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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 66 Documents
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Search results for , issue "Vol 23, No 2: August 2021" : 66 Documents clear
Indoor positioning utilizing bluetooth low energy RSSI on LoRa system Kavetha Suseenthiran; Abd Shukur Ja'afar; Ku Wei Heng; Mohamad Zoinol Abidin Abd Aziz; Azmi Awang Md Isa; Siti Huzaimah Husin; Nik Mohd Zarifie Hashim
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp927-937

Abstract

Indoor positioning systems has become popular in this era where it is a network of devices used to locate people or object especially in indoor environment instead of satellite-based positioning. The satellite-based positioning global positioning system (GPS) signal is affected and loss incurred by the wall of the building causes the GPS lack of precision which leads to large positioning error. As a solution to the indoor area coverage problem, an indoor positioning based on bluetooth low energy (BLE) and long range (LoRa) system utilising the receive signal strength indicator (RSSI) is proposed, designed and tested. In this project, the prototype of indoor positioning system is built using node MCU ESP 32, LoRa nodes and BLE beacons. The node MCU ESP 32 will collect RSSI data from each BLE beacons that deployed at decided position around the area. Then, linear regression algorithm will be used in distance estimation. Next, particle filteris implemented to overcome the multipath fading effect and the trilateration technique is applied to estimate the user’s location. The estimated location is compared to the actual position to analyze the root mean square error (RMSE) and cumulative distribution function (CDF). Based on the experiment result, implementing the particle filter reduces the error of location accuracy. The particle filter achieves accuracy with 90% of the time the location error is lower than 2.6 meters.
Optimization of learning algorithms in multilayer perceptron for sheet resistance of reduced graphene oxide thin-film Noor Aiman bin Aminuddin; Nurlaila Ismail; Marianah Masrie; Siti Aishah Mohamad Badaruddin
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp686-693

Abstract

Multilayer perceptron (MLP) optimization is carried out to investigate the classifier's performance in discriminating the uniformity of reduced Graphene Oxide(rGO) thin-film sheet resistance. This study used three learning algorithms: resilient back propagation (RP), scaled conjugate gradient (SCG) and levenberg-marquardt (LM). The dataset used in this study is the sheet resistance of rGO thin films obtained from MIMOS Bhd. This work involved samples selection from a uniform and non-uniform rGO thin-film sheet resistance. The input and output data were under going data pre-processing: data normalization, data randomization and data splitting. The data were dividedin to three groups; training,  validation and testing with a ratio of 70%: 15%: 15%, respectively. A varying number of hidden neurons optimized the learning algorithms in MLP from 1 to 10. Their behavior helped establish the best learning algorithms in discriminating MLP for rGO sheet resistance uniformity. The performances measured were the accuracy of training, validation and testing dataset, mean squared errors (MSE) andepochs. All the analytical work in this study was achieved automatically via MATLAB software version R2018a. It was found that the LM is dominant inthe optimization of a learning algorithm in MLP forrGO sheet resistance.The MSE for LM is the most reduced amid SCG and RP. 
Internal mode control based coordinated controller for brushless doubly fed induction generator in wind turbines during fault conditions Ahsanullah Memon; Mohd Wazir Mustafa; Attaullah Khidrani; Farrukh Hafeez; Shadi Khan Baloach; Touqer Ahmed Jumani
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp650-656

Abstract

Brushless double fed induction generator (BDFIG) based machines have gained popularity in wind turbine applications because of their easily accessible design. Low voltage ride through (LVRT) is critical for the reliable integration of renewable energy with the power grid. The refore, LVRT capability of brushless DFIGs makes them an attractive choice for maintaining voltage stability in grid. The existing works on BDFIG control suffer from two major drawbacks. Firstly, the methodology does not consider LVRT as a design metric, and secondly, these techniques do not have any means for coordinating between a machine side inverter (MSI) and grid side inverter (GSI). This results in sub-optimal controller design and eventually result in the violation of grid code requirements. To solve these issues, this paper proposes the use of brushless DFIGs in wind turbines using a control technique based on analytical modeling. Moreover, employing internal model control (IMC), the proposed technique can effectively coordinate the control between the MSI and GSI resulting in reduced oscillations, overshoots and improved stability under fault conditions. Furthermore, the simulation results for wind turbine generators show that the proposed scheme significantly improves the stability and compliance of grid codes ascompared to the existing hardware techniques.
Evaluation of a wireless low-energy mote with fuzzy algorithms and neural networks for remote environmental monitoring Ricardo Yauri; Jinmi Lezama; Milton Rios
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp717-724

