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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
 325   326   327   328   329 
Imputation missing value to overcome sparsity problems RZ Abdul Aziz; Sri Lestari; Fitria Fitria; Febri Arianto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 4: August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i4.25940

Abstract

Collaborative filtering (CF) is a method to be used in recommendation systems. CF works by analyzing rating data patterns from previous users to produce recommendations according to their interests. However, it faces a crucial problem, sparsity, a condition where a lot of data is empty, which will affect the quality of the recommendations produced. To state this problem, the purpose of this study is to input methods including mean, min, max, and k-nearest neighbor imputation (KNNI). The steps taken include imputation of empty data, followed by similarity calculations using the cosin similarity method, and evaluation using root mean square error (RMSE). The experimental result shows that the mean method is excellent with an average similarity value of 0.99 and an RMSE value of 0.98.
Design and manufacturing of iris waveguide filters for satellite communication Hicham Setti; Asmae Mimouni; Abdelwahed Tribak
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.v21i2.21924

Abstract

We propose in this paper, two bandpass filters in waveguide technology having rectangular symmetrical discontinuities with a half-radius r, designed and operating respectively in the X-Band (9-11.5) GHz and C-Band (3.5-5.5) GHz. These filters consists of eight irises placed symmetrically respectively on standard rectangular waveguides WR90 and WR229 in which resonant irises are inserted. These irises are used to couple the sections very strongly in this filter, which allows the bandwidth to be increased and the matching to be controlled. The comparison between the numerical and electromagnetic results, which we obtained for the filters, constitutes a means of validation of computer simulation technology (CST) environment and Mician for the design of the other circuit elements in the various frequency bands. We observed excellent consistency between the simulation curves and those of the measurements. The results obtained are promising and pave the way for the use of these structures in the fields of telecommunications.
XGBoost optimization using hybrid Bayesian optimization and nested cross validation for calorie prediction Budiman Budiman; Nur Alamsyah; Titan Parama Yoga; R Yadi Rakhman Alamsyah; Elia Setiana
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 3: June 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i3.26554

Abstract

Accurately predicting calorie expenditure is crucial for wearable device applications, enabling personalized fitness and health recommendations. However, traditional models struggle with high data variability and nonlinear relationships in activity data, leading to suboptimal predictions. This study addresses these challenges by integrating extreme gradient boosting (XGBoost) with Bayesian optimization and nested cross validation to enhance predictive accuracy. Unlike previous approaches, our method systematically tunes hyperparameters using Bayesian optimization while employing nested cross validation to prevent overfitting, ensuring robust model evaluation. We utilize a dataset of daily activity records, including steps, distance, and active minutes, extracted from wearable devices. Our experimental findings indicate a substantial enhancement in prediction performance, achieving a mean squared error (MSE) of 4294.27, an Rsquared (R2) score of 0.9917, and a root mean squared error (RMSE) of 65.53. The proposed model outperforms baseline approaches such as random forest and support vector machines in terms of predictive accuracy. These findings underscore the advantage of our approach in predictive modeling. Beyond calorie estimation, the proposed methodology is adaptable to other domains requiring high-precision predictions, such as healthcare analytics and personalized recommendation systems.
Artifact elimination in ECG signal using wavelet transform Thanh-Nghia Nguyen; Thanh-Hai Nguyen; Van-Thuyen Ngo
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.v18i2.14403

Abstract

Electrocardiogram signal is the electrical actvity of the heart and doctors can diagnose heart disease based on this electrocardiogram signal. However, the electrocardiogram signals often have noise and artifact components. Therefore, one electrocardiogram signal without the noise and artifact plays an important role in heart disease diagnosis with more accurate results. This paper proposes a wavelet transform with three stages of decomposition, filter, and reconstruction for eliminating the noise and artifact in the electrocardiogram signal. The signal after decomposing produces approximation and detail coefficients, which contains the frequency ranges of the noise and artifact components. Hence, the approximation and detail coefficients with the frequency ranges corresponding to the noise and artifact in the electrocardiogram signal are eliminated by filters before they are reconstructed. For the evaluation of the proposed algorithm, filter evaluation metrics are applied, in which signal-to-noise ratio and mean squared error along with power spectral density are employed. The simulation results show that the proposed wavelet algorithm at level 8 is effective, in which the with the “dmey” wavelet function was selected be the best based power spectrum density.
Neural network with k-fold cross validation for oil palm fruit ripeness prediction Minarni Shiddiq; Feri Candra; Barri Anand; Mohammad Fisal Rabin
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.v22i1.24845

Abstract

The combination of hyperspectral imaging and artificial neural network (ANN) can predict fruit ripeness. This work investigated the application of hyperspectral imaging and ANN models with the k-fold cross-validation method for ripeness prediction of oil palm fresh fruit bunches (FFB) for inline sorting and grading machine vision. Crude palm oil (CPO) is an exporting commodity for countries such as Indonesia and Malaysia. Oil palm FFB ripeness determines the quality of CPO. The unique shapes and colors of FFBs need innovative methods to substitute tedious and cumbersome manual sorting and grading. The oil palm FFB samples used in this study were categorized previously based on color and loosed fruits. We applied the Savitzky-Golay (SG) smoothing filter and 7-fold cross-validation for hyperspectral datasets before being used for the ANN models and a confusion matrix to find the ANN model accuracies. We obtained 72 data points after SG filter and data selection from 523 data points. The prediction results showed an average accuracy of 79.48%, in which three folds with k of 2, 5, and 7 gave the highest accuracy of 90%. The results confirmed the potential use of hyperspectral imaging, with k-fold cross-validation and ANN models for ripeness prediction of oil palm FFBs.
Secure quantum key distribution system by applying decoy states protocol Sura Adil Abbas; Nael A. Al-Shareefi
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.v20i4.22796

