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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 64 Documents
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Search results for , issue "Vol 31, No 3: September 2023" : 64 Documents clear
Arduino based irrigation monitoring system using Node microcontroller unit and Blynk application Syahir Haziq Shahar; Syila Izawana Ismail; Nik Nur Shaadah Nik Dzulkefli; Rina Abdullah; Mazratul Firdaus Mohd Zain
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1334-1341

Abstract

In the case of traditional irrigation systems, irrigation is done manually by the user. Since the water is irrigated directly into the land, plants undergo high stress from variations in soil moisture, and therefore plant appearance is reduced. The objective is for remote monitoring and controlling devices, which are controlled using a Wi-Fi module, and to optimize water consumption. The sensors collect and evaluate data regarding changing weather, soil moisture levels, and water levels before sending timely notifications to the user's phone and desktop. The system also contains a device application (Blynk) that runs on various devices and may be used to monitor plant conditions at the user's workplace. It constantly monitors the situation and notifies the user of any developments that necessitate urgent action. It combines the sensor devices, Node microcontroller unit (NodeMCU), and Arduino to work together to meet the system's objectives.
Performance analysis of bitcoin forecasting using deep learning techniques Nrusingha Tripathy; Sarbeswara Hota; Debahuti Mishra
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1515-1522

Abstract

The most popular cryptocurrency used worldwide is bitcoin. Many everyday folks and investors are now investing in bitcoin. However, it becomes quite difficult to evaluate or foresee the price of bitcoin. The price of bitcoin is extremely difficult to forecast due to its swings. By this point, machine learning has developed a number of models to examine the price behaviour of bitcoin using time series data. The digital money, a different type of payment developed utilising encryption methods, is difficult to forecast. By utilising encryption technology, cryptocurrencies may act as both a medium of exchange and a virtual accounting system. To estimate the values of a future time sequence, this work introduces a deep learning-based technique for time series forecasting that treats the current data as time series and extracts the key traits of the past. To overcome the shortcomings of conventional production forecasting, three algorithms-auto-regressive integrated moving averages (ARIMA), long-short-term memory (LSTM) network, and FB-prophet-were investigated and contrasted. We compared the models using historical bitcoin data of past eight years, from 2012 to 2020. The “FB-prophet” model, which is significant, catches variation that might draw attention and avert possible problems.
Optimize single line to ground fault detection in distribution grid power system using artificial bee colony Feryal Ibrahim Jabbar; Dur Muhammad Soomro; Mohd Noor bin Abdullah; Nur Hanis Mohammad Radzi; Mazhar Hussain Baloch; Asif Ahmed Rahmoon; Hassan Falah Fakhruldeen
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1286-1294

Abstract

The most common power system (PS) distribution network fault, single lineto-ground fault (SLGF), causes residual current (I res) to start an electrical arc and high voltage (HV) three times the rated voltage in other healthy phases. HV from capacitive currents (IC) damages cable insulation and PS appliances. Peterson The neutral point coil (PC) reduces (I res) and extinguishes the electric arc, but the fault current (I fault) remains below the protection devices' threshold. Operations and equipment are riskier. PC adaptive eliminates electrical arcs, making the network safer. This paper detects I faults online using Texas instrument validation in MATLAB and adaptive by artificial bee colony (ABC). This paper discusses Texas instrument fault current detection and MATLAB validation. It improves system reliability, device protection, and copper savings by thousands of tons. ABC intelligently optimizes many mathematical problems. ABC with network neural artificial intelligence (AI) improves algorithm performance (artificial bee colony network neural (ABCNN)). This new method may improve distribution network SLGF detection. This first work can work online in electrical power stations by building the (eZdsp F28335-RS232) into the program to send fault signals to the control when SLGF occurs without damaging devices, equipment, cables, or power outages.
Evaluation of machine learning techniques for hypertension risk prediction based on medical data in Bangladesh Md. Asadullah; Md. Murad Hossain; Sabrina Rahaman; Muhammad Saad Amin; Mst. Sharmin Akter Sumy; Md. Yasin Ali Parh; Mohammad Amzad Hossain
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1794-1802