Abstract

The devices developed for applications in the internet of things have evolved technologically in the improvement of hardware and software components, in the area of optimization of the life time and to increase the capacity to save energy. This paper shows the development of a fuzzy logic algorithm and a power propagation neural network algorithm in a wireless mote (IoT end device). The fuzzy algorithm changes the transmission frequency according to the battery voltage and solar cell voltage. Moreover,the implementation of algorithms based on neural networks, implied a challenge in the evaluation and study of the energy commitment for the implementation of the algorithm, memory space optimization and low energy consumption.
A comparative analysis of metaheuristic algorithms in fuzzy modelling for phishing attack detection Noor Syahirah Nordin; Mohd Arfian Ismail; Tole Sutikno; Shahreen Kasim; Rohayanti Hassan; Zalmiyah Zakaria; Mohd Saberi Mohamad
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp1146-1158

Abstract

Phishing attack is a well-known cyber security attack that happens to many people around the world. The increasing and never-ending case of phishing attack has led to more automated approaches in detecting phishing attack. One of the methods is applying fuzzy system. Fuzzy system is a rule-based system that utilize fuzzy sets and fuzzy logic concept to solve problems. However, it is hard to achieve optimal solution when applied to complex problem where the process of identify the fuzzy parameter becomes more complicated. To cater this issue, an optimization method is needed to identify the parameter of fuzzy automatically. The optimization method derives from the metaheuristic algorithm. Therefore, the aim of this study is to make a comparative analysis between the metaheuristic algorithms in fuzzy modelling. The study was conducted to analyse which algorithm performed better when applied in two datasets: website phishing dataset (WPD) and phishing websites dataset (PWD). Then the results were obtained to show the performance of every metaheuristic algorithm in terms of convergence speed and four metrics including accuracy, recall, precision, and f-measure. 
Two-fold complex network approach to discover the impact of word-order in Urdu language Nuzhat Khan; Mohamad Anuar Kamaruddin; Usman Ullah Sheikh; Muhammad Paend Bakht
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp1039-1048

Abstract

This work examines standard Urdu text to confirm impact of word order in the language structure. The complex network approach is used to obtain universal properties of two different word co-occurrence networks. Macro and micro scale two-fold examinations of networks are performed for structure discovery. While preserving the vocabulary size, two networks are generated from same text with and without standard word order. In addition, text networks are benchmarked with a random network to extract global features. Achieved outcomes indicate certain word order in Urdu structure for most of the sentences. The normal and shuffled text networks demonstrated similar large-scale characteristics. The results show that average path length and network diameter is reduced after shuffling. On the other hand, clustering coefficient is increased in shuffled text as compared to normal text. Our results validated that few short sentences in range of three words are fully free order. The observations revealed that long sentences are ambiguous without standard order. Both networks are topologically similar but shuffling caused massive discrepancy in network composition and sentence structure. Inside graph view, grammatical association-based words connectivity exists in normal text network. With this universal approach, impact of word order in Urdu language is confirmed. Meanwhile, this breakthrough directs to uncover language composition by extracting small sentences as motifs.
Dual channel speech enhancement using particle swarm optimization Dalal Hamza; Tariq Tashan
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp821-828

