cover
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
Bulletin of Electrical Engineering and Informatics
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 63 Documents
Search results for , issue "Vol 10, No 5: October 2021" : 63 Documents clear
Jawi using multi-maker augmented reality Siti Hasnah Tanalol; Dinna N. Mohd Nizam; Zaidatol Haslinda Abdullah Sani; Aslina Baharum; Asni Tahir; Iznora Aini Zolkifly
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.2464

Abstract

This paper discussed the development of multi-maker augmented reality for learning Jawi in order to complement the formal study in school. We conducted an experiment with N=10 participants from Pusat Minda Lestari, UMS age 5 and 6 years old, to study the effectiveness of learning Jawi using the developed mobile augmented reality application. We prepared a test environment comprising an electroencephalography (EEG) system and mobile augmented reality (AR) application for analysis and testing. Results found that the learn ability of the students was improved after they used the mobile application to learn basic Jawi. The methodology used was ADDIE model, which included the analysis, design, development, implementation, and evaluate phases. This project is an innovation in learning Jawi and hopefully can increase the children’s interest in learning Jawi.
Switchable bandstop to allpass filter using cascaded transmission line SIW resonators in K-band Amirul Aizat Zolkefli; Noor Azwan Shairi; Badrul Hisham Ahmad; Adib Othman; Nurulhalim Hassim; Zahriladha Zakaria; Imran Mohd Ibrahim; Huda A. Majid
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.2835

Abstract

In this paper, a switchable bandstop to allpass filter using cascaded transmission line SIW resonators is proposed. The switchable filter is performed by the switchable cascaded transmission line SIW resonators using discrete PIN diodes. Therefore, it can be used for rejecting any unwanted frequencies in the communication systems. The proposed filter design is operated in K-band and targeted for millimeter wave front end system for 5G telecommunication. Two filter designs with different orientation (design A and B) are investigated for the best performance and compact size. As a result, design B is the best by giving a maximum attenuation of 39.5 dB at 26.4 GHz with the layout size of 33×30 mm.
Constructed model for micro-content recognition in lip reading based deep learning Nada Hussain Ali; Matheel Emad Abdulmunem; Akbas Ezaldeen Ali
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.2927

Abstract

Communication between human beings has several ways, one of the most known and used is speech, both visual and acoustic perceptions sensory are involved, because of that, the speech is considered as a multi-sensory process. Micro contents are a small pieces of information that can be used to boost the learning process. Deep learning is an approach that dives into deep texture layers to learn fine grained details. The convolution neural network (CNN) is a deep learning technique that can be employed as a complementary model with micro learning to hold micro contents to achieve special process. In This paper a proposed model for lip reading system is presented with proposed video dataset. The proposed model receives micro contents (the English alphabet) in video as input and recognize them, the role of CNN deep learning is clearly appeared to perform two tasks, the first one is feature extraction and the second one is the recognition process. The implementation results show an efficient accuracy recognition rate for various video dataset that contains variety lip reader for many persons with age range from 11 to 63 years old, the proposed model gives high recognition rate reach to 98%.
Business process re-enginering of tourism e-marketplace by engaging government, small medium enterprises and tourists Kadek Cahya Dewi; Ni Wayan Dewinta Ayuni
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3159

Abstract

Not all tourism actors in Indonesia had utilize the e-marketplace. Therefore, one of the Indonesian government's focus is to improve the tourism business process model through e-marketplace based system. The research purpose was to re-engineer the business process of tourism e-marketplace by engaging government, small medium enterprises (SMEs) and tourists. The research used the mixed method approach that conducted by modifying The McKinsey BPR methodology. As the result, this research adding two novel aspects to the previous research which are "role" and "activities". The new tourism e-marketplace business model proposed three kinds of role, namely: (1) government, (2) SMEs, and (3) tourists. This model also introduced activities including catalogue, finance, inventory management, collaboration, order fulfilment and customization. The proposed model was implemented and can be found in http://gonusadua.com. TELOS feasibility study was conducted to evaluate the model and found the final score of 8.3. It can be concluded that this model was feasible to develop and provide benefits for the government, SMEs, as well as the tourist. Beside had a contribution in built a new model of tourism e-marketplace, the research had also constructed a new tourism e-marketplace system with some improvements on the business model.
Orchid types classification using supervised learning algorithm based on feature and color extraction Pulung Nurtantio Andono; Eko Hari Rachmawanto; Nanna Suryana Herman; Kunio Kondo
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3118

Abstract

Orchid flower as ornamental plants with a variety of types where one type of orchid has various characteristics in the form of different shapes and colors. Here, we chosen support vector machine (SVM), Naïve Bayes, and k-nearest neighbor algorithm which generates text input. This system aims to assist the community in recognizing orchid plants based on their type. We used more than 2250 and 1500 images for training and testing respectively which consists of 15 types. Testing result shown impact analysis of comparison of three supervised algorithm using extraction or not and several variety distance. Here, we used SVM in Linear, Polynomial, and Gaussian kernel while k-nearest neighbor operated in distance starting from K1 until K11. Based on experimental results provide Linear kernel as best classifier and extraction process had been increase accuracy. Compared with Naïve Bayes in 66%, and a highest KNN in K=1 and d=1 is 98%, SVM had a better accuracy. SVM-GLCM-HSV better than SVM-HSV only that achieved 98.13% and 93.06% respectively both in Linear kernel. On the other side, a combination of SVM-KNN yield highest accuracy better than selected algorithm here.
Naive Bayes modification for intrusion detection system classification with zero probability Yogiek Indra Kurniawan; Fakhrur Razi; Nofiyati Nofiyati; Bangun Wijayanto; Muhammad Luthfi Hidayat
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.2833

