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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
 310   311   312   313   314 
Detecting community on social networks with fast and optimal online clustering algorithms Muneer Sameer Gheni Mansoor; Hasanain Abdalridha Abed Alshadoodee; Rahim Muhammad Alabdali; Ahmed Dheyaa Radhi; Poh Soon JosephNg; Jamal Fadhil Tawfeq
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.v22i2.24724

Abstract

Social networks have become an essential part of our lives today, at least in their virtual dimension, and the image of the web world is almost impossible without the presence of this pervasive phenomenon. These networks are one of the important components of the information infrastructure, such as twitter networks, facebook networks, and so on. In the analysis of social networks, one of the important issues is the detection of community. Each community is a group of network nodes so that the connection between nodes within the group with each other is more than their connection with other network nodes. Various methods have been proposed for community detection. One of the existing methods is based on data stream clustering. The output data of a social network can be modeled with a data stream. Fast and accurate clustering of this data stream can be very effective in the detection of community. In this research, using a fast and accurate online clustering algorithm, the community is detected. The simulation results indicate that the method proposed in this research can calculate the number of clusters optimally and perform better than similar methods. The proposed algorithm can be used in many other applications.
Rate-splitting multiple access in satellite-terrestrial communication systems: performance analysis Huu Q. Tran; Khuong Ho-Van
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.v23i5.26854

Abstract

This paper investigates the throughput and outage probability (OP) of rate splitting multiple access (RSMA) in satellite–terrestrial communication networks. By dividing user messages into common and private parts, RSMA enhances spectral efficiency and user fairness while addressing hardware impairments and co-channel interference. The proposed hybrid system model is analyzed and compared with non-orthogonal multiple access (NOMA) under various power allocation coefficients and channel conditions. Results show that RSMA achieves lower OP and higher throughput than NOMA, particularly in dense multi-cell deployments. Numerical evaluations further demonstrate RSMA’s robustness against interference and hardware limitations, underscoring its potential as a reliable solution for next-generation satellite–terrestrial relay networks.
Ku-band specific attenuation coefficients for high-throughput satellites in equatorial region Yasser Asrul Ahmad; Ahmad Fadzil Ismail; Khairayu Badron
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.v20i4.23766

Abstract

Ku-band have a larger attenuation during heavy rain in the equatorial region. Despite that, Ku-band has been identified as a frequency band in high-throughput satellite systems (HTS) for broadband satellite communication. The available rain fade prediction models are still not able to accurately predict rain attenuation in the equatorial region. The models depend on the specific attenuation parameters produced based on the international telecommunication union radiocommunication sector (ITU-R) instead of the measured value. Direct measurement of specific attenuation is more accurate but difficult to obtain because the correlation of rainfall rate and satellite signal loss due to rain must be obtained simultaneously. This paper aims to derive new specific attenuation frequency-dependent coefficients for Ku-band using the semi-empirical method. The Malaysia East Asia Satellite 3 (MEASAT-3) satellite data was collected using a 13 m antenna located at Cyberjaya, Selangor while the rainfall data was collected by the nearby hydrological station. The specific attenuation was obtained from correlations of the direct measurement of rain attenuation and rainfall rate. The new frequency-dependent coefficients for Ku-band specific attenuation values are k = 4.6690 and α = 0.1941. The newly acquired specific attenuation coefficients have improved the rain attenuation model prediction for the equatorial region.
Transparent insights: explainable AI with machine learning classifiers for early stage of depression classification S. M. Rakibul Islam; Shaykh Yunus; Rashiduzzaman Shakil; Fatema Tuz Johora; Aditya Rajbongshi; Sujon Chandra Sutradhar
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.v24i3.27651

Abstract

Depression is a widespread mental health condition characterized by enduring feelings of persistent sadness, loss of interest, and impaired daily functioning. Untreated depression can result in significant implications, such as academic failure, social isolation, and even suicide. This study presents a machine learning (ML)–based framework for classifying depression severity among university students using the Zahir depression scale dataset, comprising 478 responses categorized into mild, moderate, severe, and profound depression. In order to address the issue of class imbalance, we utilized the synthetic minority over sampling technique (SMOTE) on the dataset. In addition, seven different ML algorithms are employed to classify the severity of depression, and each algorithm’s efficiency is determined by four performance evaluation metrics. Among the applied ML classifiers, extra tree classifier outperformed with an average accuracy of 97.85% and 95.75% precision, 95.76% recall, and 95.75% F1-score. To enhance interpretability, the shapley additive explanations (SHAP) method was integrated to identify influential features, providing transparency and insight into the model’s decision process. The proposed framework demonstrates that combining explainable artificial intelligence (XAI) with traditional ML can support healthcare professionals in early depression screening and data driven mental health interventions.
Bayes estimation of a two-parameter exponential distribution and its implementation Ardi Kurniawan; Johanna Tania Victory; Toha Saifudin
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.v22i6.26015

Abstract

Life test data analysis is a statistical method used to analyze time data until a certain event occurs. If the life test data is produced after the experiment has been running for a set amount of time, the life time data may be type I censored data. When conducting observations for survival analysis, it is anticipated that the data would conform to a specific probability distribution. Meanwhile, to determine the characteristics of a population, parameter estimation is carried out. The purpose of this study is to use the linear exponential loss function method to derive parameter estimators from the exponential distribution of two parameters on type I censored data. The prior distribution used is a non-informative prior with the determination technique using the Jeffrey’s method. Based on the research results that have been obtained, application is carried out on real data. This data is data on the length of time employees have worked before they experienced attrition with a censorship limit based on age, namely 58 years, obtained from the Kaggle.com website. Based on the estimation results, the average length of work for employees is 6.29427 years. This shows that employees tend to experience attrition after working for a relatively long period of time.
Using phosphor ZnB2O4:Mn2+ for enhancing the illuminating beam and hue standard in white light-emitting diodes Ha Thanh Tung; Huu Phuc Dang
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.v21i3.24749

