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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
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Reduction of common mode voltage for cascaded multilevel inverters using phase shift keying technique Vinh-Quan Nguyen; QuangTho Tran
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.pp691-706

Abstract

Demand of cascaded multilevel inverters in industries of electric drives and renewable energy is increasing due to their large-scale capacity and high voltage. The modulation technique of inverters significantly affects the power quality of the inverter output voltage. This paper proposes a new method of carrier wave modulation using the phase shift keying technique for cascaded multilevel inverters. The phase of a constant frequency carrier wave is changed at an accurate time by an input sinusoidal control signal. This modulation technique is simply implemented and only needs a small memory. It also helps reduce the common mode voltage of inverters in order to suppress the output voltage harmonics. Moreover, the ability to reduce switching count also helps the inverters decrease switching loss. The simulated and experienced results on a cascaded 9-level 3-phase inverter and an F28379D DSP kit have validated the performance of the proposed technique compared with that of the APOD and POD methods.
Efficient processing of continuous spatial-textual queries over geo-textual data stream Kalpana Vivek Metre; Madan Kharat
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.pp1094-1102

Abstract

Due to the extensive use of social media and mobile devices, unbounded and massive data is generated continuously. The need to process this big data is increasing day by day. The traditional data processing algorithms fail to cater to the need of processing data generated by various applications such as digital geo-based advertising, and recommendation systems. There has been a high demand to process continuous spatial fuzzy textual queries over data stream of spatial-textual objects with high density by present locationbased and social network-based service applications. For the spatialkeyword data stream, the performance plays a vital role as the geo information and keyword description matching is needed for every incoming streaming object. The various continuous geo-keyword query processing methods normally lack the support for fuzzy keyword matching when processing the objects from the geo-textual data stream. The edit distancebased approach with the adaptive partitioning tree index for the queries is used for fuzzy string matching and it outperforms than the existing approaches in storage cost and query performance cost.
Mining the crime data using naïve Bayes model Lourdes M. Padirayon; Melvin S. Atayan; Jose Sherief Panelo; Carlito R. Fagela, Jr
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.pp1084-1092

Abstract

A massive number of documents on crime has been handled by police departments worldwide and today's criminals are becoming technologically elegant. One obstacle faced by law enforcement is the complexity of processing voluminous crime data. Approximately 439 crimes have been registered in sanchez mira municipality in the past seven years. Police officers have no clear view as to the pattern crimes in the municipality, peak hours, months of the commission and the location where the crimes are concentrated. The naïve Bayes modelis a classification algorithm using the Rapid miner auto model which is used and analyze the crime data set. This approach helps to recognize crime trends and of which, most of the crimes committed were a violation of special penal laws. The month of May has the highest for index and non-index crimes and Tuesday as for the day of crimes. Hotspots were barangay centro 1 for non-index crimes and barangay centro 2 for index crimes. Most non-index crimes committed were violations of special law and for index crime rape recorded the highest crime and usually occurs at 2 o’clock in the afternoon. The crime outcome takes various decisions to maximize the efficacy of crime solutions.
A functional framework based on big data analytics for smart farming Loubna Rabhi; Noureddine Falih; Lekbir Afraites; Belaid Bouikhalene
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.pp1772-1779

Abstract

Big data in agriculture is defined as massive volumes of data with a wide variety of sources and types which can be captured using internet of things sensors (soil and crops sensors, drones, and meteorological stations), analyzed and used for decision-making. In the era of internet of things (IoT) tools, connected agriculture has appeared. Big data outputs can be exploited by the future connected agriculture in order to reduce cost and time production, improve yield, develop new products, offer optimization and smart decision-making. In this article, we propose a functional framework to model the decision-making process in digital and connected agriculture.
Data mining technique to analyse and predict crime using crime categories and arrest records Most. Rokeya Khatun; Safial Islam Ayon; Md. Rahat Hossain; Md. Jaber Alam
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.pp1052-1060

Abstract

Generally, crimes influence organisations as it starts occurring frequently in society. Because of having many dimensions of crime data, it is difficult to mine the available information using off the shelf or statistical data analysis tools. Improving this process will aid the police as well as crime protection agencies to solve the crime rate in a faster period. Also, criminals can often be identified based on crime data. Data mining includes strategies at the convergence of machine learning and database frameworks. Using this concept, we can extract previously unknown useful information and their patterns of occurrence from unstructured data. The sole purpose of this paper is to give an idea of how data mining can be utilised by crime investigation agencies to discover relevant precautionary measures from prediction rates. Data sets are analysed by some supervised classification algorithms, namely decision tree, K-nearest neighbours (KNN) and random forest algorithms. Crime forecasting is done for frequently occurring crimes like robbery, assault, theft, etc. Specifically, the results indicate the superiority of the random forest algorithm in test accuracy.
Home security monitoring system with IoT-based Raspberry Pi I Gusti Made Ngurah Desnanjaya; I Nyoman Alit Arsana
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.pp1295-1302

