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Bulletin of Electrical Engineering and Informatics

EEI
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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.

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Articles 52 Documents

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Search results for , issue "Vol 9, No 4: August 2020" : 52 Documents clear

A real-time big data sentiment analysis for iraqi tweets using spark streaming Nashwan Dheyaa Zaki; Nada Yousif Hashim; Yasmin Makki Mohialden; Mostafa Abdulghafoor Mohammed; Tole Sutikno; Ahmed Hussein Ali
Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

The scale of data streaming in social networks, such as Twitter, is increasing exponentially. Twitter is one of the most important and suitable big data sources for machine learning research in terms of analysis, prediction, extract knowledge, and opinions. People use Twitter platform daily to express their opinion which is a fundamental fact that influence their behaviors. In recent years, the flow of Iraqi dialect has been increased, especially on the Twitter platform. Sentiment analysis for different dialects and opinion mining has become a hot topic in data science researches. In this paper, we will attempt to develop a real-time analytic model for sentiment analysis and opinion mining to Iraqi tweets using spark streaming, also create a dataset for researcher in this field. The Twitter handle Bassam AlRawi is the case study here. The new method is more suitable in the current day machine learning applications and fast online prediction.

Autonomous system to control a mobile robot Ayman Abu Baker; Yazeed Yasin Ghadi
Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

This paper presents an ongoing effort to control a mobile robot in unstructured environment. Obstacle avoidance is an important task in the field of robotics, since the goal of autonomous robot is to reach the destination without collision. Several algorithms have been proposed for obstacle avoidance, having drawbacks and benefits. In this paper, the fuzzy controller is used to tackle the problem of mobile robot autonomous navigation in unstructured environment. The objective is to make the robot move along a collision free trajectory until it reaches its target. The proposed approach uses the fuzzified, adaptive inference engine and defuzzification engine. Also number of linguistic labels is optimized for the input of the mobile robot in order to reduce computational time for real-time applications. The proposed fuzzy controller is evaluated subjectively and objectively with other approaches and also the processing time is taken in consideration.

A comparative study of wavelet families for electromyography signal classification based on discrete wavelet transform Abdelouahad Achmamad; Atman Jbari
Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Automatic detection of neuromuscular disorders performed using electromyography (EMG) has become an interesting domain for many researchers. In this paper, we present an approach to evaluate and classify the non-stationary EMG signals based on discrete wavelet transform (DWT). Most often researches did not consider the effect of DWT factors on the performance of EMG signals classification. This problem is still an interesting unsolved challenge. However, the selection of appropriate mother wavelet and related level decomposition is an essential issue that should be addressed in DWT-based EMG signals classification. The proposed method consists of decomposing a raw EMG signal into different sub-bands. Several statistical features were extracted from each sub-band and six wavelet families were investigated. The feature vector was used as inputs to support vector machine (SVM) classifier for the diagnosis of neuromuscular disorders. The obtained results achieve satisfactory performances with optimal DWT factors using 10-fold cross-validation. From the classification performances, it was found that sym14 is the most suitable mother wavelet at the 8th optimal wavelet level of decomposition. These simulation results demonstrated that the proposed method is very reliable for reducing cost computational time of automated neuromuscular disorders system and removing the redundancy information.

Batik pattern recognition using convolutional neural network Mohammad Arif Rasyidi; Taufiqotul Bariyah
Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Batik is one of Indonesia's cultures that is well-known worldwide. Batik is a fabric that is painted using canting and liquid wax so that it forms patterns of high artistic value. In this study, we applied the convolutional neural network (CNN) to identify six batik patterns, namely Banji, Ceplok, Kawung, Mega Mendung, Parang, and Sekar Jagad. 994 images from the 6 categories were collected and then divided into training and test data with a ratio of 8:2. Image augmentation was also done to provide variations in training data as well as to prevent overfitting. Experimental results on the test data showed that CNN produced an excellent performance as indicated by accuracy of 94% and top-2 accuracy of 99% which was obtained using the DenseNet network architecture.

Fruit sorting robot based on color and size for an agricultural product packaging system Tresna Dewi; Pola Risma; Yurni Oktarina
Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Indonesia's location in the equator gives an ideal condition for agriculture. However, agriculture suffers the issue of old farming due to a lack of youth interest working in this sector. This problem can be overcome by applying digital farming methods, in which one of them is by employing robots. Robotics technology is suitable for handling the harvested product, such as a sorting robot. This paper presents the application of a 4DOF fruit sorting robot based on color and size in a packaging system. The sorting is made possible by image processing where color is recognized by HSV analysis, and the diameter is known in the grayscale image and setting the thresholding. The fruit to be sorted is red and green tomatoes and red and green grapes. The experiments were conducted to show the effectiveness of the proposed method. The time requires for the robot to accomplish the task is 11.91s for red tomatoes, 11.76s for green tomatoes, 12.56s for red grapes, and 12.92s for green grapes. The time difference is due to the position of the boxes for the sorted fruit. The experimental results show that the arm robot manipulator is applicable for a sorting robot using the proposed method.

