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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 63 Documents
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Search results for , issue "Vol 10, No 4: August 2021" : 63 Documents clear
Improved MSHA-1 algorithm with mixing method Rogel L. Quilala; Theda Flare G. Quilala
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.2366

Abstract

Abstract—Recently, a Modified SHA-1 (MSHA-1) has been proposed and claimed to have better security performance over SHA-1. However, the study showed that MSHA-1 hashing time performance was slower. In this research, an improved version of MSHA-1 was analyzed using avalanche effect and hashing time as performance measure applying 160-bit output and the mixing method to improve the diffusion rate.  The diffusion results showed the improvement in the avalanche effect of the improved MSHA-1 algorithm by 51.88%, which is higher than the 50% standard to be considered secured. MSHA-1 attained 50.53% avalanche effect while SHA1 achieved only 47.03% thereby showing that the improved MSHA-1 performed better security performance by having an improvement of 9.00% over the original SHA-1 and 3.00% over MSHA-1. The improvement was also tested using 500 random string for ten trials. The improved MSHA-1 has better hashing time performance as indicated by 31.03% improvement. Hash test program has been used to test the effectiveness of the algorithm by producing 1000 hashes from random input strings and showed zero (0) duplicate hashes.
An evolutionary approach to comparative analysis of detecting Bangla abusive text Tanvirul Islam; Nadim Ahmed; Subhenur Latif
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.3107

Abstract

The use of Bangla abusive texts has been accelerated with the progressive use of social media. Through this platform, one can spread the hatred or negativity in a viral form. Plenty of research has been done on detecting abusive text in the English language. Bangla abusive text detection has not been done to a great extent. In this experimental study, we have applied three distinct approaches to a comprehensive dataset to obtain a better outcome. In the first study, a large dataset collected from Facebook and YouTube has been utilized to detect abusive texts. After extensive pre-processing and feature extraction, a set of consciously selected supervised machine learning classifiers i.e. multinomial Naïve Bayes (MNB), multi layer perceptron (MLP), support vector machine (SVM), decision tree, random forrest, stochastic gradient descent (SGD), ridge, perceptron and k-nearest neighbors (k-NN) has been applied to determine the best result. The second experiment is conducted by constructing a balanced dataset by random under sampling the majority class and finally, a Bengali stemmer is employed on the dataset and then the final experiment is conducted. In all three experiments, SVM with the full dataset obtained the highest accuracy of 88%.
Execution of a smart street lighting system for energy saving enhancement Ihab Abdulrahman Satam; Suhail N. Shahab; Haider Abdulameer Kamel; Mokhaled N. A Al-Hamadani
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.2924

Abstract

In many countries, particularly, third world countries. The common issue is saving energy. That`s why smart systems considered now primary for life requirements. This work aims to solve the energy saving problem. We prepared a street model that contains several lampposts on both sides of the street; we placed three IR sensors between the lampposts alongside the street. The IR sensors are connected to the controller (in this work we used Arduino UNO). The controller takes the signal from the IR sensor, and then it sends the command to the lamppost to turn on or off. Depending on the number of cars passed, (we took a sample of a number of cars that passed on an actual street) and through formulas we calculated the power consumed by the lampposts in two cases, the first case is when the lights is always on. The second case is when the smart system applied. We also applied fuzzy logic to the system to take the intensity of the ambient light (the sun light) under consideration. The results showed that the proposed smart lighting system is efficient and reliable in saving energy. The energy saved for both (smart and fuzzy) systems was enormous.
Estimation of lithium-ion battery state-of-charge using an extended kalman filter Mouhssine Lagraoui; Ali Nejmi; Hassan Rayhane; Abderrahim Taouni
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.3082

Abstract

The main goal of a battery management system (BMS) is to estimate parameters descriptive of the battery pack operating conditions in real-time. One of the most critical aspects of BMS systems is estimating the battery's state of charge (SOC). However, in the case of a lithium-ion battery, it is not easy to provide an accurate estimate of the state of charge. In the present paper we propose a mechanism based on an extended kalman filter (EKF) to improve the state-of-charge estimation accuracy on lithium-ion cells. The paper covers the cell modeling and the system parameters identification requirements, the experimental tests, and results analysis. We first established a mathematical model representing the dynamics of a cell. We adopted a model that comprehends terms that describe the dynamic parameters like SOC, open-circuit voltage, transfer resistance, ohmic loss, diffusion capacitance, and resistance. Then, we performed the appropriate battery discharge tests to identify the parameters of the model. Finally, the EKF filter applied to the cell test data has shown high precision in SOC estimation, even in a noisy system.
Comparison of dimensionality reduction and clustering methods for SARS-CoV-2 genome Untari N. Wisesty; Tati Rajab Mengko
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.2803

Abstract

This paper aims to conduct an analysis of the SARS-CoV-2 genome variation was carried out by comparing the results of genome clustering using several clustering algorithms and distribution of sequence in each cluster. The clustering algorithms used are K-means, Gaussian mixture models, agglomerative hierarchical clustering, mean-shift clustering, and DBSCAN. However, the clustering algorithm has a weakness in grouping data that has very high dimensions such as genome data, so that a dimensional reduction process is needed. In this research, dimensionality reduction was carried out using principal component analysis (PCA) and autoencoder method with three models that produce 2, 10, and 50 features. The main contributions achieved were the dimensional reduction and clustering scheme of SARS-CoV-2 sequence data and the performance analysis of each experiment on each scheme and hyper parameters for each method. Based on the results of experiments conducted, PCA and DBSCAN algorithm achieve the highest silhouette score of 0.8770 with three clusters when using two features. However, dimensionality reduction using autoencoder need more iterations to converge. On the testing process with Indonesian sequence data, more than half of them enter one cluster and the rest are distributed in the other two clusters.
Digital centralized water meter using 433 MHz LoRa Hudiono Hudiono; Mochammad Taufik; Ridho Hendra Yoga Perdana; Amalia Eka Rakhmania
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.2950

