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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 9,174 Documents
 629   630   631   632   633 
Arabic speaker recognition using HMM Jabbar S. Hussein; Abdulkadhim A. Salman; Thmer R. Saeed
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.pp1212-1218

Abstract

In this paper, a new suggested system for speaker recognition by using hidden markov model (HHM) algorithm. Many researches have been written in this subject, especially by HMM. Arabic language is one of the difficult languages and the work with it is very little, also, the work has been done for text dependent system where HMM is very effective and the algorithm trained at the word level. One the problems in such systems is the noise, so we take it in consideration by adding additive white gaussian noise (AWGN) to the speech signals to see its effect. Here, we used HMM with new algorithm with one state, where two of these components, i.e. (π and A) are removed. This give extremely accelerates the training and testing stages of recognition speeds with lowest memory usage, as seen in the work. The results show an excellent outcome. 100% recognition rate for the tested data, about 91.6% recognition rate with AWGN noise.
Amazigh part-of-speech tagging with machine learning and deep learning Otman Maarouf; Rachid El Ayachi; Mohamed Biniz
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1814-1822

Abstract

Natural language processing (NLP) is a part of artificial intelligence that dissects, comprehends, and changes common dialects with computers in composed and spoken settings. At that point in scripts. Grammatical features part-of-speech (POS) allow marking the word as per its statement. We find in the literature that POS is used in a few dialects, in particular: French and English. This paper investigates the attention-based long short-term memory (LSTM) networks and simple recurrent neural network (RNN) in Tifinagh POS tagging when it is compared to conditional random fields (CRF) and decision tree. The attractiveness of LSTM networks is their strength in modeling long-distance dependencies. The experiment results show that LSTM networks perform better than RNN, CRF and decision tree that has a near performance.
Effect of Covid-19 on the electronic payment system: usage level trust and competence perspectives Mahmoud Odeh; Mohammad Yousef
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp1144-1155

Abstract

Covid-19 has dramatically spread globally and dramatically made several effects in almost all sectors. Electronic commerce and electronic payment systems are important sectors which affected directly by the Covid-19 pandemic. Online markets and electronic payment systems have been recognized as one of the fastest-growing technology in the last decade even within normal situations. However, several factors may influence such growth, which many consider as barriers or enablers of using the electronic payment system. This study aims to shed the light on the influence of Covid-19 on the electronic payment system from trust and competence perspectives. The study employed both qualitative and quantitative methodological approaches for data collection and analysis. The data was collected from 31 semi-structured interviews, 718 surveys, and annual reports. NVivo, Microsoft Visio, and Microsoft power business intelligence were used for the data analysis process. As a part of this study, a proposed framework has been developed which includes both technical and managerial parts.
Designing consensus algorithm for collaborative signature-based intrusion detection system Eko Arip Winanto; Mohd Yazid Idris; Deris Stiawan; Mohammad Sulkhan Nurfatih
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp485-496

Abstract

Signature-based collaborative intrusion detection system (CIDS) is highly depends on the reliability of nodes to provide IDS attack signatures. Each node in the network is responsible to provide new attack signature to be shared with other node. There are two problems exist in CIDS highlighted in this paper, first is to provide data consistency and second is to maintain trust among the nodes while sharing the attack signatures. Recently, researcher find that blockchain has a great potential to solve those problems. Consensus algorithm in blockchain is able to increase trusts among the node and allows data to be inserted from a single source of truth. In this paper, we are investigating three blockchain consensus algorithms: proof of work (PoW), proof of stake (PoS), and hybrid PoW-PoS chain-based consensus algorithm which are possibly to be implemented in CIDS. Finally, we design an extension of hybrid PoW-PoS chain-based consensus algorithm to fulfill the requirement. This extension we name it as proof of attack signature (PoAS).
IoT for smart home system Puji Catur Siswipraptini; Rosida Nur Aziza; Iriansyah Sangadji; Indrianto Indrianto; Riki Ruli A. Siregar; Grace Sondakh
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.pp733-739

Abstract

This paper examines the integration of smart home and solar panel system that is controlled and monitored using IoT (internet ofthings). To enable the smart home system to monitor the activity within the house and act according to the current conditions, it is equipped with several sensors, actuators and smart appliances. All of these devices have to be connected to a communication network, so they can communicate and provide services forthe smart home’s in habitants. The smart home system was first introduced to provide comfort and convenience, but later it should also address many other things, e.g. the importance of the efficient use of energy or electricity and hybrid use of energy sources. A solar panel is added to the smart home prototype and its addition is studied. Adaptive linear neural network is implemented in the prototype as an algorithm for predicting decisions based on the current conditions. The construction of the proposed integrated systemis carried out through several procedures, i.e. the implementation of the adaptive linear neural network (ADALINE) as the neural network method, the design of the prototype and the testing process. This prototype integrates functionalities of several household appliances into one application controlled by an Android-based framework.
A compact FPGA-based montgomery modular multiplier Ahmed A. H. Abd-elkader; Mostafa Rashdan; El-Sayed A. M. Hasaneen; Hesham F. A. Hamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 2: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i2.pp735-743

