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
Classification of water stress in cultured Sunagoke moss using deep learning Yusuf Hendrawan; Retno Damayanti; Dimas Firmanda Al Riza; Mochamad Bagus Hermanto
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.v19i5.20063

Abstract

Water stress greatly determines plant yield as it affects plant metabolism, photosynthesis rate, chlorophyll content index, number of leaves, physiological, biochemical compound, and vegetative growth. The research aimed to detect and classify water stress of cultured Sunagoke moss into several categories i.e. dry, semi-dry, wet, and soak by using a low-cost commercial visible light camera combined with a deep learning model. Cultured Sunagoke moss is a commercial product which has the potential use as rooftop-greening and wall-greening material. This research compared the performance of four convolutional neural network models, such as SqueezeNet, GoogLeNet, ResNet50, and AlexNet. The best convolutional neural network model according to the training and validation result was ResNet50 with RMSProp optimizer, 30 epoch, and 128 mini-batch size; this also gained an accuracy rate at 87.50%. However, the best result of the convolutional neural network model on data testing using confusion matrices on different data sample was ResNet50 with Adam optimizer, 30 epoch, 128 mini-batch size, and average testing accuracy of 94.15%. It can be concluded that based on the overall results, convolutional neural network model seems promising as a smart irrigation system that real-time, non-destructive, rapid, and precise method when controlling water stress of plants.
Malicious vehicle detection based on beta reputation and trust management for secure communication in smart automotive cars network Dilshad Ara Hossain; S. M. Salim Reza
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.v19i5.19358

Abstract

High reliance on wireless network connectivity makes the vehicular ad hoc network (VANET) vulnerable to several kinds of cyber security threats. Malicious vehicles accessing the network can lead to hazardous situation by disseminating misleading information or data in the network or by performing cyber-attacks. It is a requirement that the information must be originated from the authentic and authorized vehicle and confidentiality must be maintained. In these circumstances, to protect the network from malicious vehicles, reputation system based on beta probability distribution with trust management model has been proposed to differentiate trustworthy vehicles from malicious vehicles. The trust model is based on adaptive neuro fuzzy inference system (ANFIS) which takes trust metrics as input to evaluate the trustworthiness of the vehicles. The simulation platform for the model is in MATLAB. Simulation results show that the vehicles need at least 80% trustworthiness to be considered as a trusted vehicle in the network.
Visible light communication using new Flip-FBMC modulation system technique Abdullah Ali Qasim; Husam Noman Mohammed Ali; Ali Kadhim Obaid; M. F. L. Abdullah; R. Talib; M. S. M. Gismalla; Wafi A. Mabrouk
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.v19i5.18407

Abstract

Filter bank multi-carrier (FBMC) modulation in the visible light communication (VLC) system is one of the most promising modulation systems in optical wireless communications (OWC), especially in 5G and 6G future applications. FBMC has a wide bandwidth compared to other modulation systems. One of the highest degree essential conditions for utilising the signal in VLC is that the signal is real positive, the signal is agreeable with intensity modulation/direct detection (IM/DD), where Hermitian symmetry (H.S) is utilised to get a real signal (RE) and to be unipolar direct current (DC)-bias is used. Here the challenge arises as this method increases complicating, due to the modulation of the N number of frequency symbols, these symbols need 2N inverse fast fourier transform (IFFT) and fast fourier transform (FFT), in addition to energy consumption. This research focused on the time domain and not the frequency domain by using the traditional complex FBMC generation signal, and to obtain the RE signal by placing the RE signal side by side with the imaginary signal (IMs) in a row, and then using new Flip-FBMC technology, which saves more energy. The proposed technologies provide approximately 57% of the number of IFFT/FFT. The use of Flip-FBMC technology consumes less energy than traditional technologies with better bit error rate (BER) performance.
Handover evaluation of UMTS-WiMAX networks Saif A. Abdulhussein; Riyadh A. Abdulhussein
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.v19i5.20411

Abstract

Recently, data traffic movement through a wireless channel is assisted by suggesting and implementing many mechanisms, to achieve the speedy increasing importunity and popularity of the wireless networks. Various wireless technologies can be copulated to develop a heterogeneous network, which is a candidate towards (4G) networks. OPNET modeler (14.5) is used to design simulation modules of the heterogeneous network. During device connection between the worldwide interoperability for microwave access (WiMAX) and universal mobile telecommunication system (UMTS) networks, Performance metrics such as; Jitter end-to-end delay (E-2-E) Throughput is used. The results of the simulation are measured to determine the efficiency of the transfer using WiMAX-UMTS according to the selected metrics. The WiMAX-UMTS has shown valuable improvement in Process Durability, reduction of E-2-E delay, and Jitter. The maximum amount of data transfer and the least amount of delay and Jitter is at 250 sec. Because of the handover operations and data transfer momentum, the worst-case passes in the network when 618 sec is the minimum amount. The efficiency of throughput for WiMAX equal to 0.092666% as for the efficiency of throughput for UMTS equal to 4.633333*10-6 % whereas the E-2-E efficiency a delay equal to 0.5466%.
Rule-based lip-syncing algorithm for virtual character in voice chatbot Felicia Priscilla Lovely; Arya Wicaksana
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.v19i5.19824

Abstract

Virtual characters changed the way we interact with computers. The underlying key for a believable virtual character is accurate synchronization between the visual (lip movements) and the audio (speech) in real-time. This work develops a 3D model for the virtual character and implements the rule-based lip-syncing algorithm for the virtual character's lip movements. We use the Jacob voice chatbot as the platform for the design and implementation of the virtual character. Thus, audio-driven articulation and manual mapping methods are considered suitable for real-time applications such as Jacob. We evaluate the proposed virtual character using hedonic motivation system adoption model (HMSAM) with 70 users. The HMSAM results for the behavioral intention to use is 91.74%, and the immersion is 72.95%. The average score for all aspects of the HMSAM is 85.50%. The rule-based lip-syncing algorithm accurately synchronizes the lip movements with the Jacob voice chatbot's speech in real-time.
Large scale data analysis using MLlib Ahmed Hussein Ali; Maan Nawaf Abbod; Mohammed Khamees Khaleel; Mostafa Abdulghafoor Mohammed; Tole Sutikno
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.v19i5.21059

Abstract

Recent advancements in the internet, social media, and internet of things (IoT) devices have significantly increased the amount of data generated in a variety of formats. The data must be converted into formats that is easily handled by the data analysis techniques. It is mathematically and physically expensive to apply machine learning algorithms to big and complicated data sets. It is a resource-intensive process that necessitates a huge amount of logical and physical resources. Machine learning is a sophisticated data analytics technology that has gained in importance as a result of the massive amount of data generated daily that needs to be examined. Apache Spark machine learning library (MLlib) is one of the big data analysis platforms that provides a variety of outstanding functions for various machine learning tasks, spanning from classification to regression and dimension reduction. From a computational standpoint, this research investigated Apache Spark MLlib 2.0 as an open source, autonomous, scalable, and distributed learning library. Several real-world machine learning experiments are carried out in order to evaluate the properties of the platform on a qualitative and quantitative level. Some of the fundamental concepts and approaches for developing a scalable data model in a distributed environment are also discussed.

Page 4 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