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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 40 Documents
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Search results for , issue "Vol 19, No 2: April 2021" : 40 Documents clear
High-speed radix-10 multiplication using partial shifter adder tree-based convertor Utsav Kumar Malviya
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 2: April 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i2.14991

Abstract

A radix-10 multiplication is the foremost frequent operations employed by several monetary business and user-oriented applications, decimal multiplier using in state of art digital systems are significantly good but can be upgraded with time delay and area optimization. This work is proposed a more area and time delay optimized new design of overloaded decimal digit set (ODDS) architecture-based radix-10 multiplier for signed numbers. Binary coded decimal (BCD) to binary followed by binary multiplication and finally binary to BCD conversion are 3 major modules employed in radix-10 multiplication. This paperwork presents a replacement technique for binary coded decimal (BCD) to binary and vice-versa convertors in radix-10 multiplication. A novel addition tree structure called as partial shifter adder (PSA) tree-based approach has been developed for BCD to binary conversion, and it is used to add partially generated products. To meet our major concern i.e. speed, we need particular high-speed multiplication, hence the proposed PSA based radix-10 multiplier is using vertical cross binary multiplication and concurrent shifter-based addition method. The design has been tested on 45nm technology-based Zynq-7 field programmable gate array (FPGA) devices with a 6-input lookup table (LUTs). A combinational implementation maps quite well into the slice structure of the Xilinx Zynq-7 families field programmable gate array. The synthesis results for a Zynq-7 device indicate that our design outperforms in terms of the area and time delay.
Automatic human ear detection approach using modified adaptive search window technique Raad Ahmed Hadi; Loay Edwar George; Zainab Jawad Ahmed
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 2: April 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i2.18320

Abstract

The human ear biometric recognition plays an important role in the forensics specialty and has significant impact for biometrician scientists and researchers. Actually, many ear recognition researches showed promised results, but some issues such as manual detection process, efficiency and robustness aren’t attained a certain level of maturity. Therefore, the enhancement developing approaches still continuous to achieve limited successes. We propose an efficient, reliable and simple automatic human ear detection approach. This approach implement two stages: preprocessing and ear landmarks detection. We utilized the image contrast, Laplace filter and Gaussian blurring techniques to made enhancement on all images (increasing the contrast, reduce the noisy and smoothing processes). After that, we highlighted the ear edges by using the Sobel edge detector and determining the only white pixels of ear edges by applying the image substation method. The improvement focused on using the modified adaptive search window (ASW) to detect the ear region. Furthermore, our approach is tested on Indian Institute of Technology (IIT) Delhi standard ear biometric public dataset. Experimental results presented a well average detection rate 96% for 493 image samples from 125 persons and computational time almost ≈ 0.485 seconds which is evaluated with other previous works.
Effects of humidity on sand and dust storm attenuation predictions based on 14 GHz measurement Eltahir Idris Eltahir Mohamed; Elfatih A. A. Elsheikh; A. Awad Babiker; Islam Md. Rafiqul; Mohamad Hadi Habaebi; Aisha H. Abdulla; Elessaid Saad
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 2: April 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i2.18148

Abstract

Several models were proposed to predict the attenuation of microwave signals due to sand and dust storms. Those models were developed based on theoretical assumptions like Rayleigh approximation, Mie equations or numerical methods. This paper presents a comparison between attenuation predicted by three different theoretical models with measured attenuation at 14 GHz. Dielectric constant of dust particles is one of the important parameter in prediction models. This constant is estimated from measured dust samples and is utilized for predictions. All models are found largely underestimating the measurement. Humidity is also monitored and has been observed higher during dust storm. Hence dielectric constants are re-estimated with relative humidity conditions using available conversion model. The prediction has a great impact of humidity and predicted attenuations are found much higher in humid than dry dust condition. However, all models underestimate the measurement even considering 100% of relative humidity. Hence it is recommended to investigate the models by considering humidity and other environmental factors that change during dust storm.
Translating cuneiform symbols using artificial neural network Arwa Hamed Salih Hamdany; Raid Rafi Omar-Nima; Lubab H. Albak
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 2: April 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i2.16134

