cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Nizirwan Anwar
Contact Email
nizirwan.anwar@esaunggul.ac.id
Phone
-
Journal Mail Official
telkomnika@ee.uad.ac.id
Editorial Address
Ahmad Yani st. (Southern Ring Road), Tamanan, Banguntapan, Bantul, Yogyakarta 55191, Indonesia
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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 36 Documents
 1   2   3   4 
Search results for , issue "Vol 19, No 5: October 2021" : 36 Documents clear
Hardware implementation of series DC arc fault protection using fast Fourier transform Dirhamsyah Dirhamsyah; Diana Alia; Dimas Okky Anggriawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 5: October 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i5.20521

Abstract

This paper proposes method of series DC arc fault protection using low cost microcontroller. Series DC arc fault occurs when gap between conductor or wire flows a current. Series DC arc fault can cause fire hazard if do not detected and protected. However, Series DC arc fault is difficult to detected using conventional protection. To detect series DC arc fault accurately using fast Fourier transform (FFT). FFT is used to transform signal in time domain to frequency domain. Series DC arc fault has different characteristic compared by normal current in frequency domain. Therefore, the proposed algorithm for protection of series DC arc fault based on magnitudes of the current in frequency domain. Hardware system is implemented by 100 V DC power supply and DC arc fault generator. Test result is conducted experimentally under varying of load current such as 2 A, 2.5 A, 3 A, 3.5 A, 4 A and 5 A. Experimental testing results show that Series DC arc fault protection has time for trip of 0.48 s, 0.26 s, 1.04 s, 0.68 s, 0.44 s and 0.48, respectively. The fastest time for trip is 0.26 s with current of 2.5 A. Therefore, the proposed algorithm for series DC arc fault protection can operate to trip accurately and have the good performance.
Improvement security in e-business systems using hybrid algorithm L. Sumaryanti; Dedy Hidayat Kusuma; Rosmala Widijastuti; Muhammad Najibulloh Muzaki
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 5: October 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i5.20403

Abstract

E-business security becomes an important issue in the development of technology, to ensure the safety and comfort of transactions in the exchange of information is privacy. This study aims to improve security in e-business systems using a hybrid algorithm that combines two types of keys, namely symmetric and asymmetric keys. Encryption and decryption of messages or information carried by a symmetric key using the simple symmetric key algorithm and asymmetric keys using the Rivest Shamir Adleman (RSA) algorithm. The proposed hybrid algorithm requires a high running time in the decryption process compared to the application of a single algorithm. The level of security is stronger because it implements the process of message encryption techniques with two types of keys simultaneously.
Efficient hardware prototype of ECDSA modules for blockchain applications Devika K N; Ramesh Bhakthavatchalu
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 5: October 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i5.19416

Abstract

This paper concentrates on the hardware implementation of efficient and re- configurable elliptic curve digital signature algorithm (ECDSA) that is suitable for verifying transactions in Blockchain related applications. Despite ECDSA architecture being computationally expensive, the usage of a dedicated stand-alone circuit enables speedy execution of arithmetic operations. The prototype put forth supports N-bit elliptic curve cryptography (ECC) group operations, signature generation and verification over a prime field for any elliptic curve. The research proposes new hardware framework for modular multiplication and modular multiplicative inverse which is adopted for group operations involved in ECDSA. Every hardware design offered are simulated using modelsim register transfer logic (RTL) simulator. Field programmable gate array (FPGA) implementation of var- ious modules within ECDSA circuit is compared with equivalent existing techniques that is both hardware and software based to highlight the superiority of the suggested work. The results showcased prove that the designs implemented are both area and speed efficient with faster execution and less resource utilization while maintaining the same level of security. The suggested ECDSA structure could replace the software equivalent of digital signatures in hardware blockchain to thwart software attacks and to provide better data protection.
Improvement on KNN using genetic algorithm and combined feature extraction to identify COVID-19 sufferers based on CT scan image Radityo Adi Nugroho; Arie Sapta Nugraha; Aylwin Al Rasyid; Fenny Winda Rahayu
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 5: October 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i5.18535

Abstract

Coronavirus disease 2019 (COVID-19) has spread throughout the world. The detection of this disease is usually carried out using the reverse transcriptase polymerase chain reaction (RT-PCR) swab test. However, limited resources became an obstacle to carrying out the massive test. To solve this problem, computerized tomography (CT) scan images are used as one of the solutions to detect the sufferer. This technique has been used by researchers but mostly using classifiers that required high resources, such as convolutional neural network (CNN). In this study, we proposed a way to classify the CT scan images by using the more efficient classifier, k-nearest neighbors (KNN), for images that are processed using a combination of these feature extraction methods, Haralick, histogram, and local binary pattern. Genetic algorithm is also used for feature selection. The results showed that the proposed method was able to improve KNN performance, with the best accuracy of 93.30% for the combination of Haralick and local binary pattern feature extraction, and the best area under the curve (AUC) for the combination of Haralick, histogram, and local binary pattern with a value of 0.948. The best accuracy of our models also outperforms CNN by a 4.3% margin.
Dynamic power allocation and scheduling for MIMO RF energy harvesting wireless sensor platforms Amar Esse; Khaizuran Abdullah; Mohamed Hadi Habaebi; Huda Adibah Mohd Ramli; Ani Liza Asnawi; Md. Rafiqul Islam
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 5: October 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i5.20413

