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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
 332   333   334   335   336 
Optimal design of full order state observer for active surge control in centrifugal compressors using genetic algorithm Salisu Mohammed; Yusuf A. Sha’aban; Ime J. Umoh; Ahmed T. Salawudeen
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 2: April 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i2.17440

Abstract

This paper presents the design of a genetic algorithm (GA) based full order state observer for a compressor system. The observer measures the compressor system parameters and computes the difference between the measured output and the setpoint, which is used as feedback to the active surge controller. The proposed control structure with a GA tuned compressor characteristic is required to minimize system oscillations and energy losses induced by the piston actuation gas recycling technique. The design method for GA based full order observer begins with a state observer modelling, observer error estimation based on the magnitude of fluid friction and mechanical effects on measured parameters. The aim of the controller is to regulate the compressor driver speed such that the measured output achieves its required set point. In order to achieve a maximum compressor operating capacity, the compressor characteristics were optimized using GA, where the mass flow was maximized to improve the compressor efficiency. Simulations were carried out, and results showed the viability of GA based full order state observer compared with the piston actuation recycle method to control active surge in centrifugal compressor systems
Optimizing blood cell classification: evaluating feature dimensionality and validation strategies Ruaa H. Ali Al-Mallah; Marwa Mawfaq Mohamedsheet Al-Hatab; Maysaloon Abed Qasim
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 1: February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i1.27269

Abstract

Manual blood cell classification is time consuming and may lead to inconsistent results. This study aims to assist pathologists in diagnosing hematological disorders using machine learning (ML) techniques for automated classification of blood cells in multi-color test images, distinguishing red blood cells (RBCs) and white blood cells (WBCs). Features were extracted using the InceptionV3 network, and several ML models were evaluated for classifying blood cells into eight categories. Two validation strategies: a 66%–34% train–test split and 20-fold cross-validation were applied. The effect of dimensionality reduction through principal component analysis (PCA) was also examined, reducing the feature space from 2,048 to 100 components. Among all models, support vector machine (SVM) achieved highest performance, with 93.4% accuracy and an area under the curve (AUC) of 0.996 without PCA, and 90.1% accuracy with an AUC of 0.991 after PCA. Although PCA slightly reduced accuracy, it improved computational efficiency. Overall, SVM provided the most accurate, stable, and generalizable classification results for automated blood cell analysis.
Multi-stage cryptography technique for wireless networks Amer Alsaraira; Samer Alabed; Omar Saraereh
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 3: June 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i3.25896

Abstract

The security of wireless communication systems has been the focal subject of research for more than a century. It is a topic of vital importance due to its effectiveness in many fields. Besides, the importance of sending data from one point to another without intercepting by unwanted third-party has become an essential requirement in conjunction with the widespread hacking technologies. This research paper is set out to explore a new technique used in internet of things applications that will ensure a secure wireless communication system. Additionally, it clarifies five distinct cryptographic methods: advanced encryption standard, elliptic curve cryptography, transposition, substitution, and Rivest, Shamir, and Adleman. In addition, the secure wireless communication system combines four of the prementioned techniques to build a highly secure system. Moreover, we managed to elucidate the critical points of our work on the long-range systems into a short yet informative and detailed summary. This paper will extensively cover the following topics: cryptography, cryptographic methods and techniques, software implementation, applications, and advantages of the system.
Hippocampus’s volume calculation on coronal slice’s for strengthening the diagnosis of Alzheimer’s Retno Supriyanti; Yogi Ramadhani; Eko Wahyudi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 1: February 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i1.20746

Abstract

Alzheimer’s is one of the most common types of dementia in the world. Although not a contagious disease, this disease has many impacts, especially in socio-economic life. In diagnosing Alzheimer’s and using interview techniques, physical examination methods are also used, namely using an magnetic resonance imaging (MRI) machine to get a clear image of the patient’s brain condition, with a focus on the hippocampus and ventricular area. In this paper, we discuss the calculation of the volume of the hippocampus, especially the coronal slice, to provide information to doctors in making decisions on diagnosing the severity of Alzheimer’s. Using the basis of volume calculations, we made a 3D visualization reconstruction of the coronal hippocampus slice area in order to make it easier for doctors to analyze the condition of the hippocampus area, which in the end will be used as a recommendation in the classification of the severity of Alzheimer’s. Our experimental results show, the lower the severity, the bigger the volume, the more slices, and the longer the counting time.
Homogeneous transformation matrix for force-torque sensor orientation compensation in rotatable control handle Shivam Suresh Zagade; Rajiv Basavarajappa H.; Sudhir Madhav Patil; Abhishek Pradeep Buzruk; Kshitij Ghanshyam Jiwane; Tole Sutikno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 2: April 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i2.26465

Abstract

The high inertia ceiling suspended systems with multiple degrees of freedom uses power assist technologies to reduce operator’s burden to operate the machine. Such systems are popularly used in medical diagnostic systems, construction machines, material handling, automotive, and aerospace assembly lines. These systems commonly use multi-axis force-torque (FT) sensor to sense the forces applied by user on rotatable control handle. These sensed forces are utilized by power assist algorithm to drive system in required direction with the help of electrical motor drives. The rotatable control handle used to control the machine poses a significant obstacle for maintaining alignment between FT sensor co-ordinate frame and the system’s base frame. This research paper focuses on the development of homogeneous transformation matrix to compensate for any change in FT sensor orientation caused by rotation of control handle. The homogeneous transformation matrix developed in this research paper, transforms the force and torque values measured by FT sensor with respect to system base frame. This adaptive technique provided seamless control of the power assist ceiling suspended system from different directions during handling and movement. This helped to enhance control and flexibility of power assist ceiling suspended system.
Project Evaluation Method Based on Matter-Element and Hierarchy Model Haifeng Li Haifeng Li
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 10, No 3: September 2012
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v10i3.841

