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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 67 Documents
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Search results for , issue "Vol 18, No 4: August 2020" : 67 Documents clear
Semi-supervised auto-encoder for facial attributes recognition Soumaya Zaghbani; Nouredine Boujneh; Med Salim Bouhlel
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i4.14836

Abstract

The particularity of our faces encourages many researchers to exploit their features in different domains such as user identification, behaviour analysis, computer technology, security, and psychology. In this paper, we present a method for facial attributes analysis. The work addressed to analyse facial images and extract features in the purpose to recognize demographic attributes: age, gender, and ethnicity (AGE). In this work, we exploited the robustness of deep learning (DL) using an updating version of autoencoders called the deep sparse autoencoder (DSAE). In this work we used a new architecture of DSAE by adding the supervision to the classic model and we control the overfitting problem by regularizing the model. The pass from DSAE to the semi-supervised autoencoder (DSSAE) facilitates the supervision process and achieves an excellent performance to extract features. In this work we focused to estimate AGE jointly. The experiment results show that DSSAE is created to recognize facial features with high precision. The whole system achieves good performance and important rates in AGE using the MORPH II database
Outage probability analysis in DF power-splitting full-duplex relaying network with impact of Co-channel interference at the destination Phu Tran Tin; Duy Hung Ha; Minh Tran
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i4.13716

Abstract

Nowadays, improving the WPCN efficiency problem is the leading research direction in the communication network. In this research, the outage probability (OP) analysis in DF power-splitting (PS) full-duplex (FD) relaying network with impact of co-channel interference at the destination is proposed and investigated. In the system model section, we present the DF PS FD Relaying Network with Impact of Co-channel interference at the destination. Then in the system performance section, we analyze and derive the closed-form expression of the OP and investigate the effect of the main system parameters on the system network performance. Then, we perform the Monte Carlo simulation to verify the analytical section. This research can provide a new recommendation for the communication network.
Machine learning based lightweight interference mitigation scheme for wireless sensor network Ali Suzain; Rozeha A. Rashid; M. A. Sarijari; A. Shahidan Abdullah; Omar A. Aziz
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i4.14879

Abstract

The interference issue is most vibrant on low-powered networks like wireless sensor network (WSN). In some cases, the heavy interference on WSN from different technologies and devices result in life threatening situations. In this paper, a machine learning (ML) based lightweight interference mitigation scheme for WSN is proposed. The scheme detects and identifies heterogeneous interference like Wifi, bluetooth and microwave oven using a lightweight feature extraction method and ML lightweight decision tree. It also provides WSN an adaptive interference mitigation solution by helping to choose packet scheduling, Acknowledgement (ACK)-retransmission or channel switching as the best countermeasure. The scheme is simulated with test data to evaluate the accuracy performance and the memory consumption. Evaluation of the proposed scheme’s memory profile shows a 14% memory saving compared to a fast fourier transform (FFT) based periodicity estimation technique and 3% less memory compared to logistic regression-based ML model, hence proving the scheme is lightweight. The validation test shows the scheme has a high accuracy at 95.24%. It shows a precision of 100% in detecting WiFi and microwave oven interference while a 90% precision in detecting bluetooth interference.
Single document keywords extraction in Bahasa Indonesia using phrase chunking I Nyoman Prayana Trisna; Arif Nurwidyantoro
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i4.14389

Abstract

Keywords help readers to understand the idea of a document quickly. Unfortunately, considerable time and effort are often needed to come up with a good set of keywords manually. This research focused on generating keywords from a document automatically using phrase chunking. Firstly, we collected part of speech patterns from a collection of documents. Secondly, we used those patterns to extract candidate keywords from the abstract and the content of a document. Finally, keywords are selected from the candidates based on the number of words in the keyword phrases and some scenarios involving candidate reduction and sorting. We evaluated the result of each scenario using precision, recall, and F-measure. The experiment results show: i) shorter-phrase keywords with string reduction extracted from the abstract and sorted by frequency provides the highest score, ii) in every proposed scenario, extracting keywords using the abstract always presents a better result, iii) using shorter-phrase patterns in keywords extraction gives better score in comparison to using all phrase patterns, iv) sorting scenarios based on the multiplication of candidate frequencies and the weight of the phrase patterns offer better results.
Deep learning in sport video analysis: a review Keerthana Rangasamy; Muhammad Amir As’ari; Nur Azmina Rahmad; Nurul Fathiah Ghazali; Saharudin Ismail
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i4.14730

Abstract

Sport is a competitive field, where it is an element of measurement for a countries development.  Due to this reason, sport analysis has become one of the major contribution in analysing and improving the performance level of an athlete.  Video-based modality has become a crucial tool used in sport analysis by coaches and performance analysis.  There were wide variety of techniques used in sport video analysis.  The main purpose of this review paper is to compare and update review between traditional handcrafted approach and deep learning approach in sport video analysis based on human activity recognition, overview of recent study in video based human activity recognition in sport analysis and finally concluded with future potential direction in sport video analysis.
Enhancement of student performance prediction using modified K-nearest neighbor Saja Taha Ahmed; Rafah Al-Hamdani; Muayad Sadik Croock
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i4.13849

Abstract

The traditional K-nearest neighbor (KNN) algorithm uses an exhaustive search for a complete training set to predict a single test sample. This procedure can slow down the system to consume more time for huge datasets. The selection of classes for a new sample depends on a simple majority voting system that does not reflect the various significance of different samples (i.e. ignoring the similarities among samples). It also leads to a misclassification problem due to the occurrence of a double majority class. In reference to the above-mentioned issues, this work adopts a combination of moment descriptor and KNN to optimize the sample selection. This is done based on the fact that classifying the training samples before the searching actually takes place can speed up and improve the predictive performance of the nearest neighbor. The proposed method can be called as fast KNN (FKNN). The experimental results show that the proposed FKNN method decreases original KNN consuming time within a range of (75.4%) to (90.25%), and improve the classification accuracy percentage in the range from (20%) to (36.3%) utilizing three types of student datasets to predict whether the student can pass or fail the exam automatically.
Strategy to determine the foot plantar center of pressure of a person through deep learning neural networks Henry Hernández Martínez; Holman Montiel Ariza; Luz Andrea Gaviria Roa
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i4.15200

Abstract

Some case studies treated by physiotherapists or orthopedists to measure the alignment of the lower extremities during a gait cycle are based on empirical methods of visual observation. This methodology does not guarantee total success, since it depends on the experience of the specialist, what can cause irreversible damage to patients, such as: hip displacement, wear and overload of the joints of a single lower limb. Although, this problem has been addressed in the investigation by means of devices implementation with sensors or methods of processing sequences of images and videos, this topic is still under investigation because the current methods depend on many external elements and data given by an expert in the area. Therefore, this paper proposes a partial solution to this problem by systematizing the experience of a specialist through a computational learning method.

Page 7 of 7 | Total Record : 67

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