Abstract

Home security monitoring system is a system that is able to monitor the house from unwanted events such as theft. Home monitoring systems can monitor and send users notices about the condition of their homes at the same time. Notifications sent in the form of pictures of the state of the room in the house, temperature conditions and gas density conditions. The home security monitoring system was created using Raspberry Pi as the control center of the system. It was connected with several sensors namely PIR sensor is used to detect objects that enter the room, raspicam is used to take pictures when the PIR sensor detects objects, temperature sensors and gas sensors are used to detect the state of temperature and gas concentration, and telegram is used as a liaison application to send notifications from Raspberry Pi to tool users. The final result of this research is to build a home security monitoring system with Raspberry Pi based on telegraph messenger. This system is able to monitor the security of the house from burglars or intruders, notify the temperature of the house and detect smoke or gas.
Network loss reduction and voltage improvement by optimal placement and sizing of distributed generators with active and reactive power injection using fine-tuned PSO Eshan Karunarathne; Jagadeesh Pasupuleti; Janaka Ekanayake; Dilini Almeida
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.pp647-656

Abstract

Minimization of real power loss and improvement of voltage authenticity of the network are amongst the key issues confronting power systems owing to the heavy demand development problem, contingency of transmission and distribution lines and the financial costs. The distributed generators (DG) has become one of the strongest mitigating strategies for the network power loss and to optimize voltage reliability over integration of capacitor banks and network reconfiguration. This paper introduces an approach for the optimizing the  placement and sizes of different types of DGs in radial distribution systems using a fine-tuned particle swarm optimization (PSO). The suggested approach is evaluated on IEEE 33, IEEE 69 and a real network in Malaysian Context. Simulation results demonstrate the productiveness of active and reactive power injection into the electric power system and the comparison depicts that the suggested fine-tuned PSO methodology could accomplish a significant reduction in network power loss than the other research works.
A deep learning-based cardio-vascular disease diagnosis system Hamdan Bensenane; Djemai Aksa; Fawzi Walid Omari; Abdellatif Rahmoun
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.pp963-971

Abstract

Recently ehealth technologies are becoming an overwhelming aspect of public health services that provides seamless access to healthcare information. Machine learning tools associated with IoT technology play an important role in developing such health technologies. This paper proposes a decision support system-based system (DSS) to make diagnosis of cardiovascular diseases. It uses deep learning approaches that classify electrocardiogram (ECG) signals. Thus, a two-stage long-short term memory (LSTM) based neural network architecture, along with an adequate preprocessing of the ECG signals is designed as a diagnosis-aided system for cardiac arrhythmia detection based on an ECG signal analysis. This deep learning based cardio-vascular disease diagnosis system (namely ‘DLCVD’) is built to meet higher performance requirements in terms of accuracy, specificity, and sensitivity. This must also be capable of an online real-time classification. Experimental results using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database show that DLCVD led to outstanding performance
Designing a secure campus network and simulating it using Cisco packet tracer Alaa H. Ahmed; Mokhaled N. A. Al-Hamadani
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 1: July 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i1.pp479-489

Abstract

The network is a massive part of life today. It participates not only on one side of life but in nearly every station, especially in educational organizations. The key aim of education is to share data and knowledge, making the network important for education. In particular, it is essential to ensure the exchange of information; thus, no one can corrupt it. To safe and trustworthy transfers between users, integrity and reliability are crucial questions in all data transfer problems. Therefore, we have developed a secure campus network (SCN) for sending and receiving information among high-security end-users. We created a topology for a campus of multi networks and virtual local area networks (VLANs’) using cisco packet tracer. We also introduced the most critical security configurations, the networking used in our architecture. We used a large number of protocols to protect and accommodate the users of the SCN scheme.
K-NN supervised learning algorithm in the predictive analysis of the quality of the university administrative service in the virtual environment Omar Freddy Chamorro-Atalaya; Guillermo Morales Romero; Adrián Quispe Andía; Beatriz Caycho Salas; Elizabeth Katerin Auqui Ramos; Primitiva Ramos Salazar; Carlos Palacios Huaraca
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.pp521-528

Abstract

The objective of this study is to analyze and discuss the metrics of the predictive model using the K-nearest neighbor (K-NN) learning algorithm, which will be applied to the data on the perception of engineering students on the quality of the virtual administrative service, such as part of the methodology was analyzed the indicators of accuracy, precision, sensitivity and specificity, from the obtaining of the confusion matrix and the receiver operational characteristic (ROC) curve. The collected data were validated through Cronbach's Alpha, finding consistency values higher than 0.9, which allows to continue with the analysis. Through the predictive model through the Matlab R2021a software, it was concluded that the average metrics for all classes are optimal, presenting a precision of 92.77%, sensitivity 86.62%, and specificity 94.7%; with a total accuracy of 85.5%. In turn, the highest level of the area under the curve (AUC) is 0.98, which is why it is considered an optimal predictive model. Having carried out this study, it is possible to contribute significantly to the decision-making of the higher institution in relation to the improvement of the quality of the virtual administrative service.

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