Abstract

Secure quantum key distribution (QKD) promises a revolutionizing in optical applications such as encryption, and imaging. However, their implementation in real-world scenarios continues to be challenged. The goal of this work is to verify the presence of photon number splitting (PNS) attack in quantum cryptography system based on BB84 protocol and to obtain a maximum secure key length as possible. This was realized through randomly interleaving decoy states with mean photon numbers of 5.38, 1.588, and 0.48 between the signal states with mean photon numbers of 2.69, 0.794, and 0.24. Experiment results show that a maximum secure key length obtained from our system, which ignores eavesdropping cases, is 125 with 20% decoy states and 82 with 50% decoy states for mean photon number of 0.794 for signal states and 1.588 for decoy states.
Real-time flood forecasting with attention-enhanced hybrid deep learning using internet of things data Rissal Efendi; Indrastanti R. Widiasari
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.v24i3.27501

Abstract

Floods are a frequent disaster in Semarang city, Indonesia, requiring an accurate and real-time forecasting system to support effective risk management. This study introduces a hybrid long short-term memory-gated recurrent unit (LSTM-GRU) model with an attention mechanism (attention-enhanced LSTM-GRU) designed to improve the accuracy of flood predictions based on multiparameter internet of things (IoT) data. The novelty of this study lies in the integration of the attention mechanism within the hybrid LSTM-GRU architecture, which allows the model to provide adaptive focus on features and time periods that most influence flood occurrences. The dataset used consists of 1,736 time series samples covering rainfall and water level data collected every 15 minutes from IoT sensors in the upstream and downstream areas of Semarang, Indonesia. Experimental results show that the hybrid model with the attention mechanism provides the best performance with a mean absolute percentage error (MAPE) value of 1.4%, root mean squared error (RMSE) of 1.05, and coefficient of determination (R²) reaching 0.96. This model also achieves 100% recall for the “Danger” class, demonstrating its reliability in detecting critical conditions. The practical implication of this research is the availability of a flood prediction model that is accurate, adaptive, and can be directly applied to IoT-based early warning systems in flood-prone urban areas.
IR4.0 and internet of things: future directions towards enhanced connectivity, automation, and sustainable innovation Yousef A. Baker El-Ebiary; Salameh A. Mjlae; Hadeel Ahmad; Suzie Y. Rababa'h; Mahmoud A. Rababah; Omar G. Arabiat
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.v22i6.25487

Abstract

This study provides a systematic review of the literature on the fourth industrial revolution (IR4.0) and the internet of things (IoT) in genetics, informatics, and biotechnology, as well as their many rapidly evolving applications, which often overlap with various aspects of life. The study reviews the latest research, books, scientific theses, and official websites in this field, placing them in a coherent context for researchers. It covers innovations that have driven the continuous development of IR4.0 and its challenges. The study highlights numerous fields and modern technologies expected to bring about tangible and radical changes in health, agriculture, and industry. It addresses key aspects of IR4.0 and the significant advancements brought by smart cities, and most importantly, artificial intelligence (AI), including robots, autonomous cars, 3D printing, big data, IoT, nanotechnology, biotechnology, energy storage, and quantum computing. By applying cutting-edge technology across various disciplines to boost productivity and foster development, technology plays a crucial role in linking the physical, digital, and biological realms. This revolution is built on multiple axes, including the convergence of biotechnology, digital media, and physical systems.
Results of simulation modeling of technical parameters of a multiservice network Alevtina Aleksandrovna Muradova; Dilbar Turgunovna Normatova
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.v21i3.24058

Abstract

In a scientific article, the authors presented the results of simulation modeling of the technical parameters of a multiservice network. As a model of a multiservice communication network for the computational experiment, the model proposed in the previous scientific publications of the author was chosen. The selected model uses the Laplase-Stilles transform. Simulation modeling was carried out using the technical parameters of the multiservice network’s availability factor and network load. Scientific results of experimental research work are given in the form of tables. Relations of the probability of betime servicing of an application on the load of a multiservice communication network for various availability factors and the probability of untimely service of an application on the availability factor for numerous network loadings are obtained. The character of the distribution of costs necessary for the implementation of solutions for different categories of technical operation is shown. Scientific research on determining the objective function’s minimum value is presented in graphs and diagrams. The results of simulation modeling of the technical parameters of multiservice networks are presented in the form of diagrams using the Matlab software environment.
Optimal active disturbance rejection control with applications in electric vehicles Juan Quecan-Herrera; Sergio Rivera; Jorge Neira-Garcia; John Cortés-Romero
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.v23i5.26867

Abstract

This work proposes an optimal control strategy based on a modified active disturbance rejection control (ADRC) that considers disturbance weighting for a three-phase induction motor under rotor field-oriented control (FOC) to enhance energy efficiency. Induction motors (IMs) are widely used in electric vehicles (EVs) due to their cost-effectiveness and technological maturity. However, improving energy efficiency remains a key challenge, as it directly impacts vehicle range. The proposed approach employs ADRC, where part of the disturbance rejection task is handled offline by a hybrid optimization algorithm combining particle swarm optimization (PSO), tabu search (TS), and simulated annealing (SA) to tune a state-feedback controller. The controller parameters are optimized using a composite cost function that balances energy consumption and performance. Simulation and experimental results indicate that disturbance weighting has a significant impact on both problem complexity and performance. Optimal weighting improves the overall system response compared to conventional disturbance rejection methods. Energy and performance analyses show that disturbance weighting enhances energy usage compared to the traditional ADRC method, suggesting a novel efficiency control strategy for electric machines.

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