Abstract

The enhancement in hue uniformity and luminous production of multiple-chip white LED lamps (MCW-LEDs) using dual-layer remote phosphor packaging designs are the emphases of this paper. We blended Mn2+ activated strontium–barium silicate (ZnB2O4:Mn2+) with the phosphor mixture and manage to record significant impacts of this new phosphor mixture on the LED lights’ lighting performance. There is evidence that the growing concentration of yellow-green-emitting ZnB2O4:Mn2+ phosphor encourages the enhancement of hue uniformity and illumination effectiveness in MCW-LEDs with mean correlated hue heats (CCTs) of roughly 8500 K, though the color quality scale is gradually deteriorating. It is possible to successfully achieve such amazing MCW-LED performance if we choose the right concentration and size of ZnB2O4:Mn2+.
Substrate thickness variation on the frequency response of microstrip antenna for mm-wave application Bello Abdullahi Muhammad; Mohd Fadzil Ain; Mohd Nazri Mahmud; Mohd Zamir Pakhuruddin; Ahmadu Girgiri; Mohamad Faiz Mohamed Omar
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.v23i5.26731

Abstract

Substrate height (Hs) is an important parameter that influences antenna propagation. This research designed a low-profile 28 GHz microstrip antenna on a polyimide substrate with varying Hs using CST Studio software. The simulated results and MINITAB software were used to develop regression model equations, which analyzed the impact of Hs variation on the antenna performance. The proposed models’ equations have indicated an increase in average responses of resonant frequency (Fr), percentage bandwidth (% BW), gain (G), return loss (RL), and efficiency (ƞ) as the Hs decreased. The antenna achieved a BW of 3.87 GHz at Hs 0.525 mm and 5.54 GHz at 0.025 mm, a G of 3.89 dBi at Hs 0.525 mm and 3.91 dBi at Hs 0.025 mm, and an ƞ of 94.19% at Hs 0.525 mm and 98.24% at Hs 0.025 mm. The antenna was fabricated and tested, and the experimental results were validated with the models’ equations. The thinner substrate resulted in an improvement in the antenna performance.
Enhancement detection distributed denial of service attacks using hybrid n-gram techniques Andi Maslan; Kamaruddin Malik Mohamad; Cik Feresa Mohd Foozy
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.v20i1.18103

Abstract

Distributed denial of service (DDoS) attacks have always been a concern of cyber experts. To detect DDoS attacks, several methods can be used. One of the methods used in this research is the n-gram technique. The n-gram approach analyzes the payload of data packets that enter the network to obtain attack patterns. Data is captured and analyzed, after which it is compared with clean data packets. The chi-square distance value close to 1 indicates that the two packages are very similar so that the data packet is not an attack. A deal less than one means the data packet is categorized as an attack. In this research, the threshold for determining the attack level can be lowered to obtain a very high detection accuracy. As a result, the 2-gram technique has a detection accuracy rate with the lowest false positive value of around 13%, with the highest actual positive ratio reaching 99.98%.
Using decision tree classifier to detect Trojan Horse based on memory data Mosleh M. Abualhaj; Sumaya N. Al-Khatib
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.v22i2.25753

Abstract

Trojan Horse is a major threat that has grown with the spread of the digital world. Data gathered through the study of memory can provide valuable insights into the Trojan Horse’s behavior patterns. Because of this, memory analysis techniques are one of the topics that should be investigated in Trojan Horse detection. This study proposes the use of memory data in Trojan Horse detection. Trojan Horse detection used a decision tree (DT) classifier with memory data. Experiments were performed on the Trojan Horse samples from the CIC-MalMem-2022 dataset. The binary classification was made using DT, gradient boosted tree, Naive Bayes (NB), linear vector support machine, K-nearest neighbors (KNN), and machine learning (ML) classifiers. The comparison of the various classification methods was performed utilizing the accuracy, recall, precision, and F1-score metrics. As a result, the most successful Trojan Horse detection was gained with the DT classifier, which achieved accuracy of 99.96% using memory data. The NB classifier showed the lowest achievement in Trojan Horse detection using memory data, which achieved accuracy of 98.41%. In addition, numerous of the classifiers utilized have attained very high results. Based on the achieved results, the data from memory analysis is very valuable in detecting Trojan Horse.
Optimization of principal component analysis and k-nearest neighbors in cultivation area classification red onion Arif Ridho Lubis; Purwa Hasan Putra; Fahdi Saidi Lubis
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.v23i6.27103

Abstract

This research aims to increase the effectiveness in classifying shallot cultivation areas through the combined application of principal component analysis (PCA) and k-nearest neighbors (KNN) methods. Shallot is an important agricultural commodity, and identification of optimal areas for its cultivation is essential to support food self-sufficiency. Onion cultivation is generally done in the highlands. One of the areas with shallot cultivation in North Sumatra Province is Berastagi, Karo Regency. This research was conducted by determining the spatial extent of upland land. In the use of data there are 2 types of data that will be used: land suitability dataset and land condition dataset for each region. The PCA method is utilized to simplify the data structure by reducing the number of dimensions and removing insignificant attributes, while KNN was used to classify regions based on their suitability for shallot cultivation. This research produces a classification map that can be used to identify the most optimal areas for shallot cultivation. The test results with the regional spatial dataset using precision, recall and fi-score testing accuracy value 0.92%, and macro avg value 0.94%, weighted avg value 0.93%.

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