Abstract

Radio frequency (RF) energy harvesting systems are enabling new evolution towards charging low energy wireless devices, especially wireless sensor networks (WSN). This evolution is sparked by the development of low-energy micro-controller units (MCU). This article presents a practical multiple input multiple output (MIMO) RF energy-harvesting platform for WSN. The RF energy is sourced from a dedicated access point (AP). The sensor node is equipped with multiple antennas with diverse frequency responses. Moreover, the platform allows for simultaneous information and energy transfer without sacrificing system duplexity, unlike time-switching RF harvesting systems where data is transmitted only for a portion of the total transmission duty cycle, or power-splitting systems where the power difference between the information signal (IS) and energy signal (ES) is neglected. The proposed platform addresses the gap between those two. Furthermore, system simulation and two energy scheduling methods between AP and sensor node (SN) are presented, namely, Continuous power stream (CPS) and intermittent power stream (IPS).
Analysis of the reliability of the components of a multiservice communication network based on the theory of fuzzy sets Alevtina Aleksandrovna Muradova; Aybek Fayzullaevich Khaytbaev
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 5: October 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i5.19854

Abstract

The article presents the results of modeling the solution to the problem of determining the reliability of the components of a multiservice communication network (MCN) based on the theory of fuzzy sets. The main characteristics of the equipment that affect the reliability parameters of the MCN are given. To solve the problem of determining the reliability of MCN components based on the theory of fuzzy sets, a multiservice network is presented in the form of a hierarchical diagram, which shows the main components of each network level. A multiservice network is presented as a parameter of the U function. The reliable state of the MCN depends on the state of the equipment at the corresponding levels. The results of modeling the solution to the problem of determining the reliability of MCN components based on the theory of fuzzy sets are presented using the mathematical apparatus of the theory of fuzzy sets and fuzzy logic in MATLAB fuzzy logic toolbox, fuzzyTECH.
Analysis of frequent itemset generation based on trie data structure in Apriori algorithm Ade Hodijah; Urip Teguh Setijohatmo
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 5: October 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i5.19273

Abstract

Apriori is one technique of data mining association rules that aims to extract correlations between sets of items in the transaction database. The main problem with the Apriori algorithm is the process of scanning databases repeatedly to generate itemset candidates. This research examines the combination of pruning by using the trieapproach and multi-thread implementation in three algorithms to obtain frequent itemset. Trie is a data structure in the form of an ordered tree to store a set of strings where every node in the tree contains the same prefix. The use of a full combination trie (different from frequent pattern (FP) tree using links) allows the implementation of arrays and the hash calculation to achieve the addressing of itemset combination. In this research, the measure to get the address is called Hash-node calculation used to update support value. For these three alternatives, run time processing is analyzed based on the number of itemset combinations and transaction data at a certain minimum support value. The experimental results show that an algorithm thatexploits resource capabilities by applying multi-threadperforms almost seven times betterthanan algorithm implemented in single-thread in calculating hash-node. The fastest run time of the multi-thread approach is 43 minutes with 150-itemset combinations on 100,000 transaction data.
Quantitative approach for reclassification of the spatial cluster of archipelagos in Maluku Province for the basis of forest development Patrich Papilaya; Endang Suhendang; I Nengah Surati Jaya; Teddy Rusolono
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 5: October 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i5.17041

Abstract

In natural resource management, it is necessary to group regions based on the similarity of their spatial and non-spatial characteristics, to efficiency and effectiveness Therefore, this study describes the re-grouping of the twelve island clusters established by the provincial government of Maluku into more homogeneous classes. The re-grouping was carried out based on the biophysical conditions of the regions, therefore, it could be used as the basis for determining the forest management units. The results showed that the twelve designated island clusters could be simplified to eight more homogeneous island clusters with 86.4% accuracy and 82.2 validation. It also showed that there were thirteen significant changes in the grouping of clusters of the island, including the horticultural crop area (Bf) and horticultural crop production (E). Moreover, when the island cluster is reclassified into 5 classes, the grouping would be more accurate, with 94.9% accuracy and 92.4% validation. This study concludes that there are two dominant factors in the classification of the island cluster in Maluku province namely, biophysical and social.
Cervical cancer classification using convolutional neural network-support vector machine Jane Eva Aurelia; Zuherman Rustam; Ilsya Wirasati
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 5: October 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i5.20406

