Bulletin of Electrical Engineering and Informatics

Journal Information
ISSN / EISSN : 2089-3191 / 2302-9285
Current Publisher: Institute of Advanced Engineering and Science (10.11591)
Former Publisher: Universitas Ahmad Dahlan (10.12928)
Total articles ≅ 980
Current Coverage
SCOPUS
Archived in
SHERPA/ROMEO
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Latest articles in this journal

Mustafa Ahmed Nayyef, Yasir Abdulhafedh Ahmed, Omar Kamil Dahham Alazzawi
Bulletin of Electrical Engineering and Informatics, Volume 10, pp 541-549; doi:10.11591/eei.v10i2.2708

The publisher has not yet granted permission to display this abstract.
Anh-Tu Le, Dinh-Thuan Do
Bulletin of Electrical Engineering and Informatics, Volume 10, pp 811-819; doi:10.11591/eei.v10i2.1936

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Mustafa Wassef Hasan, Nizar Hadi Abbas
Bulletin of Electrical Engineering and Informatics, Volume 10, pp 609-618; doi:10.11591/eei.v10i2.2288

The publisher has not yet granted permission to display this abstract.
Dyna Marisa Khairina, Rizka Khairunnisa, Heliza Rahmania Hatta, Septya Maharani
Bulletin of Electrical Engineering and Informatics, Volume 10, pp 978-987; doi:10.11591/eei.v10i2.2711

The publisher has not yet granted permission to display this abstract.
Anh-Tu Le, Dinh-Thuan Do
Bulletin of Electrical Engineering and Informatics, Volume 10, pp 793-800; doi:10.11591/eei.v10i2.1903

The publisher has not yet granted permission to display this abstract.
Nemir Ahmed Al-Azzawi
Bulletin of Electrical Engineering and Informatics, Volume 10, pp 724-731; doi:10.11591/eei.v10i2.2743

Abstract:
Microscopic images are becoming important and need to be studied to know the details and how-to quantitatively evaluate decellularization. Most of the existing research focuses on deep learning-based techniques that lack simplification for decellularization. A new computational method for the segmentation microscopy images based on the shearlet neural network (SNN) has been introduced. The proposal is to link the concept of shearlets transform and neural networks into a single unit. The method contains a feed-forward neural network and uses a single hidden layer. The activation functions are depending on the standard shearlet transform. The proposed SNN is a powerful technology for segmenting an electron microscopic image that is trained without relying on the pre-information of the data. The shearlet neural networks capture the features of full accuracy and contextual information, respectively. The expected value for specific inputs is estimated by learning the functional configuration of a network for the sequence of observed value. Experimental results on the segmentation of two-dimensional microscopy images are promising and confirm the benefits of the proposed approach. Lastly, we investigate on a challenging datasets ISBI 2012 that our method (SNN) achieves superior outcomes when compared to classical and deep learning-based methods.
Chi-Bao Le, Dinh-Thuan Do
Bulletin of Electrical Engineering and Informatics, Volume 10, pp 828-836; doi:10.11591/eei.v10i2.2116

Abstract:
This paper studies the secondary network relying relay selection to transmit signal from the secondary source (base station) to two destinations. Especially, two destinations are required non-orthogonal multiple access (NOMA) scheme and it benefits to implementation of the Internet of Things (IoT) systems. However, eavesdropper over-hears signal related link from selected relay to destination. This paper measure secure performance via metric, namely secure outage probability (SOP). In particular, signal to noise ratio (SNR) criterion is used to evalute SOP to provide reliable transmission to the terminal node. Main results indicates that the considered scheme provides performance gap among two signals at destination. The exactness of derived expressions is confirmed via numerical simulation.
Ruaa H. Ali Al-Mallah, Dheyaa Alhelal, Razan Abdulhammed
Bulletin of Electrical Engineering and Informatics, Volume 10, pp 732-741; doi:10.11591/eei.v10i2.2746

Abstract:
A smart student attendance system (SSAS) is presented in this paper. The system is divided into two phases: hardware and software. The Hardware phase is implemented based on Arduino's camera while the software phase is achieved by using image processing with face recognition depended on the cross-correlation technique. In comparison with traditional attendance systems, roll call, and sign-in sheet, the proposed system is faster and more reliable (because there is no action needed by a human being who by its nature makes mistakes). At the same time, it is cheaper when compared with other automatic attendance systems. The proposed system provides a faster, cheaper and reachable system for an automatic smart student attendance that monitors and generates attendance report automatically.
Ichsan Taufik, Mohamad Jaenudin, Fatimah Ulwiyatul Badriyah, Beki Subaeki, Opik Taupik Kurahman
Bulletin of Electrical Engineering and Informatics, Volume 10, pp 1008-1014; doi:10.11591/eei.v10i2.2629

Abstract:
Currently, the Vector Space Model algorithm has been widely implemented for the document search feature because of its reliability in retrieving information. One of them in the search for verses of the Qur'an based on the translation. However, if the phrase or word used is different (even though it has one meaning) with the word in the document in the database, the system will not display the verse. As we know that the Qur'an has a very deep meaning, so an interpretation of the verse is needed. Therefore, this research focuses on implementing the Vector Space Model (VSM) algorithm for searching verses and hadiths in science and technology by using the discussion parameters of these verses or hadiths. The test results obtained with 20 keyword samples using metric recall were 81% with an average time of 2.24 seconds.
Kristo Radion Purba, Yulia Yulia
Bulletin of Electrical Engineering and Informatics, Volume 10, pp 1046-1053; doi:10.11591/eei.v10i2.2656

Abstract:
In recent years, the emergence of social media influencers attracts the study of a realistic influence maximization (IM) technique. The theoretical performance of IM has become matured. However, it is not enough since IM has to be implemented in a social media environment. Realistic IM algorithms and diffusion models have been proposed, such as the addition of user factors or a learning agent. However, most studies still relied on the influence spread benchmark, which makes the usefulness questionable. This research is among the first IM study using Instagram data. In this study, two diffusion models are proposed, which are based on the original IC and LT models, with the addition of the engagement grade (EG) factor. An algorithm called IMFS (IM with followers score) is proposed to accommodate the new models as well as IC and LT. In addition, realistic benchmark methods are proposed, namely the average engagement of the activated users, and the overlapping between post likers and activated users. The result shows that the proposed models are 2-3x more realistic if compared to IC and LT.
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