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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics 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 2,901 Documents
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Application of content based image retrieval in digital image search system Syamsul Yakin; Tasrif Hasanuddin; Nia Kurniati
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2713

Abstract

Multimedia data is growing rapidly in the current digital era, one of which is digital image data. The increasing need for a large number of digital image datasets makes the constraints faced eventually drain a lot of time and cause the process of image description to be inconsistent. Therefore, a method is needed in processing the data, especially in searching digital image data in large image dataset to find image data that are relevant to the query image. One of the proposed methods for searching information based on image content is content based image retrieval (CBIR). The main advantage of the CBIR method is automatic retrieval process, compared to traditional keyword. This research was conducted on a combination of the HSV color histogram methods and the discrete wavelet transform to extract color features and textures features, while the chi-square distance technique was used to compare the test images with images into a database. The results have showed that the digital image search system with color and texture features have a precision value of 37.5% - 100%, with an average precision value of 80.71%, while the percentage accuracy is 93.7% - 100% with an average accuracy is 98.03%.
A new model for early diagnosis of alzheimer's disease based on BAT-SVM classifier Shereen A. Taie; Wafaa Ghonaim
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2714

Abstract

Magnetic Resonance Images (MRI) of the Brain is a significant tool to diagnosis Alzheimer's disease due to its ability to measure regional changes in the brain that reflect disease progression to detect early stages of the disease. In this paper, we propose a new model that adopts Bat for parameter optimization problem of Support vector machine (SVM) to diagnose Alzheimer’s disease via MRI biomedical image. The proposed model uses MRI for biomedical image classification to diagnose three classes; normal controls (NC), mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The proposed model based on segmentation for the most involved areas in the disease hippocampus, the features of MRI brain images are extracted to build feature vector of the brain, then extracting the most significant features in neuroimaging to reduce the high dimensional space of MRI images to lower dimensional subspace, and submitted to machine learning classification technique. Moreover, the model is applied on different datasets to validate the efficiency which show that the new Bat-SVM model can yield promising acceptable level of accuracy reached to 95.36 % using maximum number of bats equal to 50 and number of generation equal to 10.
Diabetics blood glucose control based on GA-FOPID technique Wesam M. Jasim; Yousif I. Al Mashhadany
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2715

Abstract

In this paper, an optimized Fractional Order Proportional, Integral, Derivative based Genetic Algorithm GA-FOPID optimization technique is proposed for glucose level normalization of diabetic patients. The insulin pump with diabetic patient system used in the simulation is the Bergman minimal model, which is used to simulate the overall system. The main purpose is to obtain the optimal controller parameters that regulate the system smoothly to the desired level using GA optimization to find the FOPID parameters. The next step is to obtain the FOPID controller parameters and the traditional PID controller parameters normally. Then, the simulation output results of using the proposed GA-FOPID controller was compared with that of using the normal FOPID and the traditional PID controllers. The comparison shows that all the three controllers can regulate the glucose level but the use of GA-FOPID controller was outperform the use of the other two controllers in terms of speed of normalization and the overshoot value.
Development of an IoT-based and cloud-based disease prediction and diagnosis system for healthcare using machine learning algorithms Abdali-Mohammadi, Fardin; Meqdad, Maytham N.; Kadry, Seifedine
Bulletin of Electrical Engineering and Informatics List of Accepted Papers (with minor revisions)
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2716

Abstract

Internet of Things (IoT) refers to the practice of designing and modelingobjects connected to the Internet through computer networks. In the past fewyears, IoT-based health care programs have provided multidimensionalfeatures and services in real time. These programs provide hospitalization formillions of people to receive regular health updates for a healthier life.Induction of IoT devices in the healthcare environment have revitalizedmultiple features of these applications. In this paper, a disease diagnosissystem is designed based on the Internet of Things. In this system, first, thepatient's courtesy signals are recorded by wearable sensors. These signals arethen transmitted to a server in the network environment. This article alsopresents a new Hybrid Decision Making approach for diagnosis. In thismethod, a feature set of patient signals is initially created. Then thesefeatures go unnoticed on the basis of a learning model. A diagnosis is thenperformed using a neural fuzzy model. In order to evaluate this system, aspecific diagnosis of a specific disease, such as a diagnosis of a patient'snormal and unnatural pulse, or the diagnosis of diabetic problems, will besimulated.
Enhancement of the Estimation of Energy Consumption for Electric Vehicles by Using Machine Learning Cabani, Adnane; Zhang, Peiwen; Khemmar, Redouane; Xu, Jin
Bulletin of Electrical Engineering and Informatics List of Accepted Papers (with minor revisions)
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2717

Abstract

Three main classes are considered of significant influence factors when predicting theenergy consumption rate of Electric Vehicles (EV): environment, driver behaviour, and vehicle. These classes take into account constant or variable parameters which influ-ences the energy consumption of the EV. In this paper, we develop a new model taking into account the three classes as well as the interaction between them in order to im-prove the quality of EV energy consumption. The model depends on a new approach based on machine learning and especially k-NN algorithm in order to estimate the EVenergy consumption. Following a lazy learning paradigm, this approach allows better estimation performance. The advantage of our proposal, in regards to mathematical approach, is taking into account the real situation of the ecosystem on the basis of historical data. In fact, the behavior of the driver (driving style, heating usage, air con-ditioner usage, battery state, etc.) impacts directly the EV energy consumption. The obtained results show that we can reach up to 96.5% of accuracy about the estimatedof energy-consumption. The proposed method is used in order to find the optimal pathbetween two points (departure-destination) in terms of energy consumption.
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%.

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