Abstract

The local water supply corporation in Indonesia only uses analog water meter so that the monitoring of water usage information was conducted by officers manually. Officers must physically monitor the value in the customer's water meter that can lead to unreliable reading and ineffectiveness of process. Smart meter is one of the smart city metrics which could overcome this problem. This research uses the flow sensor to design and incorporate automated water meters. The measured value is then passed via the 433 MHz LoRa, a low-power wide-area network protocol, to the local hub, then forwarded to the server via the internet based cellular network. Results show that our proposed system's accuracy hit 97.31% at an ideal distance of 200 meters from customer to the local hub. The customer's water usage could be tracked in real time with our proposed system. Furthermore, the original water meter need not to be replaced which may minimize capital costs for this system.
Electricity-theft detection in smart grids based on deep learning Noor Mahmoud Ibrahim; Sufyan T. Faraj Al-Janabi; Belal Al-Khateeb
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.2875

Abstract

lectricity theft is a major concern for utilities. The smart grid (SG) infrastructure generates a massive amount of data, including the power consumption of individual users. Utilizing this data, machine learning, and deep learning techniques can accurately identify electricity theft users. A convolutional neural network (CNN) model for automatic electricity theft detection is presented. This work considers experimentation to find the best configuration of the sequential model (SM) for classifying and identifying electricity theft. The best performance has been obtained in two layers with the first layer consists of 128 nodes and the second layer is 64 nodes. The accuracy reached up to 0.92. This enables the design of high-performance electricity signal classifiers that can be used in several applications. Designing electricity signals classifiers has been achieved using a CNN and the data extracted from the electricity consumption dataset using an SM. In addition, the blue monkey (BM) algorithm is used to reduce the features in the dataset. In this respect, the focusing of this work is to reduce the features in the dataset to obtain high-performance electricity signals classifier models.
Evaluation of appliances mobile controller system using expectation-confirmation theory model Aslina Baharum; Chew Yun Fai; Rozita Ismail; Ismassabah Ismail; Farhana Diana Deris; Noorsidi Aizuddin Mat Noor
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.3061

Abstract

Nowadays, most Malaysians have used overpower usage of house appliances. Malaysian does not have the habit of controlling the household’s electricity consumption every day. Reducing electricity consumption is better for the earth, reducing harmful greenhouse emissions and minimizing the household's overall impact. Besides, one of the safety problems that Malaysian currently face is thieves entering the house when the owner is outstation or traveling. The proposed home appliance controller application can control and calculate the power consumption of home appliances. It can also control and set automatic timing based on the light to cause thieves to realize that the house may have people since the lights were turned on. This paper aims to identify the application features of controllers for home appliances, then develop the mobile application not only for gaming or entertainment but for better, enhanced, convenience and efficiency of lifestyle and finally to evaluate the users’ acceptance towards mobile app using expectation-confirmation theory model. Results show that perceived usefulness significant with confirmation (0.61) and continuance intentions (0.69). Perceived usefulness was demonstrated to be an essential predictor of continuance intentions (0.44). With this system or app, house appliances will be communicated and under control by the house owner.
A smart guidance navigation robot using petri net, database location, and radio frequency identification Yudhi Gunardi; Jumadril J. N.; Dirman Hanafi
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.3077

Abstract

The objective of this research is, to explain a new framework to navigate the movement of the robot towards a target goal. This involves the need for the robot to move from the initial position to 1 out of 30 rooms. Therefore the strategy used involves the combination of the room database stored in the RFID data using the petri net (PN) method to simulate and model the movement of the robot for navigation after which the dynamic behavior of the robot is moving to the desired location was analyzed. The process started from the creation of an environmental map determined by the user followed by modeling through PN and the result was used to produce a marking value which explains and navigates the movement of the robot towards the selected room. The marking value was also used as the database for the robot's movement and later substituted with the RFID to be used as the sensor input in the implementation stage. It was concluded that the robot has the ability to move to the target position according to the database stored in RFID and designed to move forward and turn left and right. For example, it followed the marking value M1 M2 M3M13M12 M11 M10 M9 M8 to Room 1 and M1 M2 M46 M47 to Room 29.
A multi domains short message sentiment classification using hybrid neural network architecture Devi Munandar; Andri Fachrur Rozie; Andria Arisal
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.2790

Abstract

Sentiment analysis of short texts is challenging because of its limited context of information. It becomes more challenging to be done on limited resource language like Bahasa Indonesia. However, with various deep learning techniques, it can give pretty good accuracy. This paper explores several deep learning methods, such as multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and builds combinations of those three architectures. The combinations of those three architectures are intended to get the best of those architecture models. The MLP accommodates the use of the previous model to obtain classification output. The CNN layer extracts the word feature vector from text sequences. Subsequently, the LSTM repetitively selects or discards feature sequences based on their context. Those advantages are useful for different domain datasets. The experiments on sentiment analysis of short text in Bahasa Indonesia show that hybrid models can obtain better performance, and the same architecture can be directly used in another domain-specific dataset.

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