Abstract

Cuneiform language is an old language that was invented by the people of Sumerian nation. It is an essential language for many archeologists. Especially who are interested in studying and investigating the old nations of Iraq. Dealing with this type of language usually requires specialist to translate its symbols, which are basically forms of nail shapes. This study presents a new approach to translate the cuneiform writing by employing artificial neural network (ANN) technique. Effectively, multi-layer perceptron (MLP) neural network has been adapted for translating the Sumerian cuneiform symbol images to their corresponding English letters. This work has been successfully established and it attained 100%.
The importance of data classification using machine learning methods in microarray data Aws Naser Jaber; Kohbalan Moorthy; Logenthiran Machap; Safaai Deris
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 2: April 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i2.15948

Abstract

The detection of genetic mutations has attracted global attention. several methods have proposed to detect diseases such as cancers and tumours. One of them is microarrays, which is a type of representation for gene expression that is helpful in diagnosis. To unleash the full potential of microarrays, machine-learning algorithms and gene selection methods can be implemented to facilitate processing on microarrays and to overcome other potential challenges. One of these challenges involves high dimensional data that are redundant, irrelevant, and noisy. To alleviate this problem, this representation should be simplified. For example, the feature selection process can be implemented by reducing the number of features adopted in clustering and classification. A subset of genes can be selected from a pool of gene expression data recorded on DNA micro-arrays. This paper reviews existing classification techniques and gene selection methods. The effectiveness of emerging techniques, such as the swarm intelligence technique in feature selection and classification in microarrays, are reported as well. These emerging techniques can be used in detecting cancer. The swarm intelligence technique can be combined with other statistical methods for attaining better results.
An ensemble based approach for effective intrusion detection using majority voting Alwi M. Bamhdi; Iram Abrar; Faheem Masoodi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 2: April 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i2.18325

Abstract

Of late, Network Security Research is taking center stage given the vulnerability of computing ecosystem with networking systems increasingly falling to hackers. On the network security canvas, Intrusion detection system (IDS) is an essential tool used for timely detection of cyber-attacks. A designated set of reliable safety has been put in place to check any severe damage to the network and the user base. Machine learning (ML) is being frequently used to detect intrusion owing to their understanding of intrusion detection systems in minimizing security threats. However, several single classifiers have their limitation and pose challenges to the development of effective IDS. In this backdrop, an ensemble approach has been proposed in current work to tackle the issues of single classifiers and accordingly, a highly scalable and constructive majority voting-based ensemble model was proposed which can be employed in real-time for successfully scrutinizing the network traffic to proactively warn about the possibility of attacks. By taking into consideration the properties of existing machine learning algorithms, an effective model was developed and accordingly, an accuracy of 99%, 97.2%, 97.2%, and 93.2% were obtained for DoS, Probe, R2L, and U2R attacks and thus, the proposed model is effective for identifying intrusion.
Frequent pattern growth algorithm for maximizing display items Asyahri Hadi Nasyuha; Jalius Jama; Rijal Abdullah; Yohanni Syahra; Zulfi Azhar; Juniar Hutagalung; Buyung Solihin Hasugian
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 2: April 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i2.16192