Abstract

Adaptive processing for canceling noise is a powerful technology for signal processing that can completely remove background noise. In general, various adaptive filter algorithms are used, many of which can lack the stability to handle the convergence rate, the number of filter coefficient variations, and error accuracy within tolerances. Unlike traditional methods, to accomplish these desirable characteristics as well as to efficiently cancel noise, in this paper, the cancelation of noise is formulated as a problem of coefficient optimization, where the particle swarm optimization (PSO) is employed. The PSO is structured to minimize the error by using a very short segment of the corrupted speech. In contrast to the recent and conventional adaptive noise cancellation methods, the simulation results indicate that the proposed algorithm has better capability of noise cancelation. The results show great improvement in signal to noise ratio (SNR) of 96.07 dB and 124.54 dB for finite impulse response (FIR) and infinite impulse response (IIR) adaptive filters respectively.
Cotton-wool spots, red-lesions and hard-exudates distinction using CNN enhancement and transfer learning Tian-Swee Tan; M. A. As'ari; Wan Hazabbah Wan Hitam; Qi Zhe Ngoo; Matthias Tiong Foh thye; Kelvin Ling Chia hiik
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp1170-1179

Abstract

The automatic retinal disease diagnosis by artificial intelligent is an interesting and challenging topic in the medical field. It requires an appropriate image enhancement technique and a sufficient training dataset for the specific retina conditions. The aim of this study was to design an automatic diagnosis convolutional neural network (CNN) model which does not require a large training dataset to specifically identify diabetic retinopathy symptoms, which are cotton wool, exudates spots and red lesionin colour fundus pictures. A novel framework comprised image enhancement method by using upgraded contrast limited adaptive histogram equalization (UCLAHE) filter and transferred pre-trained networks was developed to classify the retinal diseases regarding to the symptoms. The performance of the proposed framework was evaluated based on accuracy, sensitivity and specificity metrics. The collected results have proven the robustness of the proposed framework in offering good accuracy in retina diseases diagnosis. 
Active filtering capability based on the RSC control of WECS equipped with a DFIG Touati Abdelwahed; Majdoul Radouane; Aboulfatah Mohamed; Rabbah Nabila
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp760-771

Abstract

The increasing integration of decentralized production from renewable energies on the electricity grids should contribute to improving the stability and quality of the energy produced. The main objective of this work is to prove how renewable energy sources can improve the quality of electrical energy in the grid. In particular, controlled by the oriented flux technique, a double - feed induction generator DFIG driven by a wind turbine is together used to produce active power to the electrical network and to compensate the currentharmonics generated by a non - linear load, which leads to improve the supplied energy quality. The Active filtering function consists first of all in identifying the current harmonics using the theory of instantaneous active and reactive powers quality (PQ). Then, the closed loop based on the fast terminal sliding mode control (FTSMC) control allows both the generator to follow the optimal operating point of the wind turbine and to compensate for the current harmonics. The analysis and simulation results using MATLAB/Simulink confirm the effectiveness and the limits of the proposed methods and also show the performances of the law control which provides flexibility, high precision and fast response.
Sarcasm detection of tweets without #sarcasm: data science approach Rupali Amit Bagate; R. Suguna
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp993-1001

Abstract

Identifying sarcasm present in the text could be a challenging work. In sarcasm, a negative word can flip the polarity of a positive sentence. Sentences can be classified as sarcastic or non-sarcastic. It is easier to identify sarcasm using facial expression or tonal weight rather detecting from plain text. Thus, sarcasm detection using natural language processing is major challenge without giving away any specific context or clue such as #sarcasm present in a tweet. Therefore, research tries to solve this classification problem using various optimized models. Proposed model, analyzes whether a given tweet, is sarcastic or not without the presnece of hashtag sarcasm or any kind of specific context present in text. To achieve better results, we used different machine learning classification methodology along with deep learning embedding techniques. Our optimized model uses a stacking technique which combines the result of logistic regression and long short-term memory (LSTM) recurrent neural net feed to light gradient boosting technique which generates better result as compare to existing machine learning and neural network algorithm. The key difference of our research work is sarcasm detection done without #sarcasm which has not been much explored earlier by any researcher. The metrics used for evolutionis F1-score and confusion matrix.

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