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.v22i1.25230

Abstract

An application of moving-horizon strategy for nonlinear systems with possible outliers in measurements is addressed. With the increased success of movinghorizon strategy in the state estimation for linear systems with outliers acting on the measurement, investigating the nonlinear approach is highly required. In this paper we applied the nonlinear version which has been presented in the literature in term of discrete-time linear time-invariant systems, where the applied strategy considers minimizing a least-squares functions in which each measure possibly contaminated by outlier is left out in turn and the lowest cost is propagated. The moving horizon filter effectiveness as compared with the extended Kalman filter is shown by means of simulation example and estimation error comparison. The moving horizon filter shows the feature of resisting outliers with robust estimation
Frequency reconfigurable circular microstrip patch antenna with slots for cognitive radio Mohamed Labiod; Zoubir Mahdjoub; Mohamed Debab
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 4: August 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i4.23761

Abstract

In this paper, a novel multi-frequency microstrip antenna with complementary ring slot resonator (CRSR) structure that satisfies Bluetooth, worldwide interoperability for microwave access (WiMAX), and wireless local area network (WLAN) applications is proposed. The conventional antenna consists of a circular microstrip patch at a resonance frequency band of 2.5 GHz. By loading two CRSR at the radiating element, three operating frequency bands 2.5 GHz, 3.6 GHz, and 5.2 GHz are achieved. The operational bands covered by the antenna are Bluetooth 2.5 GHz, WiMAX 3.6 GHz, and WLAN 5.2 GHz. The insertion of CRSR to patch antenna has made it possible to compact and simple design, and miniaturized antenna for cognitive radio. Moreover, the directivity of the proposed antenna is adequate with acceptable radiation properties and perfectly matches with the simulated and measured results.
Enhancing energy efficiency in wireless mesh networks through time-synchronized sleep scheduling and low-power hardware Rifki Muhendra; Dede Rukmayadi; Solihin Solihin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 3: June 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i3.27616

Abstract

Energy efficiency remains a critical challenge in wireless mesh networks (WMNs), particularly for internet of things (IoT) deployments with battery powered nodes and multihop communication. This paper proposes a time synchronized sleep scheduling framework that integrates a lightweight regression-based time synchronization model with low-power hardware to reduce energy consumption in long range (LoRa)-based WMNs. The proposed mechanism aligns local node clocks with a global reference using slope and offset correction, enabling synchronized active and sleep states across nodes. This coordination significantly reduces idle listening and unnecessary radio-on time. The proposed approach is validated through real world experiments on a multihop LoRa mesh testbed with up to three hops. Results show a substantial improvement in energy efficiency, reducing cumulative energy consumption from 125.31 mWh to 28.18 mWh over 10 hours (77.5% reduction). The sleep-mode current is reduced to 0.01 mA, demonstrating effective duty cycling. Furthermore, the approach maintains stable routing, bounded latency, and high packet delivery ratio (PDR). These findings confirm that accurate time synchronization is a key enabler for energy-efficient and reliable multihop communication, providing a practical solution to extend the operational lifetime of IoT-based WMNs.
Design of a concurrent tri-band LNA based on composite right/left-handed transmission line resonators Faycal El Hardouzi; Mohammed Lahsaini; Mustapha Bahich; Badr Nasiri; Younes Achaoui
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.v23i4.26816

Abstract

This article presents a three-band low noise amplifier (LNA) in microstrip technology based on composite right left handed transmmision line (CRLH TL) resonators for multi-band behavior, which has been designed to meet all the criteria that determine the quality of its operation. The transistor used is biased via a transmission line and matched by λ/4 transformer filters with CRLH-TL type resonators at the output to establish multiband behavior with improved band rejection to suppress unwanted frequencies and interference. The results demonstrate excellent performance at three frequencies: 900 MHz (15.03 dB gain), 2.1 GHz (13.58 dB gain), and 3.5 GHz (12.57 dB gain), with a noise figure below 2 dB and unconditional stability. The size of the proposed amplifier is 73×63 ??2 in area.
P-D controller computer vision and robotics integration based for student’s programming comprehension improvement Nova Eka Budiyanta; Catherine Olivia Sereati; Lukas Lukas
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.v18i2.14881

Abstract

The 21st-century skills needed to face the speed of understanding technology. Such as critical thinking in computer vision and robotics literacy, any student is hampered by the programming that is considered complicated. This study aims at the improvement of student embedded system programming competency with computer vision and mobile robotics integration approach. This method is proposed to attract the students to learn about embedded system programming by delivering integration between computer vision and robotics using the P-D controller since both of the fields are closely related. In this paper, the researcher described computer vision programming to get the data of captured images through the camera stream and then delivered the data into an embedded system to make the decision of robot movement. The output of this study is the improvement of a student’s ability to make an application to integrate a sensor system using a camera and the mobile robot running follow the line. The result of the test shows that the integration method between computer vision and robotics can improve the student’s programming comprehension by 40%. Based on the Feasibility test survey, it can be interpreted that from the whole assessment after being converted to qualitative data, all aspects of the learning stages of programming application tested with the integration of computer vision and robotics fall into the very feasible category for used with a percentage of feasibility by 77.44%.
Joint watermarking encryption compression algorithm for securing an e-healthcare application Daham Abderrahmane; Ouslim Mohamed
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.v22i2.25490

