TELKOMNIKA (Telecommunication Computing Electronics and Control)
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)
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Database techniques for resilient network monitoring and inspection
Zahraa A. Jaaz;
Suha Sahib Oleiwi;
Seba Aziz Sahy;
Israa Albarazanchi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v18i5.14305
Network connection logs have long been recognized as integral to proper network security, maintenance, and performance management. This paper provides a development of distributed systems and write optimized databases: However, even a somewhat sizable network will generate large amounts of logs at very high rates. This paper explains why many storage methods are insufficient for providing real-time analysis on sizable datasets and examines database techniques attempt to address this challenge. We argue that sufficient methods include distributing storage, computation, and write optimized datastructures (WOD). Diventi, a project developed by Sandia National Laboratories, is here used to evaluate the potential of WODs to manage large datasets of network connection logs. It can ingest billions of connection logs at rates over 100,000 events per second while allowing most queries to complete in under one second. Storage and computation distribution are then evaluated using Elastic-search, an open-source distributed search and analytics engine. Then, to provide an example application of these databases, we develop a simple analytic which collects statistical information and classifies IP addresses based upon behavior. Finally, we examine the results of running the proposed analytic in real-time upon broconn (now Zeek) flow data collected by Diventi at IEEE/ACM Supercomputing 2019.
Gender voice classification with huge accuracy rate
Mustafa Sahib Shareef;
Thulfiqar Abd;
Yaqeen S. Mezaal
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v18i5.13717
Gender voice recognition stands for an imperative research field in acoustics and speech processing as human voice shows very remarkable aspects. This study investigates speech signals to devise a gender classifier by speech analysis to forecast the gender of the speaker by investigating diverse parameters of the voice sample. A database has 2270 voice samples of celebrities, both male and female. Through Mel frequency cepstrum coefficient (MFCC), vector quantization (VQ), and machine learning algorithm (J 48), an accuracy of about 100% is achieved by the proposed classification technique based on data mining and Java script.
Automatic sound synthesis using the fly algorithm
Zainab A. Abbood;
Imad S. Alshawi;
Asaad A. Alhijaj;
Franck P. Vidal
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v18i5.15665
Our study is demonstrated a new type of evolutionary sound synthesis method. This work based on the fly algorithm, a cooperative co-evolution algorithm; it is derived from the Parisian evolution approach. The algorithm has relatively amended the position of individuals (the Flies) represented by 3-D points. The fly algorithm has successfully investigated in different applications, starting with a real-time stereo vision for robotics. Also, the algorithm shows promising results in tomography to reconstruct 3-D images. The final application of the fly algorithm was generating artistic images, such as digital mosaics. In all these applications, the flies’representation started for simple, 3-D points, to complex one, the structure of 9-elements. Our method follows evolutionary digital art with the fly algorithm in representing the pattern of the flies. They represented in a way of having their structure. This structure includes position, colour, rotation angle, and size. Our algorithm has the benefit of graphics processing units (GPUs) to generate the sound waveform using the modern OpenGL shading language.
A new eliminating EOG artifacts technique using combined decomposition methods with CCA and H.P.F. techniques
Fadia Noori Hummadi Al-Nuaimy
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v18i5.14143
Normally, the collected EEG signals from the human scalp cortex by using the non-invasive EEG collection methods were contaminated with artifacts, like an eye electrical activity, leading to increases in the challenges in analyzing the electroencephalogram for obtaining useful clinical information. In this paper, we do a comparison of using two decomposing methods (DWT and EMD) with CCA technique or High Pass Filter, for the elimination of eye artifacts from EEG. The eye artifacts (EOG) signals were extracted from the un-cleaned or raw EEG signals by DWT and EMD with CCA approach or H.P.F. The root means square error ratio of the uncontaminated EEG signal to the contaminated EEG signal with eye artifacts were the performance indicators for both elimination methods, which indicate that the combined CCA method outperforms the combined H.P.F method in the elimination of eye blinking contamination artifact from the EEG signal.
Integrated bio-search approaches with multi-objective algorithms for optimization and classification problem
Mohammad Aizat Basir;
Mohamed Saifullah Hussin;
Yuhanis Yusof
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v18i5.15141
Optimal selection of features is very difficult and crucial to achieve, particularly for the task of classification. It is due to the traditional method of selecting features that function independently and generated the collection of irrelevant features, which therefore affects the quality of the accuracy of the classification. The goal of this paper is to leverage the potential of bio-inspired search algorithms, together with wrapper, in optimizing multi-objective algorithms, namely ENORA and NSGA-II to generate an optimal set of features. The main steps are to idealize the combination of ENORA and NSGA-II with suitable bio-search algorithms where multiple subset generation has been implemented. The next step is to validate the optimum feature set by conducting a subset evaluation. Eight (8) comparison datasets of various sizes have been deliberately selected to be checked. Results shown that the ideal combination of multi-objective algorithms, namely ENORA and NSGA-II, with the selected bio-inspired search algorithm is promising to achieve a better optimal solution (i.e. a best features with higher classification accuracy) for the selected datasets. This discovery implies that the ability of bio-inspired wrapper/filtered system algorithms will boost the efficiency of ENORA and NSGA-II for the task of selecting and classifying features.