Abstract

Products are goods that are available and provided in stores for sale. Products provided in stores must be arranged properly to order to attract the attention of consumers to buy. Products arranged in a store will depend on the type of store. The product arrangement at a retail store will be different from the product arrangement at a clothing store. Store display will reflect a picture that is in the store so consumers know the types of products sold by product arrangement. An attractive arrangement will stimulate the desire of consumers to buy. In data mining there are several types of methods by use including prediction, association, classification and estimation. In the prediction method there are several techniques including the frequent pattern growth (FP-growth) method. FP-growth algorithm is the development of the apriori algorithm. So, the shortcomings of the apriori algorithm are corrected by the FP-growth algorithm. FP-growth is one alternative algorithm that can be used to determine the set of data that most often appears (frequent itemset) in a data set. Results of research on the application of the FP-growth algorithm to maximizing the display of goods. It is hoped that this research can be used to adjust the product layout according to the level of frequency the product is sought by the customer so that the customer has no difficulty finding the product they want.
Earprint recognition using deep learning technique Arwa H. Salih Hamdany; Aseel Thamar Ebrahem; Ahmed M. Alkababji
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 2: April 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i2.16572

Abstract

Earprint has interestingly been considered for recognition systems. It refers to the shape of ear, where each person has a unique shape of earprint. It is a strong biometric pattern and it can effectively be used for authentications. In this paper, an efficient deep learning (DL) model for earprint recognition is designed. This model is named the deep earprint learning (DEL). It is a deep network that carefully designed for segmented and normalized ear patterns. IIT Delhi ear database (IITDED) version 1.0 has been exploited in this study. The best obtaining accuracy of 94% is recorded for the proposed DEL.
Utilizing CaCO3, CaF2, SiO2, and TiO2 phosphors as approaches to the improved color uniformity and lumen efficacy of WLEDs Huu Phuc Dang; Phung Ton That; Nguyen Doan Quoc Anh
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 2: April 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i2.16357

Abstract

The two elements that are most favorable in the quality evaluation for phosphor-converted LEDs (pcLEDs) these days are the chromatic homogeneity and the lumen output. In this study, a thorough research on enhancing color uniformity and luminous flux of pcLEDs that have a high correlated color temperature (CCT) of 8500K is carried out. The scattering enhancement particles (SEPs): CaCO3, CaF2, SiO2, and TiO2 are used to accomplish the goal by adding them to a yellow phosphor compounding Y3Al5O12:Ce3+, and comparing their characteristics afterwards. LightTools program is used to build an optical simulation and Mie-scattering theory helps to examine the achieved results. Specifically, the parameters included in SEPs’ scattering calculation are the scattering coefficients, the anisotropic scattering, the reduced scattering, and the scattering amplitudes at 455 nm and 595 nm. The outcomes presented that compared to other SEPs, TiO2 particles can yield the highest chromatic homogeneity. However, the lumen output reduces considerably as TiO2 concentration greatly increases while it can be bettered when using SiO2 particles with any particle size. For CaCO3 particles, the color deviation of 620 K CCT can be reduced with 30% concentration, leading to the recommendation of using CaCO3 to promote the CCT homogeneity and luminescence efficiency.
Machine learning with multistage classifiers for identification of of ectoparasite infected mud crab genus Scylla Rozniza Ali; Muhamad Munawarar Yusro; Muhammad Suzuri Hitam; Mhd Ikhwanuddin Abdullah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 2: April 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i2.16724

Abstract

Recently, the mud-crab farming can help the rural population economically. However, the existing parasite in the mud-crabs could interfere the long live of the mud-crabs. Unfortunately, the parasite has been identified to live in hundreds of mud-crabs, particularly it happened in Terengganu Coastal Water, Malaysia. This study investigates the initial identification of the parasite features based on their classes by using machine learning techniques. In this case, we employed five classifiers i.e logistic regression (LR), k-nearest neighbors (kNN), Gaussian Naive Bayes (GNB), support vector machine (SVM), and linear discriminant analysis (LDA). We compared these five classfiers to best performance of classification of the parasites. The classification process involving three stages. First, classify the parasites into two classes (normal and abnormal) regardless of their ventral types. Second, classified sexuality (female or male) and maturity (mature or immature). Finally, we compared the five classifiers to identify the species of the parasite. The experimental results showed that GNB and LDA are the most effective classifiers for carrying out the initial classification of the rhizocephalan parasite within the mud crab genus Scylla.

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