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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 9,138 Documents
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Development design of an IoT-based smart home monitoring system with security features Rahmawati Fitriyan; Syafii Syafii
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp788-794

Abstract

A smart home is a system that has been programmed and can work automatically by utilizing internet of things (IoT) technology, this system can control various electronic devices in the home. This paper presents a design for developing an IoT-based smart home monitoring system with the addition of security features. This research aims to design and develop a smart home monitoring system that uses the IoT which operates via the web and improves the security aspects of the system. This research includes the development of hardware and software that enables efficient and safe monitoring and control of various aspects of the home via smartphone or computer-based devices using resources from solar power plants. This system relies on the use of a Raspberry Pi as a microcontroller and several sensors. In this context has important value in maintaining user security, and privacy and supports the growing development of the smart home technology industry.
Transformer faults identification via fuzzy logic approach Babagana Ali Dapshima; Renu Mishra; Priyanka Tyagi
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1327-1335

Abstract

The need for a constant electricity supply is at an alarming rate especially in the 21st century due to the high rate of increase in industrialization across the globe. Conventional protection schemes such as differential relays, Buchholz relay, and other techniques such as genetic algorithms and artificial neural networks, do not match the precision and reliability needed for transformer fault indentification, due to their complexity in computation, tedious training system, time consumption, and need the of human experts. The method proposed in this research is the use of a fuzzy inference system in detecting potential faults in power system transformers. The faults in the transformer were observed and analyzed using a simulation system of MATLAB/Simulink software. The suggested approach ensures swift identification of faults as it relies on if-then rules and only uses current and voltage measurements with 100% independence toward the power flow direction, making it highly reliable and simple to implement compared to other techniques for transformer fault identification.
Development of mathematical methods for diagnosing kidney diseases using fuzzy set tools Myrzakerimova, Alua; Kolesnikova, Kateryna
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp405-417

Abstract

An approach based on fuzzy set theory is presented in the scientific article to enhance the efficiency of diagnosing kidney diseases by decreasing the time required for making medical decisions. The suggested approach employs fuzzy models and algorithms that consider the uncertainty and variability of clinical data to optimize the assessment of the functional state of the kidneys, taking into account various risk factors and individual characteristics of patients. The paper suggests a technique to develop a system of fuzzy decision rules. This technique combines E. Shortliff’s iterative rules with functions from the studied classes of kidney diseases. Mathematical modeling and experimental studies have indicated relatively high effectiveness in classifying different forms of kidney diseases. The results can be used to formulate intelligent decision support systems in clinical practice and improve diagnostic and monitoring processes. Moreover, the findings may aid in shaping more targeted and effective health policies at the national and regional levels, enhancing access to healthcare, and promoting the population’s overall health.
Methods for optimizing the assignment of cloud computing resources and the scheduling of related tasks Zeenath Sultana; Raafiya Gulmeher; Asra Sarwath
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp1092-1099

Abstract

Efficient scheduling algorithms are necessary in the cloud paradigm to optimize service provision to clients while minimizing time duration, energy consumption, and violations of service level agreements (SLAs). Disregarding task appropriateness in resource scheduling can have a detrimental effect on the quality of service provided by cloud providers. Moreover, the utilization of resources in an ineffective manner will necessitate a substantial expenditure of energy to execute activities, leading to prolonged processing duration that adversely affect the temporal duration. Many research projects have focused on employment scheduling problems, and the algorithms used in these studies have offered answers that were deemed nearly flawless. This study presents a chaos bird swarm algorithm (Chaos BSA) approach that use machine learning to consider task priority while allocating tasks to the cloud platform. The method calculates the priorities of task virtual machines and incorporates these values into the scheduler. The scheduler will select tasks that align with the specified priorities and are compatible with the virtual machines. The implementation of the system utilized the openstack cloud platform and the cloudsim tool. The results and comparison with the baseline approach genetic algorithm (GA), ant colony optimization (ACO), and particle swarm optimization (PSO) clearly demonstrate that our Chaos BSA outperforms them by 18% in terms of efficiency.
Ataxia severity classification using enhanced feature selection and ranking optimization through machine learning model Pavithra Durganivas Seetharama; Shrishail Math
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1605-1613

Abstract

The examination of neurological disorders and the monitoring of ataxic gait are major scientific topics that benefit from digital signal processing techniques and machine learning (ML) technologies. In this research, an ML approach is optimized with the use of Spatio-temporal data obtained from a kinect-sensor to differentiate between normal gait and ataxic. The current ML-based approaches perform very poorly because they cannot build feature-correlation among many gait characteristics. Furthermore, current ML-based techniques generate more false-positive whenever data is imbalanced in nature; especially for performing multi-label classification. This work presents a feature selection and ranking (FSR) based on extreme gradient boost (XGB) for ataxia severity classification. The FSR-XGB introduce an enhanced misclassification minimization error optimization and presents a novel feature selection and ranking to introduce feature importance using new cross-validation mechanism, both of which are aimed at solving the multi-label classification research problems. Results from experiments demonstrate that the presented FSR-XGB approach outperforms other ML-based and deep learning-based approaches.
Comparative analysis of coding schemes for effective wireless communication Mohammed A. Aljubouri; Mahmoud Zaki Iskandarani
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp936-950