Abstract

Cervical cancer is the second most common cancer in women worldwide, and occurs when there are presences of abnormal cells in the cervix, which continue to grow uncontrollably. In the early stages, cervical cancer indications are not perceptible; however, it is easily detected with different forms of machine learning methods, such as the convolutional neural network (CNN). This is a popular method with a wide range of applications and known for its high accuracy value. Moreover, there is a support vector machine (SVM) with several kernel functions that is commonly used in the classification of diseases, and also known for its high accuracy value. Therefore, the combination of CNN–SVM with several linear kernels functions as classifier for the categorization of cervical cancer.
Comparative study of standalone classifier and ensemble classifier Tri Okta Priasni; Teddy Oswari
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 5: October 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i5.19508

Abstract

Ensemble learning is one of machine learning method that can solve performance measurement problem. Standalone classifiers often show a poor performance result, thus why combining them with ensemble methods can improve their performance scores. Ensemble learning has several methods, in this study, three methods of ensemble learning are compared with standalone classifiers of support vector machine, Naïve Bayes, and decision tree. bagging, AdaBoost, and voting are the ensemble methods that are combined then compared to standalone classifiers. From 1670 dataset of twitter mentions about tourist’s attraction, ensemble methods did not show a specific improvement in accuracy and precision measurement since it generated the same result as decision tree as standalone classifier. Bagging method showed a significant development in recall, f-measure, and area under curve (AUC) measurement. For overall performance, decision tree as standalone classifier and decision tree with AdaBoost method have the highest score for accuracy and precision measurements, meanwhile support vector machine with bagging method has the highest score for recall, f-measure, and AUC.

Page 2 of 4 | Total Record : 36

 1   2   3   4 

Filter by Year

2021 2021


Reset
Filter By Issues
All Issue Vol 20, No 3: June 2022 Vol 20, No 2: April 2022 Vol 20, No 1: February 2022 Vol 19, No 6: December 2021 Vol 19, No 5: October 2021 Vol 19, No 4: August 2021 Vol 19, No 3: June 2021 Vol 19, No 2: April 2021 Vol 19, No 1: February 2021 Vol 18, No 6: December 2020 Vol 18, No 5: October 2020 Vol 18, No 4: August 2020 Vol 18, No 3: June 2020 Vol 18, No 2: April 2020 Vol 18, No 1: February 2020 Vol 17, No 6: December 2019 Vol 17, No 5: October 2019 Vol 17, No 4: August 2019 Vol 17, No 3: June 2019 Vol 17, No 2: April 2019 Vol 17, No 1: February 2019 Vol 16, No 6: December 2018 Vol 16, No 5: October 2018 Vol 16, No 4: August 2018 Vol 16, No 3: June 2018 Vol 16, No 2: April 2018 Vol 16, No 1: February 2018 Vol 15, No 4: December 2017 Vol 15, No 3: September 2017 Vol 15, No 2: June 2017 Vol 15, No 1: March 2017 Vol 14, No 4: December 2016 Vol 14, No 3: September 2016 Vol 14, No 2: June 2016 Vol 14, No 1: March 2016 Vol 13, No 4: December 2015 Vol 13, No 3: September 2015 Vol 13, No 2: June 2015 Vol 13, No 1: March 2015 Vol 12, No 4: December 2014 Vol 12, No 3: September 2014 Vol 12, No 2: June 2014 Vol 12, No 1: March 2014 Vol 11, No 4: December 2013 Vol 11, No 3: September 2013 Vol 11, No 2: June 2013 Vol 11, No 1: March 2013 Vol 10, No 4: December 2012 Vol 10, No 3: September 2012 Vol 10, No 2: June 2012 Vol 10, No 1: March 2012 Vol 9, No 3: December 2011 Vol 9, No 2: August 2011 Vol 9, No 1: April 2011 Vol 8, No 3: December 2010 Vol 8, No 2: August 2010 Vol 8, No 1: April 2010 Vol 7, No 3: December 2009 Vol 7, No 2: August 2009 Vol 7, No 1: April 2009 Vol 6, No 3: December 2008 Vol 6, No 2: August 2008 Vol 6, No 1: April 2008 Vol 5, No 3: December 2007 Vol 5, No 2: August 2007 Vol 5, No 1: April 2007 Vol 4, No 3: December 2006 Vol 4, No 2: August 2006 Vol 4, No 1: April 2006 Vol 3, No 3: December 2005 Vol 3, No 2: August 2005 Vol 3, No 1: April 2005 Vol 2, No 1: April 2004 More Issue