Abstract

This paper presents the FPGA-based implementation of compact montgomery modular multiplier (MMM). MMM serves as a building block commonly required in security protocols relying on public key encryption.  The proposed design is intended for hardware applications of lightweight cryptographic modules that is utilized for the system on chip (SoC) and internet of things (IoT) devices. The proposed design is a modification in the structure of MMM without any multiplication or subtraction processes. The main target of the new modification is enhancing the performance and reducing the area of the MMM hardware module. The operands and internal variables of the proposed hardware circuit is optimized to be bounded to the smallest efficient size to minimize the area and the critical path delay.   The proposed design was coded in VHDL, implemented in the Virtex-6 FPGA, and its performance was analyzed utilizing XILINX ISE tools. Our design occupies the smallest area comparing with other implementations on the same FPGA type. The proposed design saves in a range between 60.0 and 99.0% of the resources compared with other relevant designs.
Analyzing and detecting hemorrhagic and ischemic strokebased on bit plane slicing and edge detection algorithms Warqaa Shaher Alazawee; Zobeda Hatif Naji; Weaam Talaat Ali
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp1003-1010

Abstract

Nowadays, in the medical world, analyzing and diagnosing acute brain stroke and its location is a difficult process. In many hospitals, however, striking symptoms with the use of computed tomography (CT) imaging for patients is an important step in screening and diagnosis. Furthermore, computer-assisted accurate detection of diseased brain regions Because of the several sorts of strokes, their uneven form, and their great intensity and size, aided design is extremely challenging. Using the bit plan slice technique and the canny detector, we created and suggested a novel approach. Our algorithm produces excellent outcomes. The results demonstrate that our proposed algorithm is an accurate and reliable technique. This study also indicates that this system can detect two different types of strokes: hemorrhagic and ischemic strokes. The results of a comparison study of our suggested technique and other methods such as negative and logarithmic transformation methods are also included in this article.
Implementation multiple linear regresion in neural network predict gold price Musli Yanto; Sigit Sanjaya; Yulasmi Yulasmi; Dodi Guswandi; Syafri Arlis
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1635-1642

Abstract

The movement of gold prices in the previous period was crucial for investors. However, fluctuations in gold price movements always occur. The problem in this study is how to apply multiple linear regression (MRL) in predicting artificial neural networks (ANN) of gold prices. MRL is mathematical calculation technique used to measure the correlation between variables. The results of the MRL analysis ensure that the network pattern that is formed can provide precise and accurate prediction results. In addition, this study aims to develop a predictive pattern model that already exists. The results of the correlation test obtained by MRL provide a correlation of 62% so that the test results are said to have a significant effect on gold price movements. Then the prediction results generated using an ANN has a mean squared error (MSE) value of 0.004264%. The benefits obtained in this study provide an overview of the gold price prediction pattern model by conducting learning and approaches in testing the accuracy of the use of predictor variables.
Lightweight hardware fingerprinting solution using inherent memory in off-the-shelf commodity devices Mohd Syafiq Mispan; Aiman Zakwan Jidin; Muhammad Raihaan Kamarudin; Haslinah Mohd Nasir
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp105-112

Abstract

An emerging technology known as Physical unclonable function (PUF) can provide a hardware root-of-trust in building the trusted computing system. PUF exploits the intrinsic process variations during the integrated circuit (IC) fabrication to generate a unique response. This unique response differs from one PUF to the other similar type of PUFs. Static random-access memory PUF (SRAM-PUF) is one of the memory-based PUFs in which the response is generated during the memory power-up process. Non-volatile memory (NVM) architecture like SRAM is available in off-the-shelf microcontroller devices. Exploiting the inherent SRAM as PUF could wide-spread the adoption of PUF. Therefore, in this study, we evaluate the suitability of inherent SRAM available in ATMega2560 microcontroller on Arduino platform as PUF that can provide a unique fingerprint. First, we analyze the start-up values (SUVs) of memory cells and select only the cells that show random values after the power-up process. Subsequently, we statistically analyze the characteristic of fifteen SRAM-PUFs which include uniqueness, reliability, and uniformity. Based on our findings, the SUVs of fifteen on-chip SRAMs achieve 42.64% uniqueness, 97.28% reliability, and 69.16% uniformity. Therefore, we concluded that the available SRAM in off-the-shelf commodity hardware has good quality to be used as PUF.
Hybrid bacteria foraging-particle swarm optimization algorithm in DTC performance improving for induction motor drive Salah Eddine Rezgui; Hocine Benalla; Houda Bouhebel
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp660-669

Abstract

This paper presents a hybrid algorithm that combines the particle swarm optimization method with the bacteria foraging technique, named: BF-PSO. The aim is to achieve more efficient and precise parameters determination of the regulators that leads to performance improvement in the speed-loop control of an induction motor (IM) implemented in a direct torque control (DTC). The approach consists of tuning the proportional-integral (PI) parameters that meet high dynamics and tracking behavior using the hybrid BF-PSO algorithm. Investigations have been completed with Matlab/Simulink and several performance tests are conducted. The comparison results are exposed with the most used indices in the controllers' tuning with optimization techniques. It will be shown that the presented technique presents better quality results compared to the conventional method of calculated PI.

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