Abstract

Radiological imaging generates large data volumes for diagnosing. These data volumes still constitute an immense challenge for individual radiology practices, either when complying with the archive, hacker manipulation or swiftly distributing images among specialists as part of the diagnostic process. Analyzing images through illegal distortions may lead to inaccurate medical decisions. Watermarking techniques can be used to authenticate images, detect, and recover illegal changes made to teleradiology images as well. In this paper, a new blind medical image watermarking joint crypto-compression system is proposed to simultaneously achieve watermarking and compression. A discrete wavelet transform is applied to the medical image to locate different frequency sub-bands. The watermark is scrambled using one-dimensional chaotic map generator with uniform distribution modulo-one transformations for generating highly independent and uniformly distributed random chaotic sequences. A particle crypto-compression algorithm is applied for the insertion step: the scrambled watermark is inserted into the highest singular value derived from the compressed low-frequency sub-band, ensuring a delicate balance between imperceptibility and robustness. From the conducted tests and comparison of the obtained results with other techniques, we concluded that the robustness of the algorithm under various attacks was improved by the excellent value of the NC parameter, especially in the case of compression and geometric attacks.
Mixed attention mechanism on ResNet-DeepLabV3+ for paddy field segmentation Alya Khairunnisa Rizkita; Masagus Muhammad Luthfi Ramadhan; Yohanes Fridolin Hestrio; Muhammad Hannan Hunafa; Danang Surya Candra; Wisnu Jatmiko
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.v23i6.26829

Abstract

Rice cultivation monitoring is crucial for Indonesia, where paddy field areas de clined by 2.45% according to the Central Bureau of Statistics due to land func tion changes and shifting crop preferences. Regular monitoring of paddy field distribution is essential for understanding agricultural land utilization by farmers and landowners. Satellite imagery has become increasingly common for agricul tural land observation, but traditional neural networks alone provide insufficient segmentation accuracy. This study proposes an enhanced deep learning architec ture combining residual network (ResNet)-DeepLabV3+ with coordinate atten tion (CA) and spatial group-wise enhancement (SGE) modules. The attention mechanisms establish direct connections between context vectors and inputs, enabling the model to prioritize relevant spatial and spectral features for precise paddy field identification. The CA module enhances spectral feature discrim ination, whereas the SGE improves spatial characteristic representation. The experimental results demonstrate superior performance over the baseline meth ods, achieving intersection over union (IoU) of 0.85, dice coefficient of 0.89, and accuracy of 0.95. The proposed mixed attention mechanism significantly improves the accuracy and efficiency of automatic crop area identification from satellite imagery.
Optimization of light source wavelength for ammonia detection in water Nurfatihah Che Abd Rashid; Noran Azizan Cholan; Kim Gaik Tay; Afiqah Yaacob; Nazrah Ilyana Sulaiman; Khairulanwar Mokhiri; Nor Hafizah Ngajikin
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.v20i5.24079

Abstract

Optimization of light source wavelength for ammonia detection in surface water is presented in this work. For the ammonia detection, the surface water sample is mixed with sodium chloride and nessler reagent, whereas the sensor head consists of unclad plastic optical fiber. The unclad region has a length of 1 cm and the cladding is removed by immersing it in acetone solution. Experimental results indicate that the output light intensity of the sensor has linear relationship with the ammonia concentration. At the wavelength of 510 nm, the output light increases linearly as the ammonia concentration varies from 0.07 mg/L to 8.97 mg/L. At the same wavelength, the proposed sensor achieves the sensitivity of 0.0139 (mg/L)-1, accuracy of 99.59% and resolution of 0.72 µg/L. The analysis of light source wavelength reveals that a wavelength range from 450 nm to 580 nm produces the optimized performances. Within this wavelength range, the proposed sensor achieves sensitivity of higher than 0.01 (mg/L)-1, accuracy of higher than 99% and resolution of less than 1 µg/L.
Methods of finding the maximum common transitive subgraph: experimental comparison Oleg Sychev; Anton Chupinin
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.v24i4.27683

Abstract

The problem of finding a maximum common subgraph (MCS) in a graph has broad applications in practical domains. However, certain scenarios require subgraphs with special properties, such as transitivity, that must be kept during building the subgraph. We formally define the concept of a transitive subgraph, investigate its properties. We study four different algorithms for finding the max imum common transitive subgraph (MCTS), compiled a list of tests aim at com paring graphs after making various changes and evaluated their accuracy and efficiency on a set of test cases. Benchmarking on 64 tests ranks the algorithms by scalability and accuracy: branch matching is the most scalable (> 1000 ver tices) and accurate (F1: 0.9907). MCS tree search is viable for graphs of up to ∼ 250 vertices (F1: 0.9752). Backtracking is limited to < 30 vertices (ac curacy: 0.5625), and brute-force is only feasible for graphs with ≤ 10 vertices, despite its high accuracy (0.9375). We discuss the advantages and disadvantages of each method, the test cases where each method demonstrates a non-optimal MCTS,identify the classes on which the methods work correctly and found that the branch matching method based on the longest common subsequence (LCS) algorithm performed the best.
Simple RNN-LSTM hybrid deep learning model for Bitcoin and EUR_USD forecasting Mohamed EL Mahjouby; Khalid El Fahssi; Mohamed Taj Bennani; Mohamed Lamrini; Mohamed El Far
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.v23i1.25925

Abstract

The popularity of deep learning in time series prediction has significantly increased compared to the past. In this article, we utilize deep learning methods, which encompass long short term memory (LSTM) networks, simple recurrent neural network (SimpleRNN) networks, and gated recurrent units (GRU) networks. This research introduces a hybrid foundational model for forecasting future closing prices of EUR_USD in financial time series and Bitcoin, combining SimpleRNN with LSTM, referred to as SimpleRNN-LSTM. To improve the precisions of our hybrid model, we incorporate twenty-one technical indicators into the training data. Then, we compute four measures to evaluate the performance of various prediction models. When predicting currency pairs EUR_USD and Bitcoin, our hybrid foundational model outperforms SimpleRNN, LSTM, and GRU models.

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