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Bulletin of Electrical Engineering and Informatics

EEI
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Published by IAES Indonesia Section and Partners

ISSN : - EISSN : - DOI : -

Core Subject : Engineering,

Electrical & Electronics Engineering

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Articles 627 Documents

59 60 61 62 63

Development of augmented reality to learn history Nur Hazirah Mohd Azhar; Norizan Mat Diah; Suzana Ahmad; Marina Ismail
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Augmented Reality (AR) is a technology that enables a new information delivery environment. AR promotes both engagement and motivation for people to obtain and acquire certain knowledge or information including those concerning history. People, especially the young generation, often view history as an uninteresting and boring subject matter. This may be due to the lack of interactivity and visual images that accompanying the information on history. This could affect our level of understanding about the history of our country such as the fall of Melaka Empire and weaken our spirit of patriotism. Thus, this research aims to study the effect of combining the AR technology together with the traditional information to create excitement in learning history. The development of the AR application in this project is to enhance the traditional book by allowing users to see the digital visual of historical events. The development of the application involves five phases that are analysis, design, develop, implement, and evaluate. The mobile application of AR book on the fall of Melaka Empire history has been developed successfully and the findings show that most users agree that the application contributes to higher users’ satisfaction.

Optimizing community detection in social networks using antlion and K-median Amany A. Naem; Neveen I. Ghali
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Antlion Optimization (ALO) is one of the latest population based optimization methods that proved its good performance in a variety of applications. The ALO algorithm copies the hunting mechanism of antlions to ants in nature. Community detection in social networks is conclusive to understanding the concepts of the networks. Identifying network communities can be viewed as a problem of clustering a set of nodes into communities. k-median clustering is one of the popular techniques that has been applied in clustering. The problem of clustering network can be formalized as an optimization problem where a qualitatively objective function that captures the intuition of a cluster as a set of nodes with better in ternal connectivity than external connectivity is selected to be optimized. In this paper, a mixture antlion optimization and k-median for solving the community detection problem is proposed and named as K-median Modularity ALO. Experimental results which are applied on real life networks show the ability of the mixture antlion optimization and k-median to detect successfully an optimized community structure based on putting the modularity as an objective function.

A systematic approach to evaluating the influence of demand side management resources on the interarea capacity benefit margin Olatunji Obalowu Mohammed; Mohd Wazir Mustafa; Daw Saleh Sasi Mohammed; Sani Salisu; Nabila Ahmad Rufa’i
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Available transfer capability is an index to measure the security and economic viability of an interconnected system. However, to accurately determine this index, other associated parameters need to be accurately evaluated. One of these parameters is the capacity benefit margin (CBM). For efficient power generation reliability and sustainability, a certain amount of supply capacity is commonly reserved by utilities, which in most cases remain unused, to reduce the effect of generation outage. To minimize this unused reserve, utilities usually reserve a predetermined amount of tie-line capacity between interconnected areas to have access to external supply. This tie-line reserved for this purpose is termed as capacity benefit margin (CBM). In this paper a technique for computing CBM is used, the sensitivity of CBM support from other areas to the increase in load in one of the areas is investigated, and conclusively, demand side management is proposed to improve the quantification of CBM. The contribution of this work is the assessment of the CBMs support from other areas during a critical condition, using the flexibility of DSM technique. The modified 24-bus IEEE reliability test system is employed for the verification of the approach.

The generation revenue and demand payment assessment for pool based market model in Malaysia electricity supply industry Zuraidah Ngadiron; N. H. Radzi; M. Y. Hassan
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

The objective of this paper is to address the economic benefits in term of generation revenue and demand payment for the pool based market model in Malaysia electricity supply industry (MESI). In pool market model, there are issues on the benefit of the generators such as too high system marginal price (SMP) during peak demand and no revenue during low demand. Therefore, conceptual study for two bus test system in MESI involving four generators around Peninsular Malaysia is conducted to perform the economic analysis in term of generation revenue and demand assessment considering existing single buyer model and pool based market model, i.e., pool model, spot market model and the proposed model, in order to identify which market model is superior. As a result, the proposed model managed to decrease the demand payment as it is proportional to generation revenue, even though the generation revenue is at intermediate value and succeed to increase the low and medium generator’s revenue.

Developing a secured image file management system using modified AES Heidilyn V. Gamido; Marlon V. Gamido; Ariel M. Sison
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Images are a means to share and convey relevant data in today’s digital world. This paper presents an image file management system to provide a platform for distributing and viewing images in a secured manner. The shared image files are stored in the server in an encrypted manner to provide additional security to the owner of the file. A modified AES algorithm using bit permutation was used to encrypt the image files. Based on the experimental result, image files were successfully encrypted in the server and can only be decrypted by the intended recipient of the file providing an efficient and reliable way of exchanging images.