Road surface classification based on LBP and GLCM features using kNN classifier Arthur Ahmad Fauzi; Fitri Utaminingrum; Fatwa Ramdani
Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Autonomous Ground Vehicle (UGV) technology has shown a fast development this past year and proven to be useful. The use of UGV technology is restricted on a particular road condition. Classification of the road is an essential process in UGV, especially to control the autonomous vehicle. For example, the speed could be adjusted by referring to the road type, these process require a fast computational time. This research focuses on finding the most discriminant feature while keeping the number of features into a minimum to obtain fast computational time and accurate classification result. One can experiences difficulties because the condition of the road varies, this research proposes a combination of Gray Level Co-occurrence Matrix (GLCM) a statistical method to extract feature and Local Binary Pattern (LBP) feature to improve the robustness of the features. The kNN classifier is used to do the classification with the accuracy of 98% and 12 picture processed per second.

An overview of big data analysis Fabio Arena; Giovanni Pau
Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Big data represents one of the most profound and most pervasive evolutions in the digital world. Examples of big data come from Internet of Things (IoT) devices, as well as smart cars, but also the use of social networks, industries, and so on. The sources of data are numerous and continuously increasing, and, therefore, what characterizes big data is not only the volume but also the complexity due to the heterogeneity of information that can be obtained. The fastest growth in spending on big data technologies is happening within banking, healthcare, insurance, securities and investment services, and telecommunications. Remarkably, three of those industries lie within the financial sector, which has many particularly serviceable use cases for big data analytics, such as fraud detection, risk management, and customer service optimization. In fact, the definition of big data analysis refers to the process that encompasses the gathering and analysis of big data to obtain useful information for the business. This paper focuses on delivering a short review concerning the current technologies, future perspectives, and the evaluation of some use cased associated with the analysis of big data.

The effect of a SECoS in crude palm oil forecasting to improve business intelligence Al-Khowarizmi Al-Khowarizmi; Ilham Ramadhan Nasution; Muharman Lubis; Arif Ridho Lubis
Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Crude palm oil is a crop that has a harvest period of ± 2 weeks and is in dire need of dissemination of information using e-commerce in order to be able to predict the price of the yield of companies or individual gardens within the next 2 weeks in order to improve studies on business intelligence. The disadvantage of not implementing e-commerce is certainly detrimental to the garden owner because they have to go through an agent so prices are set based on the agent. So with the application of e-commerce, buyers of crude palm oil can predict prices in conducting business processes to the future. So the need to forecasting the price of crude palm oil heads in order to improve the application of business intelligence using the evolution-based artificial neural network (ANN) method which in this paper is tested with SECoS get a MAPE value of 0.035% and by applying business intelligence can protect transaction costs by 33.3%.

Comparison of routing protocol performance on multimedia services on software defined network Vivi Monita; Indrarini Dyah Irawati; Rohmat Tulloh
Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Software defined network (SDN) is a new paradigm in network engineering, where control plane and data plane are separated. Data plane is carried out on each node, while a control plane is centrally located. In conventional networks, the planes are implemented in the firmware of the router. In this paper, we implemented multimedia services on SDN using exterior and interior routing protocols, such as open shortest path first (OSPF), border gateway protocol (BGP) and routing information protocol (RIP), measured the quality of service (QoS), and analyze the measurement results. The test results showed that the greater the background traffic provided, the smaller the throughput. Traffic on the network will be congested, so the available bandwidth is also increasingly used up so that the number of bits sent every second decrease. Mean opinion score (MOS) test results are 4 showing good categories based on ITU-T standards.

A closed modified V-shaped uniplanar triple band ACS fed antenna for wireless applications Anuj Kumar; Anukul Jindal; Apurva Singh; Reshma Roy; Om Prakash Kumar; Tanweer Ali
Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

In the proposed paper, a uniplanar asymmetric coplanar strip (ACS) fed antenna with closed V-shaped radiating patch of size printed on FR4 substrate with loss tangent ( =0.02, height (h)=1.6mm, and dielectric constant of 4.4 covering WiMAX, X-band and WLAN applications is presented. The closed V-shaped radiating patch is formed by joning two rectangular stubs. The remaining radiating part is obtained by adding rectangular strips to feed to obtained multiband operation. The advantage of this structure is that it forms simple configuration as well as helps the antenna attaining three distinict useful frequency band with good impedance matching for S11 less than -10 dB criteria. The proposed antenna operates at 3.1 (WiMAX), 5.0 (WLAN) and 9.9 (X-band) GHz in simulation. Under measurement the proposed antenna shows multiband phenomenon at 3.2, 5.3 and 9.7 GHz, respectively. The antenna exhibits simulated gain of 2.51, 1.18 and 1.96 dB at 3.1, 5.0 and 9.9 GHz. The key parameters of the antenna like length and width of the multi-branched strips are optimized to get the multiband operation. The evolution and optimization process is dealt in detail with the help of S11, VSWR, current distributions, radiation patterns and gain.

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