Abstract

One of the methods used in detecting the intrusion detection system is by implementing Naïve Bayes algorithm. However, Naïve Bayes has a problem when one of the probabilities is 0, it will cause inaccurate prediction, or even no prediction was found. This paper proposed two modifications for Naïve Bayes algorithm. The first modification eliminated the variable that has 0 probability and the second modification changed the multiplication operations to addition operations. This modification is only applied when the Naïve Bayes algorithm does not find any prediction results caused by zero probabilities. The results of this research show that the value of precision, recall, and accuracy in the modification made tends to increase and better than the original Naïve Bayes algorithm. The highest precision, recall, and accuracy are obtained from modification by changing the multiplication operation to the addition. Increasing precision can reach 4%, increasing recall reaches 2% and increasing accuracy reaches 2%.
Telehealth care enhancement using the internet of things technology Farouk Boumehrez; A. Hakim Sahour; Noureddine Doghmane
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.2968

Abstract

Chronic diseases quickly become broader public health issues because of the difficulty in obtaining appropriate, often long-term health care. So that, it requires the extension of health care for patients with chronic diseases beyond the clinic to include patient’s home and work environment. To reduce costs and provide more appropriate healthcare, we need telehealth care where internet of things (IoT) technology plays an important role. The integration of the IoT and medical science offers opportunities to improve healthcare quality, and efficiency and to better coordinate healthcare delivery at home and in the workplace. In this paper, we present the realization of a remote healthcare system based on the IoT technology. The function of this system is the transmission via a gateway of internet collected data using biomedical sensors node based Arduino board (e.g., temperature, electrical activity of the heart, heart rate monitor). These data will be stored automatically in a cloud. The health can then be monitored by the doctor or patient using a web page in real-time from anywhere at any time in the world using laptops or smart phones, etc. This method also reduces the need for direct interaction between doctor and patient.
Cloud middleware and services-a systematic mapping review Isaac Odun-Ayo; Marion Adebiyi; Olatunji Okesola; Olufunke Vincent
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3169

Abstract

Cloud computing currently plays a crucial role in the delivery of vital information technology services. A unique aspect of cloud computing is the cloud middleware and other related entities that support applications and networks. A specific field of research may be considered, particularly as regards cloud middleware and services at all levels, and thus needs analysis and paper surveys to elucidate possible study limitations. The purpose of this paper is to perform a systematic mapping for studies that capture cloud computing middleware, stacks, tools and services. The methodology adopted for this study is a systematic mapping review. The results showed that more papers on the contribution facet were published with tool, model, method and process having 18.10%, 13.79%, 6.03% and 8.62% respectively. In addition, in terms of tool, evaluation and solution research had the largest number of articles with 14.17% and 26.77% respectively. A striking feature of the systemic map is the high number of articles in solution research with respect to all aspects of the features applied in the studies. This study showed clearly that there are gaps in cloud computing middleware and delivery services that would interest researchers and industry professionals desirous of research in this area.
Automatic voltage regulator performance enhancement using a fractional order model predictive controller Imen Deghboudj; Samir Ladaci
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.2435

Abstract

In this paper, a new design method for fractional order model predictive control (FO-MPC) is introduced. The proposed FO-MPC is synthesized for the class of linear time invariant system and applied for the control of an automatic voltage regulator (AVR). The main contribution is to use a fractional order system as prediction model, whereas the plant model is considered as an integer order one. The fractional order model is implemented using the singularity function approach. A comparative study is given with the classical MPC scheme. Numerical simulation results on the controlled AVR performances show the efficiency and the superiority of the fractional order MPC.
Classifying lymphoma and tuberculosis case reports using machine learning algorithms Moanda Diana Pholo; Yskandar Hamam; Abdel Baset Khalaf; Chunling Du
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3132

Abstract

Available literature reports several lymphoma cases misdiagnosed as tuberculosis, especially in countries with a heavy TB burden. This frequent misdiagnosis is due to the fact that the two diseases can present with similar symptoms. The present study therefore aims to analyse and explore TB as well as lymphoma case reports using Natural Language Processing tools and evaluate the use of machine learning to differentiate between the two diseases. As a starting point in the study, case reports were collected for each disease using web scraping. Natural language processing tools and text clustering were then used to explore the created dataset. Finally, six machine learning algorithms were trained and tested on the collected data, which contained 765 lymphoma and 546 tuberculosis case reports. Each method was evaluated using various performance metrics. The results indicated that the multi-layer perceptron model achieved the best accuracy (93.1%), recall (91.9%) and precision score (93.7%), thus outperforming other algorithms in terms of correctly classifying the different case reports.