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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 2,614 Documents
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Recent systematic review on student performance prediction using backpropagation algorithms Edi Ismanto; Hadhrami Ab Ghani; Nurul Izrin Md Saleh; Januar Al Amien; Rahmad Gunawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 3: June 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i3.21963

Abstract

A comprehensive systematic study was carried out in order to identify various deep learning methods developed and used for predicting student academic performance. Predicting academic performance allows for the implementation of various preventive and supportive measures earlier in order to improve academic performance and reduce failure and dropout rates. Although machine learning schemes were once popular, deep learning algorithms are now being investigated to solve difficult predictions of student performance in larger datasets with more data attributes. Deep neural network prediction methods with clear modelling and parameter measurements formulated on publicly available and recognised datasets are the focus of the research. Widely used for academic performance prediction, backpropagation algorithms have been trained and tested with various datasets, especially those related to learning management systems (LMS) and massive open online courses (MOOC). The most widely used prediction method appears to be the standard artificial neural network approach. The long-short-term memory (LSTM) approach has been reported to achieve an accuracy of around 87 percent for temporal student performance data. The number of papers that study and improve this method shows that there is a clear rise in deep learning-based academic performance prediction over the last few years
A novel fern-like lines detection using a hybrid of pre-trained convolutional neural network model and Frangi filter Heri Pratikno; Mohd Zamri Ibrahim; Jusak Jusak
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 3: June 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i3.23319

Abstract

Full ferning is the peak of the formation of a salt crystallization line pattern shaped like a fern tree in a woman’s saliva at the time of ovulation. The main problem in this study is how to detect the shape of the salivary ferning line patterns that are transparent, irregular and the surface lighting is uneven. This study aims to detect transparent and irregular lines on the salivary ferning surface using a comparison of 15 pre-trained convolutional neural network models. To detect fern-like lines on transparent and irregular layers, a pre-processing stage using the Frangi filter is required. The pre-trained convolutional neural network model is a promising framework with high precision and accuracy for detecting fern-like lines in salivary ferning. The results of this study using the fixed learning rate model ResNet50 showed the best performance with an error rate of 4.37% and an accuracy of 95.63%. Meanwhile, in implementing the automatic learning rate, ResNet18 achieved the best results with an error rate of 1.99% and an accuracy of 98.01%. The results of visual detection of fern-like lines in salivary ferning using a patch size of 34×34 pixels indicate that the ResNet34 model gave the best appearance
Enhancement process of AES: a lightweight cryptography algorithm-AES for constrained devices Hussein M. Mohammad; Alharith A. Abdullah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 3: June 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i3.23297

Abstract

The restricted devices have a small memory, simple processor, and limited power. To secure them, we need lightweight cryptography algorithms, taking into account the limited specifications. Lightweight cryptography (LWC) algorithms provide confidentiality and maintain information integrity for devices with limited resources. This paper improves and enhances advanced encryption standard (AES) algorithm by reducing algorithm computation power and improving cryptography performance from the point of resource constraint devices. The proposed algorithm is fast and lightweight, which is essential for securing all kinds of data. Besides, the use of mix column overhead is dispensing with, and the ciphertext is processed by the mathematical function (continued fraction) to compress the ciphertext and make it more confusing and also to increasing the data transfer speed. The proposed lightweight cryptography-AES (LWC-AES) algorithm highly suitable for the timely execution of encryption and decryption (such as when encrypt text has (45.1 KB) encryption execution time for AES was (294 ms), while in LWC-AES was (280 ms), as well as suitable for the memory size of the resource-constrained devices for all types of data, than the AES algorithm. The proposed algorithm tested for security analysis using the Avalanche Effect parameter, and this test showed acceptable and within required security results
Energy-efficient speed profile: an optimal approach with fixed running time An Thi Hoai Thu Anh; Nguyen Van Quyen
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 3: June 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i3.19525

Abstract

Tracking the optimal speed profile in electric train operation has been proposed as an efficient and feasible solution for not only reducing energy consumption, but also no at costs to upgrading the existing railway systems. This paper focuses on finding the optimal speed profile based on Pontryagin’s maximum principle (PMP) while ensuring the fixed running time, and comparing energy saving levels in the cases of applying or not applying PMP. The way to determine the fixed running time also differs from works published is to calculate the total trip time equal to scheduled timetable exactly. Calculating accelerating time ta, coasting time tc, braking time tb via values of maximum speed vh, braking speed vbof optimal speed profile. The other hands, vh and vb are determined by solving nonlinear equations with constraint condition: the running time equal to the demand time. Simulation results with data collected from electrified trains of Cat Linh-Ha Dong metro line, Vietnam show that energy reduction for the entire route when PMP utilization is up to 8.7% and running time complied with scheduled timetables.

Page 262 of 262 | Total Record : 2614

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