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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
 318   319   320   321   322 
Identification of human resource analytics using machine learning algorithms Elham Mohammed Thabit A. Alsaadi; Sameerah Faris Khlebus; Ashwak Alabaichi
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.v20i5.21818

Abstract

Employee attrition is one of the most significant business issues in human resource (HR) analytics. This research aims to identify the most critical elements that contribute to employee attrition. Businesses operate heavily on employee training in order to maximize the returns they will offer to the company in the future. By utilizing the employee information value concept, it has been discovered that employee features such as overtime, the total number of projects and job level have a significant impact on attrition. To find the probability of new employee attrition, various classification algorithms such as decision trees (DT) classifier, logistic regression (LR), random forests (RF), and K-means clustering are used. A comparative analysis of the models with different rating scales is carried out for the highest accuracy. For prediction, four diverse machine learning (ML) algorithms such as LR, RF, DT classifier, and k-nearest neighbors (k-NN) are used. DT classifier outperforms with 97% of accuracy than other techniques. The effects of predictive ML techniques on the employee dataset show that RF evaluation outperforms other ML techniques followed by model of LR for the specific dataset if precision is the preferred metric. Identification of HR is forecasted using ML algorithms on employee data.
Lightweight SDN/NFV-based framework for dynamic data-flow and network slice adaptation Sumbal Zahoor; Ali Mamoon
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.v24i3.27810

Abstract

The increasing demand for responsive and reliable network services in next-generation communication systems has intensified the need for dynamic resource management and quality of service (QoS) assurance. Software-defined networking (SDN) and network function virtualization (NFV) provide programmability and flexibility for modern networks. However, practical platforms that demonstrate real-time adaptive behavior remain limited. This study differs from prior simulation-focused work by demonstrating real-time adaptive slice control in a reproducible container-based SDN/NFV emulation environment. A bottleneck-aware slice controller is developed to classify degradations as network-limited, server-limited, or service failure using joint indicators, and to select rerouting or service migration using stability constraints and a lightweight action-cost model. Experimental results show that throughput is restored to above 90% of nominal capacity. Recovery typically occurs within two to three control iterations. Service continuity is maintained with low control-plane overhead. The work provides a reproducible experimental baseline and a decision mechanism that reduces incorrect reroutes/migrations under ambiguous key performance indicator (KPI) drops.
An image encryption based on Fibonacci sequence and fusion of advanced encryption standard-least significant bit method Purwanto Purwanto; Aris Marjuni; Erna Zuni Astuti; Christy Atika Sari; Nova Rijati; Pulung Nurtantio Andono; Md Kamruzzaman Sarker
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.v22i6.26078

Abstract

Image encryption is a vital field ensuring the secure transmission of digital images. In this study, encryption is the core process, employing complex mathematical algorithms and cryptographic keys to transform the original image into a secure format, shielding visual data from unauthorized access during transmission. To enhance security, the research integrates Fibonacci and advanced encryption standard (AES)–least significant bit (LSB) methodologies for a complex key generation system. This mechanism introduces intricate transformations within the image data, creating patterns challenging for potential attackers to decipher. Evaluation of the algorithm’s performance reveals efficiency in terms of mean squared error (MSE) and peak signal-to-noise ratio (PSNR). The RGB cover image achieves the lowest MSE of 0.0001 and the highest PSNR values ranging from 44.31 to 49.27. Integration of the Fibonacci sequence notably improves visual quality, enhancing both MSE and PSNR metrics. Unified average changing intensity (UACI) and normalized pixel change rate (NPCR) assessments consistently show the effectiveness of the algorithm, with the RGB cover image presenting the highest UACI and NPCR values. Future research directions involve exploring advanced encryption algorithms, optimizing techniques for high-dimensional datasets, and addressing ethical implications in image encryption, contributing to the development of adaptable and secure solutions.
Contrast modification for pre-enhancement process in multi-contrast rubeosis iridis images Rohana Abdul Karim; Nurul Wahidah Arshad; Yasmin Abdul Wahab
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.v21i4.22251

Abstract

Existing researchers for rubeosis iridis disease focused on image enhancement as a collective group without considering the multi-contrast of the images. In this paper, the pre-enhancement process was proposed to improve the quality of iris images for rubeosis iridis disease by separating the image into three groups; low, medium and high contrast. Increment, decrement and maintenance of the images’ original contrast were further operated by noise reduction and multi-contrast manipulation to attain the best contrast value in each category for increased compatibility prior subsequent enhancement. As a result, this study proved that there have three rules for the contrast modification method. Firstly, the histogram equalization (HE) filter and increasing the image contrast by 50% will achieve the optimum value for the low contrast category. Experimental revealed that HE filters successfully increase the luminance value before undergoing the contrast modification method. Secondly, reducing the 50% of the image contrast to achieve the optimum value for the high contrast category. Finally, the image contrast was maintained for the middle contrast category to optimise contrast. The mean square error (MSE) and peak signal-to-noise ratio (PSNR) of the outputs were then calculated, yielding an average of 18.25 and 28.87, respectively.
Agriculture data visualization and analysis using data mining techniques: application of unsupervised machine learning Kunal Badapanda; Debani Prasad Mishra; Surender Reddy Salkuti
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 1: February 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i1.18938

Abstract

Unsupervised machine learning is one of the accepted platforms for applying a broad data analytics challenge that involves the way to identify secret trends, unexplained associations, and other significant data from a wide dispersed dataset. The precise yield estimate for the various crops involved in the planning is a critical problem for agricultural planning. To achieve realistic and effective solutions to this problem, data mining techniques are an essential approach. Applying distplot combined with kernel density estimate (KDE) in this paper to visualize the probability density of disseminated datasets of vast crop deals for crop planning. This paper focuses on analyzing and segmenting agricultural data and determining optimal parameters to maximize crop yield using data mining techniques such as K-means clustering and principal component analysis (PCA)
An insight on using deep learning algorithm in diagnosing gastritis Ragu P. J.; Ashok Vajravelu; Muhammad Mahadi bin Abdul Jamil; Syed Riyaz Ahammed
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.v23i6.27191

