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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 66 Documents
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Search results for , issue "Vol 18, No 3: June 2020" : 66 Documents clear
Convolutional neural network for maize leaf disease image classification Mohammad Syarief; Wahyudi Setiawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.14840

Abstract

This article discusses the maize leaf disease image classification. The experimental images consist of 200 images with 4 classes: healthy, cercospora, common rust and northern leaf blight. There are 2 steps: feature extraction and classification. Feature extraction obtains features automatically using convolutional neural network (CNN). Seven CNN models were tested i.e AlexNet, virtual geometry group (VGG) 16, VGG19, GoogleNet, Inception-V3, residual network 50 (ResNet50) and ResNet101. While the classification using machine learning methods include k-Nearest neighbor, decision tree and support vector machine. Based on the testing results, the best classification was AlexNet and support vector machine with accuracy, sensitivity, specificity of 93.5%, 95.08%, and 93%, respectively.
UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation Anindya Apriliyanti Pravitasari; Nur Iriawan; Mawanda Almuhayar; Taufik Azmi; Irhamah Irhamah; Kartika Fithriasari; Santi Wulan Purnami; Widiana Ferriastuti
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.14753

Abstract

A brain tumor is one of a deadly disease that needs high accuracy in its medical surgery. Brain tumor detection can be done through magnetic resonance imaging (MRI). Image segmentation for the MRI brain tumor aims to separate the tumor area (as the region of interest or ROI) with a healthy brain and provide a clear boundary of the tumor. This study classifies the ROI and non-ROI using fully convolutional network with new architecture, namely UNet-VGG16. This model or architecture is a hybrid of U-Net and VGG16 with transfer Learning to simplify the U-Net architecture. This method has a high accuracy of about 96.1% in the learning dataset. The validation is done by calculating the correct classification ratio (CCR) to comparing the segmentation result with the ground truth. The CCR value shows that this UNet-VGG16 could recognize the brain tumor area with a mean of CCR value is about 95.69%.
Web-app realization of Shor’s quantum factoring algorithm and Grover’s quantum search algorithm Arya Wicaksana; Anthony Anthony; Adjie Wahyu Wicaksono
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.14755

Abstract

Quantum algorithms are well-known for their quadratic if not exponential speedup over their classical counterparts. The two widely-known quantum algorithms are Shor’s quantum factoring algorithm and Grover’s quantum search algorithm. Shor’s quantum factoring algorithm could perform integer factorization in O(logN). Grover’s quantum search algorithm could solve the unsorted search problem in O(√N). However, both algorithms are introduced as theoretical concepts in the original papers due to the limitations of quantum technology at that time. In this paper, an improved way is presented to realize the two algorithms into a web application using state-of-the-art quantum technology. The web-app is designed and built considering the uses of a quantum simulator and libraries provided by ProjectQ and Rigetti Forest. The result shows that both algorithms are realizable into web-applications.
Tractable computation in outage performance analysis of relay selection NOMA Minh-Sang Van Nguyen; Dinh-Thuan Do
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.12826

Abstract

In recent years, using full-duplex (FD) transmission model provides enhanced bandwidth efficiency and improved performance for non-orthogonal multiple access (NOMA) system. However, lack of papers have investigated FD relay together with relay selection issue to improve performance of NOMA system. The problems in power allocation for two NOMA users satisfying fairness as well as relay selection strategy are studied in this paper. By considering the outage performance of proposed scheme with its vital result, general NOMA wireless networks can be developed for future networks due to its improved performance. Simulation results show that the relaying selection scheme can achieve a significant performance improvement by increasing required quantity of relay.
Stochastic renewable energy resources integrated multi-objective optimal power flow Sundaram B. Pandya; Hitesh R. Jariwala
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.13466

Abstract

The modern state of electrical system consists the conventional generating units along with the sources of renewable energy. The proposed article recommends a method for the solution of single and multi-objective optimal power flow, integrating wind and solar output energy with traditional coal-based generating stations. In the first part of the article, the two wind power plants and one solar PV power plants are incorporated with the thermal power plants. The optimal power flow problem of single and conflicting multi-objectives are taken with this scenario. The second part of the paper, solar power plant is replaced with another wind power plant with the conventional coal-based power plants. The techno-economic analysis are done with this state of electrical system. In proposed work, lognormal and weibull probability distribution functions are also utilized for predicting solar and wind outputs, respectively. A non-dominated multi-objective moth flame optimization technique is used for the optimization issue. The fuzzy decision-making approach is applied for extracting the best compromise solution. The results are validated though adapted IEEE-30 bus test system, which is incorporated with wind and solar generating plants.
Deep hypersphere embedding for real-time face recognition Ryann Alimuin; Elmer Dadios; Jonathan Dayao; Shearyl Arenas
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.14787