Modelling and analysis of a PV/wind/diesel hybrid standalone microgrid for rural electrification in Nigeria Ibim Sofimieari; Mohd Wazir Bin Mustafa; Felix Obite
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

The scarce electricity supply in Nigeria is a key factor to the low industrial development in a country well-known for having the least electrification in Africa per capita. Presently, Nigeria employs four different kinds of energy such as coal, natural gas, hydro, and oil. Three of the four resources mentioned above used for the production of energy in Nigeria is connected with increasing emissions of greenhouse gas: natural gas, oil, and coal, with coal releasing the worst. This paper presents a model and analysis of PV/Wind/Diesel hybrid system for rural electrification in Kaduna state, northern Nigeria. HOMER (Hybrid Optimization Model for Electric Renewable) software tool was used for optimization and modeling of this work. Simulation results show that the PV/Wind/Diesel system with Battery storage is the most cost-effective system since it recorded considerable cost of energy and reduces CO2 emissions significantly.

Comparison of lightning return stroke channel-base current models with measured lightning current Chin-Leong Wooi; Zulkurnain Abul-Malek; Mohamad Nur Khairul Hafizi Rohani; Ahmad Muhyiddin Bin Yusof; Syahrun Nizam Md Arshad; Ali I Elgayar
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Electromagnetic pulse radiation produced around the lightning stroke channel has caused the disturbance to the microelectronic industry, especially to disturbance of high frequency to electronic systems. Lightning channel-base current function (CBC) characteristics and parameters determine lightning electromagnetic field (LEMF) results obtained on the basis of the used models. This paper evaluated and compared the measured lightning current and six lightning current-based channels models namely Bruce and Golde, Heidler, Diendorfer and Uman, Nucci, Pierce and Cianos and new current-based current (NCBC) models. In terms of the waveshape, among all the six lightning channel-based current models discussed, the models developed by Javor, Nucci and Diendorfer and Uman have showed a good agreement compared to the measured lightning current. In terms of 10-90% risetime and full width half maximum time (FWHM) comparison, NCBC and Nucci models have showed compatible comparison. However, Nucci model is not easily adjustable to different desired pulse-current waveshapes. On the other hand, NCBC model can be simplified, the values of lightning peak current and risetime can be chosen arbitrarily and independently from other parameters, and there is no need for the peak-correction factor, so that reduces the number of parameters. Therefore, the NCBC model was suggested to be used in the future in order to simulate much accurate return stroke model. This knowledge will contribute to the development of a new accurate and efficient return stroke model.

A mapping study on blood glucose recommender system for patients with gestational diabetes mellitus Shuhada Mohd Rosli; Marshima Mohd Rosli; Rosmawati Nordin
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Blood glucose (BG) prediction system can help gestational diabetes mellitus (GDM) patient to improve the BG control with managing their dietary intake based on healthy food. Many techniques have been developed to deal with blood glucose prediction, especially those for recommender system. In this study, we conduct a systematic mapping study to investigate recent research about BG prediction in recommender systems. This study describes an overview of research (2014-2018) about BG prediction techniques that has been used for BG recommender system. As results, 25 studies concerning BG prediction in recommender system were selected. We observed that although there is numerous studies published, only a few studies took serious discussion about techniques used to incorporate the BG algorithms. Our result highlighted that only one study discusses hybrid filtering technique in BG recommender system for GDM even though it has an ability to learn from experience and to improve prediction performance. We hope that this study will encourage researchers to consider not only machine learning and artificial intelligent techniques but also hybrid filtering technique for BG recommender system in the future research.

Comparative analysis of classification algorithms for chronic kidney disease diagnosis Zainuri Saringat; Aida Mustapha; R. D. Rohmat Saedudin; Noor Azah Samsudin
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Chronic Kidney Disease (CKD) is one of the leading cause of death contributed by other illnesses such as diabetes, hypertension, lupus, anemia or weak bones that lead to bone fractures. Early prediction of CKD is important in order to contain the disesase. However, instead of predicting the severity of CKD, the objective of this paper is to predict the diagnosis of CKD based on the symptoms or attributes observed in a particular case, whether the stage is acute or chronic. To achieve this, a classification model is proposed to label stage of severity for kidney diseases patients. The experiments then investigated the performance of the proposed classification model based on eight supervised classification algorithms, which are ZeroR, Rule Induction, Support Vector Machine, Naïve Bayes, Decision Tree, Decision Stump, k-Nearest Neighbour, and Classification via Regression. The performance of the all classifiers is evaluated based on accuracy, precision, and recall. The results showed that the regression classifier perform best in the kidney diagnostic procedure.

A regression approach for prediction of Youtube views Lau Tian Rui; Zehan Afizah Afif; R. D. Rohmat Saedudin; Aida Mustapha; Nazim Razali
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

YouTube has grown to be the number one video streaming platform on Internet and home to millions of content creator around the globe. Predicting the potential amount of YouTube views has proven to be extremely important for helping content creator to understand what type of videos the audience prefers to watch. In this paper, we will be introducing two types of regression models for predicting the total number of views a YouTube video can get based on the statistic that are available to our disposal. The dataset we will be using are released by YouTube to the public. The accuracy of both models are then compared by evaluating the mean absolute error and relative absolute error taken from the result of our experiment. The results showed that Ordinary Least Square method is more capable as compared to the Online Gradient Descent Method in providing a more accurate output because the algorithm allows us to find a gradient that is close as possible to the dependent variables despite having an only above average prediction.

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