Abstract

Chronic autoimmune gastritis (CAG) is a condition in which the stomach membrane is significantly impacted by inflammation. Despite the availability of numerous modern medical techniques, the detection of this condition continues to be a difficult challenge. White light endoscopy (WLE) has been employed to diagnose gastritis, but it has been subject to certain constraints. This technique is most effective when executed by an endoscopist who possesses a high level of expertise. In the present day, WLE is frequently accompanied by artificial intelligence (AI) due to its superior ability to detect defects that lead to damage. Recently, there has been a substantial increase in the efficacy of AI in conjunction with the expertise of endoscopists in the detection of CAG. The 25,216 intriguing case studies were examined in the eight selected studies. The collection comprised 84,678 frames and 10,937 images. The AI was 94% sensitive (95% CI: 0.88-0.97, I2 = 96.2%) and 96% specific (95% CI: 0.88-0.98, I2 = 98.04%). The receiver operating characteristic curve had an area of 0.98 (95% confidence interval: 0.96–0.99). A camera is highly effective when combined with AI to assist in the identification of CAG and is advantageous for clinical review.
A survey on the dataset, techniques, and evaluation metric used for abstractive text summarization Shivani Sharma; Gaurav Aggarwal; Bipin Kumar Rai
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.v22i3.25512

Abstract

Whenever there is too much information out there, it is desirable to summarize. If humans are trying to create the summary, it will take lot of time. Now to make the problem of summarizing information easier and more effortless one can automate the summarization process which can reduce the time taken in creating summary. This is called as automatic summarization. The two ways of summarization are extractive summarization and abstractive summarization. Extractive summarization and its applications have been the subject of extensive research and have received state of art solution. But abstractive summarization still is a progressive field as it is difficult to create abstractive summary as humans do. Also, it is still a question i.e., how to evaluate the quality of a summary? therefore, this paper is a comprehensive survey on the dataset used with its details and statistics, analysis of various abstractive summarization techniques and important parameters for evaluating the quality of summary. Deep leaning based models have given new direction in this field. The author also focuses on problems and challenges faced in the generation of summary which are opening the future research scope in this domain.
LPCNN: convolutional neural network for link prediction based on network structured features Asia Mahdi Naser Alzubaidi; Elham Mohammed Thabit A. Alsaadi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 6: December 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i6.22990

Abstract

In a social network (SN), link prediction (LP) is the process of estimating whether a link will exist in the future. In prior LP papers, heuristics score techniques were used. Recent state-of-the-art studies, like Wesfeiler-Lehman neural machine (WLNM) and learning from subgraphs, embeddings, and attributes for link prediction (SEAL), have demonstrated that heuristics scores may increase LP model accuracy by employing deep learning and sub-graphing techniques. WLNM and SEAL, on the other hand, have some limitations and perform poorly in some kinds of SNs. The goal of this research is to present a new framework for enhancing the effectiveness of LP models throughout various types of social networks while overcoming the constraints of earlier techniques. We present the link prediction based convolutional neural network (LPCNN) framework, which uses deep learning techniques to examine common neighbors and predict relations. Adapts the LP task into an image classification issue and classifies the links using a convolutional neural network. On 10 various types of real-work networks, tested the suggested LP model and compared its performance to heuristics and state-of-the-art approaches. Results revealed that our model outperforms the other LP benchmark approaches with an average area under curved (AUC) above 99%.
Study of the effect of changes in cell output temperature on the net efficiency of solid oxide fuel cell power generation Handrea Bernando Tambunan; Ignatius Riyadi Mardiyanto; Lina Troskialina; Sri Paryanto Mursid; Lidya Elizabeth; Dhyna Analyes Trirahayu; Retno Dwijayanti
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.v24i4.27828

Abstract

Solid oxide fuel cell (SOFC) power plants offer a promising pathway toward highly efficient energy conversion compared to conventional systems. This study explores the impact of output temperature variations on system performance through simulation analysis. The results demonstrate that increasing the temperature difference between the fixed input (700 °C) and the cell output significantly enhances net efficiency, while gross efficiency remains relatively stable. At an output temperature of 875 °C, the system achieves a net efficiency approaching 50% and a gross efficiency of approximately 61%. These findings emphasize the critical role of output temperature in determining overall system efficiency and highlight the importance of thermal management in SOFC operation.
Power system frequency control: instantaneous discrete testing for numerical relay using wavelet transform Emad Awada; Akram Al-Mahrouk; Eyad Radwan; Tareq A. Alawneh; Aws Al-Qaisi
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.v23i1.26433

Abstract

With today’s advanced technology and rapidly growing energy demands, the reliability of electrical power systems has reached an important level. With extensive monitoring and protection, system issues like voltage drops, power irregularities, and frequency variations can have destructive consequences on the power network. Therefore, as frequency relays play a critical role in protecting power generators and load equipment from power frequency shifts, relays have evolved from electromechanical to solid-state devices with ongoing optimization to handle integrated modern networks. Traditional numerical relays use Fourier transform to identify frequency changes, which necessitates numerous data samples and has limitations with transient waveform data. To address these challenges, this work proposes a new relay algorithm based on instantaneous discrete testing and wavelet transform for frequency analysis, aimed at enhancing relay performance. This new approach demonstrates promising advantages, including significant reductions in data sample requirements, compilation complexity, decision-making time, and improved handling of transient waveforms.

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