Abstract

With the advancement of human-computer interaction capabilities of robots, computer vision surveillance systems involving security yields a large impact in the research industry by helping in digitalization of certain security processes. Recognizing a face in the computer vision involves identification and classification of which faces belongs to the same person by means of comparing face embedding vectors. In an organization that has a large and diverse labelled dataset on a large number of epoch, oftentimes, creates a training difficulties involving incompatibility in different versions of face embedding that leads to poor face recognition accuracy. In this paper, we will design and implement robotic vision security surveillance system incorporating hybrid combination of MTCNN for face detection, and FaceNet as the unified embedding for face recognition and clustering.
A review on region of interest-based hybrid medical image compression algorithms Suhaila Ab Aziz; Suriani Mohd Sam; Norliza Mohamed; Salwani Mohd Daud; Siti Zaleha Abdul Rashid; Hafiza Abas; Muhammad Fathi Yusof; Rudzidatul Akmam Dziyauddin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.14900

Abstract

Digital medical images have become a vital resource that supports decision-making and treatment procedures in healthcare facilities. The medical image consumes large sizes of memory, and the size keeps on growth due to the trend of medical image technology. The technology of telemedicine encourages the medical practitioner to share the medical image to support knowledge sharing to diagnose and analyse the image. The healthcare system needs to ensure distributes the medical image accurately with zero loss of information, fast and secure. Image compression is beneficial in ensuring that achieve the goal of sharing this data. The region of interest-based hybrid medical compression algorithm plays the parts to reduce the image size and shorten the time of medical image compression process. Various studies have enhanced by combining numerous techniques to get an ideal result. This paper reviews the previous works conducted on a region of interest-based hybrid medical image compression algorithms.
Simple broadband circularly polarized monopole antenna with two asymmetrically connected U-shaped parasitic strips and defective ground plane Hussein Alsariera; Z. Zakaria; A. A. M. Isa; Sameer Alani; M. Y. Zeain; Othman S. Al-Heety; S. Ahmed; Mussa Mabrok; R. Alahnomi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.14313

Abstract

A simple compact broadband circularly polarized monopole antenna, which comprises a simple monopole, a modified ground plane with an implementing triangular stub and two asymmetrically connected U-shaped parasitic strips, is proposed. Simulation results show that the proposed compact antenna (0.62λo×0.68λo) achieves a 10-dB impedance bandwidth (IBW) of 111% (1.7 to 5.95 GHz) and a 3-dB axial ratio bandwidth (ARBW) of 61% (3.3–6.2 GHz) with a peak gain between 2.9–4 dBi for the entire ARBW. With its broad IBW and ARBW, compact size and simple structure, the proposed antenna is suitable for different wireless communications.
Comparison of machine learning performance for earthquake prediction in Indonesia using 30 years historical data I Made Murwantara; Pujianto Yugopuspito; Rickhen Hermawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.14756

Abstract

Indonesia resides on most earthquake region with more than 100 active volcanoes,and high number of seismic activities per year. In order to reduce the casualty, some method to predict earthquake have been developed to estimate the seismic movement. However, most prediction use only short term of historical data to predict the incoming earthquake, which has limitation on model performance. This work uses medium to long term earthquake historical data that were collected from 2 local government bodies and 8 legitimate international sources. We make an estimation of a mediumto-long term prediction via Machine Learning algorithms, which are Multinomial Logistic Regression, Support Vector Machine and Na¨ıve Bayes, and compares their performance. This work shows that the Support Vector Machine outperforms other method. We compare the Root Mean Square Error computation results that lead us into how concentrated data is around the line of best fit, where the Multinomial Logistic Regression is 0.777, Na¨ıve Bayes is 0.922 and Support Vector Machine is 0.751. In predicting future earthquake, Support Vector Machine outperforms other two methods that produce significant distance and magnitude to current earthquake report.
A new model for large dataset dimensionality reduction based on teaching learning-based optimization and logistic regression Hind Raad Ibraheem; Zahraa Faiz Hussain; Sura Mazin Ali; Mohammad Aljanabi; Mostafa Abdulghafoor Mohammed; Tole Sutikno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.13764

Abstract

One of the human diseases with a high rate of mortality each year is breast cancer (BC). Among all the forms of cancer, BC is the commonest cause of death among women globally. Some of the effective ways of data classification are data mining and classification methods. These methods are particularly efficient in the medical field due to the presence of irrelevant and redundant attributes in medical datasets. Such redundant attributes are not needed to obtain an accurate estimation of disease diagnosis. Teaching learning-based optimization (TLBO) is a new metaheuristic that has been successfully applied to several intractable optimization problems in recent years. This paper presents the use of a multi-objective TLBO algorithm for the selection of feature subsets in automatic BC diagnosis. For the classification task in this work, the logistic regression (LR) method was deployed. From the results, the projected method produced better BC dataset classification accuracy (classified into malignant and benign). This result showed that the projected TLBO is an efficient features optimization technique for sustaining data-based decision-making systems.

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