Abstract

Project evaluation is always the key link of the engineering project management. Project evaluation is a complex work which involves many factors. The final evaluation result is always influenced directly by the definition of various evaluation indexes and the corresponding weight. Mainly study the judgment of the experts’ ability and the establishment of the project evaluation index system in the peer communication review, analyze deeply the engineering project review work, build a evaluation index system for the engineering project, and put forward a comprehensive evaluation method based on the matter-element and hierarchy model in the engineering project, at last, apply it in the actual project which proves the practicability of the paper’s theory.
Design zigzag edge of S shape slot antenna by using SIW technology for 5G application Alaa Abd Ali Hadi; Essam Hamoodi Ahmed; Baydaa Hadi Saoudi; Yaqdhan Mahmood Hussein; Tabarek Alwan Tuib
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 6: December 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i6.23788

Abstract

This study offers a 5G substrate integrated waveguide (SIW) antenna with vertical S zigzag shape slot. The suggested antenna's radiating patch depends on the shape of the slot. Two types of slots have been used, straight and zigzag S shape slot with vertical and horizontal direction, slots are etched on the patch to increase the antenna's overall bandwidth and gain. The suggested straight and zigzag SIW S slot antennas both resonate at 28 GHz, and the overall structure size is 7.10×14.93 mm. The presented design could achieve high gain, efficiency, and minimal losses, which are all important concerns. The presented antenna may produce a gain of 9.48 dB, a 95% efficiency, and a wider bandwidth of no less than 3.25 GHz at 28 GHz.
Comparative study of extraction features and regression algorithms for predicting drought rates Irza Hartiantio Rahmana; Amalia Rizki Febriyani; Indra Ranggadara; Suhendra Suhendra; Inna Sabily Karima
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 3: June 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i3.23156

Abstract

Rice is the primary staple food source for Indonesian people, with consumption increasing so that rice production needs to be increased. Rice drought is one of the problems that can hamper rice production. This research aims to determine the best extraction feature between the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) in describing rice fields’ dryness. Moreover, using the random forest regression algorithm. This research compares NDVI with NDWI using data originating from Sentinel-2A and retrieved via the google earth engine. Regression algorithms are used in research to predict drought in paddy fields. This research shows that NDVI is better than NDWI in predicting drought using random forest regression algorithms and logistic regression algorithms. The random forest regression algorithm based on the results obtained shows that the average root mean square error (RMSE) on NDVI is 0.018, and NDWI is 0.012. Based on the logistic regression algorithm results, it was found that the average value of RMSE on NDVI was 0.346, and NDWI was 0.336. Based on the results of the RMSE, it shows that the forecasting ability of the random forest regression algorithm is better than the logistic regression.
Adaptive fuzzy sliding mode control with exponential reaching law and MPL method for the coupled-tank system Thanh Tung Pham; Le Minh Thien Huynh
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.v24i2.27437

Abstract

This study develops an adaptive fuzzy sliding mode control (ASMC) scheme incorporating an exponential reaching law (ERL) and a minimum parameter learning (MPL) strategy to achieve liquid-level regulation in a coupled-tank system. Such systems are widely used in industrial applications, including chemical and petrochemical processing, water treatment, power generation, and the manufacturing of construction materials, as well as in boilers, evaporators, reactors, and distillation columns. The ERL-based sliding mode controller is formulated to guarantee finite-time tracking of the desired liquid level while effectively suppressing chattering near the sliding surface. The MPL approach is embedded within the fuzzy system (FS), resulting in a single online adaptive parameter, which significantly reduces computational complexity and enhances real-time performance. The stability of the closed-loop system is rigorously established using Lyapunov theory. Simulation studies conducted in MATLAB/Simulink validate the effectiveness of the proposed controller, demonstrating a rise time of 6.1918 s, a settling time of 11.2553 s, zero overshoot, convergence of the steady-state error to zero, and a noticeable reduction in chattering.
Feature selection to improve distributed denial of service detection accuracy using hybrid N-Gram heuristic techniques Andi Maslan; Abdul Hamid; Dedy Fitriawan; Anggia Dasa Putri; Tukino Tukino
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.v22i5.24913

Abstract

Distributed denial of service (DDoS) attacks servers and computers in various ways, such as flooding traffic. There are three DDoS detection methods, namely anomaly-based, pattern-based and heuristic-based. However, pattern-based methods cannot detect recent attacks, while anomaly-based methods have low accuracy and relatively high false positives. This research proposes increasing accuracy using a heuristic-based DDoS detection method and a new feature. The combination of CSDPayload+N-Gram and CSPayload+N-Gram features is called hybrid N-Gram, which is analysed on four datasets: CIC2017, CIC2019, MIB-2016, and H2NPayload. Next, calculate Chi-square distance (CSD) and cosine similarity (CS) using the N-Gram frequency value results. Subsequently, compute Pearson Chi-square using the N-Gram frequency value results. Compare the CSDPayload+N-Gram and CSPayload+N-Gram, along with the Pearson Chi-square value, to classify it as either DDoS or not. Finally, feature selection based on weight correlation and payload classification employs machine learning algorithms: support vector machine (SVM), K-nearest neighbors (KNN), and neural network (NN). The average accuracy rate for detecting DDoS attacks across four datasets, utilising the CSDPayload+4-Gram and CSPayload+4-Gram features with the SVM algorithm, is 99.71%, which surpasses the accuracy achieved by using KNN (96.22%) and NNs (99.50%) imitation. Thus, the best algorithm for detecting DDoS is SVM with hybrid 4-Gram.

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