Precipitation prediction using recurrent neural networks and long short-term memory
Mishka Alditya Priatna;
Esmeralda C. Djamal
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v18i5.14887
Prediction of meteorological variables such as precipitation, temperature, wind speed, and solar radiation is beneficial for human life. The variable observations data is available from time to time for more than thirty years, scattered each observation station makes the opportunity to map patterns into predictions. However, the complexity of weather variables is very high, one of which is influenced by Decadal phenomena such as El-Nino Southern Oscillation and IOD. Weather predictions can be reviewed for the duration, prediction variables, and observation stations. This research proposed precipitation prediction using recurrent neural networks and long short-term memory. Experiments were carried out using the prediction duration factor, the period as a feature and the amount of data set used, and the optimization model. The results showed that the time-lapse as a shorter feature gives good accuracy. Also, the duration of weekly predictions provides more accuracy than monthly, which is 85.71% compared to 83.33% of the validation data.
A new algorithm for implementing message authentication and integrity in software implementations
Alaa Wagih Abdul Qader;
Israa Ezzat Salem;
Haider Rasheed Abdulshaheed
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v18i5.15276
IT systems and data that you store, and process are valuable resources that need protection. Validation and reliability of information are essential in networks and computer systems. The communicating is done by two parties via an unsafe channel require a way to validate the data spent by one party as valid (or unaltered) by the other party. In our study, we suggest new one-way defragmentation algorithm to implement message authentication and integration in program execution. These software applications are readily available and freely available because most of the hash functions are faster than their existing radioactive blocks.
Ananas comosus crown image thresholding and crop counting using a colour space transformation scheme
Wan Nurazwin Syazwani Rahimi;
Muhammad Asraf H.;
Megat Syahirul Amin Megat Ali
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v18i5.13895
The implementation of unmanned aerial vehicle (UAV) technology having image processing capabilities provides an alternative way to observe pineapple crowns captured from aerial images. In the majority of pineapple plantations, an agricultural officer will physically count the crop yield prior to harvesting the Ananas Comosus, also known as pineapple. This process is particularly evident in large plantation areas to accurately identify pineapple numbers. To alleviate this issue, given it is both time-consuming and arduous, automating the process using image processing is suggested. In this study, the possibilities and comparisons between two techniques associated with an image thresholding scheme known as HSV and L*A*B* colour space schemes were implemented. This was followed by determining the threshold by applying an automatic counting (AC) method to count the crop yield. The results of the study found that by applying colour thresholding for segmentation, it improved the low contrast image due to different heights and illumination levels on the acquired colour image. The images that were acquired using a UAV revealed that the best distance for capturing the images was at the height of three (3) metres above ground level. The results also confirm that the HSV colour space provides a more efficient approach with an average error increment of 47.6% when compared to the L*A*B*colour space.
Comparison of calcium carbonate and titania particles on improving color homogeneity and luminous flux of WLEDs
Thinh Cong Tran;
Nguyen Doan Quoc Anh;
Nguyen Thi Phuong Loan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v18i5.13552
In this paper, the experiments on phosphor-converted LEDs (pc-LEDs) with a correlated color temperature (CCT) of 8500K has been conducted with the scattering enhancement particles (SEPs) to achieve the higher color uniformity and emitted luminous flux of pc-LEDs. Moreover, this paper also introduced about choosing scattering enhancement particles (SEPs), including calcium carbonate (CaCO3) and titania (TiO2), and compared these particles’ properties by adding them into the yellow Y3Al5O12:Ce3+ phosphor compounding. Afterward, the LightTools program was applied to illustrate the optical simulation, and obtained results was analyzed and verified by applying the Mie-scattering theory. The scattering coefficients, the anisotropic scattering, the reduced scattering, and the scattering amplitudes at 455 nm and 595 nm are included in the scattering computation of SEPs. According to researched results, among the SEPs, TiO2 particles result in the highest value of color uniformity. However, a rise in their concentration is the cause of a sharp decline in luminous flux. Meanwhile, CaCO3 particles show the ability of reducing the deviated level in correlated color temperature by 620K if there is employed 30% of CaCO3 concentration. Hence, CaCO3 particles are the recommendation for achieving higher chromatic homogeneity and lumen output.
Hidden Markov model technique for dynamic spectrum access
Jayant P Pawar;
Prashant V. Ingole
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan
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DOI: 10.12928/telkomnika.v18i5.14470
Dynamic spectrum access is a paradigm used to access the spectrum dynamically. A hidden Markov model (HMM) is one in which you observe a sequence of emissions, but do not know the sequence of states the model went through to generate the emissions. Analysis of hidden Markov models seeks to recover the sequence of states from the observed data. In this paper, we estimate the occupancy state of channels using hidden Markov process. Using Viterbi algorithm, we generate the most likely states and compare it with the channel states. We generated two HMMs, one slowly changing and another more dynamic and compare their performance. Using the Baum-Welch algorithm and maximum likelihood algorithm we calculated the estimated transition and emission matrix, and then we compare the estimated states prediction performance of both the methods using stationary distribution of average estimated transition matrix calculated by both the methods.