Abstract

Communication systems have recently focused on sending information efficiently and effectively from one sending point to another across a communication channel in the shortest amount of time. The main objective of this work is to compare the high-range coding scheme types, such as low density parity check (LDPC), turbo, and convolution, to see which works better and is more efficient. to establish a coding system with quadrature amplitude modulation (QAM) modulation and an additive white gaussian noise (AWGN) noisy channel to find which is more reliable and resilient for encoding and decoding. Because of this, digital media has to be sent over wireless channels and through satellites, requiring a connected network all the time, which has become a major concern over time. Furthermore, the high amount of data and efficiency are the focus points. After running the simulation, it was found that 64 QAM with a rate of 0.455 and an efficiency of 2.731 has a bit error rate (BER) of 0.001 and a 7.08 dB energy per bit Eb/No, and the 256 QAM simulation revealed that it has a BER of 0.001 and 11.88 dB Eb/No with a rate of 0.736 and an efficiency of 5.891. Over the AWGN channel noise, the simulation built a standard orthogonal frequency division multiplexing (OFDM) system, which used MATLAB.
Modelling and estimating trajectory points from RTK-GNSS based on an integrated modelling approach Ravenny Sandin Nahar; Kok Mun Ng; Fadhlan Hafizhelmi Kamaruzaman; Noorfadzli Abdul Razak; Juliana Johari
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp162-172

Abstract

The sparse Gaussian process regression (GPR) has been used to model trajectory data from Real time kinematics-global navigation satellite system (RTK-GNSS). However, upon scrutinizing the model residuals; the sparse GPR model poorly fits the data and exhibits presence of correlated noise. This work attempts to address these issues by proposing an integrated modeling approach called GPR-LR-ARIMA where the sparse GPR was integrated with the linear regression with autoregressive integrated moving average errors (LR-ARIMA) to further enhance the description of the trajectory data. In this integrated approach, the predicted trajectory points from the GPR were further described by the LR-ARIMA. Simulation of the GPR-LR-ARIMA on three sets of trajectory data indicated better model fit, revealed in the normally distributed model residuals and symmetrically distributed scatter plots. Correlated noise was also successfully eliminated by the model. The GPR-LR-ARIMA outperformed both the GPR and LRARIMA by its ability to improve mean-absolute-error in 2-dimension positioning by up to 86%. The GPR-LR-ARIMA contributes to enhancement of positioning accuracy of dynamic GNSS measurements in localization and navigation system with good model fit.
Energy efficient slotted synchronization approach in LoRaWAN Shayo, Eva; Abdalla, Abdi T.; Mwambela, Alfred; Sutikno, Tole
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp203-212

Abstract

In recent years, long-range wide-area networks (LoRaWAN) have gained much attention as low-power wide-area networks. LoRaWAN uses ALOHA as the medium access control protocol, where the end devices transmit data randomly and retransmit it up to eight times if collisions occur. ALOHA is not energy efficient and works perfectly for a smaller network. Several techniques, including the use of synchronization and scheduling schemes, to deal with the limitations imposed by ALOHA in LoRaWAN have been reported in the literature. However, the existing synchronization and scheduling algorithms transmit synchronization messages randomly using one super frame with fixed time slots that accommodate devices using different spreading factors, which limit the LoRaWAN network's scalability. This work proposes a slotted synchronization mechanism for transmitting synchronization requests to the gateway. The performance of the slotted synchronization was evaluated through simulation using packet delivery ratio (PDR) and energy efficiency as the performance parameters. The results indicate that when the number of devices in the network increases, a time-slotted synchronization consumes less energy, on average, by about 0.2 mAh. The use of a slotted synchronization can improve the energy efficiency of the end devices as collisions are completely avoided, achieving a PDR of 100%.
Deep transfer learning model for brain tumor segmentation and classification using UNet and chopped VGGNet Jayashree Shedbalkar; Kappargaon Prabhushetty
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1405-1415

Abstract

Brain tumors (BT) are a leading cause of cancer-related mortality worldwide, underscoring the critical need for early and precise detection to improve patient survival rates. Computer-aided diagnosis (CAD) plays a pivotal role in early BT detection by providing medical experts with valuable information image analysis. Various researchers have developed distinct methodologies, drawing from both machine and deep learning approaches. ML relies on manual feature analysis, which entails a time-intensive procedure of selecting an optimal feature extractor and necessitates domain experts with a deep understanding of feature selection. Conversely, deep learning methods exhibit superior performance compared to ML owing to their end-to-end, automated, high-level, and robust attribute mining capabilities. In this study introduced an innovative two-stage framework designed for the automatic classification of BT. In the initial stage, utilize U-Net models to conduct BT segmentation as part of the pre-processing step. Subsequently, in the second stage, utilize the improved BT images as input for a transfer learning-based model known as visual geometry group neural network (VGGNet), which excels in BT classification. The experimental analysis shows that the proposed approach has reported the average classification accuracy as 98.6%, 98.76%, and 99.45% for Meningioma, Glioma, and Pituitary BTs, respectively.
Power flow analysis in a distributed network for a smart grid system Thangavel Jothi; Manoharan Arun; Murugesan Varadarajan
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp42-52

Abstract

This article presents the implementation of a hybrid renewable energy-based smart grid in a distributed system. Photovoltaic (PV) and wind generation are variable and time-dependent, yet they are very efficient and correlated, making them perfect for a two-source hybrid system. To maximize the generated power, using the maximum power point tracker (MPPT) technique, the incremental conductance (IC) algorithm is employed. The proportional integral (PI)-based MPPT controller is chosen to improve the efficiency of conventional MPPT controllers. A battery system is implemented as an energy management system (EMS) to aid in transferring or managing the high load throughout peak and off-peak hours. The proposed system uses an optimization technique called genetic algorithm (GA) to control the inverter voltage. The GA-tuned PI controller performs efficiently and has less harmonic distortion than the traditional sinusoidal pulse width modulation (SPWM) control method. The designed system uses real-time measurable parameters as inputs and is simulated in Matlabtool. The system generates 42 kW of solar power and 250 kW of wind power; the total harmonic distortion (THD) value is 5% less than the SPWM technique. For future work, flexible alternating current transmission system (FACTS) devices can improve the power quality and lower the oscillations.

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