Abstract

Hypertension in Bangladesh is a leading cause of cardiovascular diseases, stroke, and kidney failure, resulting in significant morbidity and mortality. Preventive measures and simple health practices can effectively reduce hypertension and its complications. This study utilizes machine learning algorithms (Naive Bayes, support vector machine, logistic regression, random forest) to predict hypertension in high-risk individuals. The proposed hybrid model achieves a prediction accuracy of 78.17%, surpassing other machine learning methods. Random forest has the highest accuracy among the individual algorithms at 73.86%. Classification performance is evaluated using sensitivity, specificity, precision, and F-score, along with receiver operating characteristic analyses and confusion matrices through 10-fold cross-validation. These findings emphasize the importance of managing risk factors for better population health and highlight the efficacy of the hybrid model in hypertension prediction. The study underscores the significance of preventive measures in reducing the burden of hypertension-related diseases and improving overall well-being.
Texture and pixel intensity characterization-based image segmentation with morphology and watershed techniques Noor Khalid Ibrahim; Anwar Hassan Al-Saleh; Asmaa Sadiq Abdul Jabar
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1464-1477

Abstract

Image segmentation is an image processing technique that concentrated on finding and locating the parts of an image such as objects and boundaries. The purpose of locating these parts is for use in further processing analysis of an image such as recognition tasks, and content-based image retrieval. This paper introduces the segmentation procedure using a proposed template of features with watershed or morphology operations. Features template based on segmentation process conveys pixels’ intensities property perceived by the threshold value of histogram representation and texture feature where the regions are characterized by their texture content using standard deviation (SD) filtering. Wiener filter and histogram equalization (HE) techniques are used as preprocessing operations to enhance the image quality. The edge detector operator is hybridized to boost the segmentation process. Some statistical metrics are used for assessing and analyzing the performance of the stages in the proposed work. As a result, this proposed template of features achieved more performance with watershed and morphology segmentation.
Custom application programming interface data extractor applied to the Klarna e-commerce dataset Anas El Attaoui; Alaeddine Boukhalfa; Sara Rhouas; Norelislam El Hami
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1624-1632

Abstract

The use of smart technologies, the internet of things (IoT), social media, and others produce a billion or more pieces of data in different formats. Big data has risen to become the most sought-after field in computer science. The e-commerce evolved significantly and continued to flow until now and even after the pandemic. So, big data technologies helped with the development and approach to collecting, storing, processing, and extracting the data in this field. This paper proposes an application programming interface (API) data extractor tool applied to a collection of e-commerce public websites named “Klarna dataset” to extract its data, and an analysis of the results. The study of e-commerce sales has given results matching universal e-commerce sales tendencies. The peak of the number of e-commerce transactions and sales was between 2018-2019. Thus, the highest e-commerce sales price was in the United States for “luxury” or “fancy” products, and the highest sales in Europe were in Frankfurt, Germany, for hardware and gaming material.
Cyber security: performance analysis and challenges for cyber attacks detection Azar Abid Salih; Maiwan Bahjat Abdulrazzaq
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1763-1775

Abstract

Nowadays, with the occurrence of new attacks and raised challenges have been facing the security of computer systems. Cyber security techniques have become essential for information technology services to detect and react against cyber-attacks. The strategy of cyber security enables visibility of various types of attacks and vulnerabilities throughout computer networks, whilst also provides detecting cyber-attacks and effective ways of identifying and preventing them. This study mainly focuses on the performance analysis and challenges faced by cyber security using the latest techniques. It also provides a review of the attack detection process including the robust effectiveness of intelligent techniques. Finally, summarize and discuss some methods to increase attack detection performance utilizing deep learning (DL) architectures.
Fault analysis in grid-connected solar photovoltaic systems based on multi-objective grey wolf optimisation Vanam Satyanarayana; Vinoth Krishnamoorthy
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1250-1257

Abstract

The renewable energy resources depend on environmental concerns. Photovoltaic (PV) systems are an interesting form of renewable energy resource. The two main problems associated with PV systems are nonuniformity of irradiation and temperature change. PV achieving maximum power point tracking are dependent on power electronics converters. Emissions are a key challenge for the future of conventional renewable energy resources, which play an important role in the generation system. It is crucial in applications and industry because the primary goal of optimization is to discover specific research. We offer using the population of search agents based on fitness, the multi-objective grey wolf optimizer, a more modern meta-heuristic based on the hunting habits and social structure of grey wolves, for optimizing adaptive controllers for failures in grid analysis and power system integration various faults in the grid cause several power quality issues. Harmonics, voltage sag and swell imbalances, and many power conditioning topologies. This was carried out using MATLAB/Simulink, and the results showed a superior and flexible performance in reducing voltage sag and voltage swell problems and imperfections at various loading situations.
Distribution networks power loss allocation with various power factors Debani Prasad Mishra; Rudranarayan Senapati; Arun Kumar Sahoo; Jayanta Kumar Sahu; Surender Reddy Salkuti
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1234-1241

Abstract

The users of power distribution and transmission networks are generally guided to sustain advanced power factor (PF) of load as it is affecting the power loss of a feeder network where it separately owns major influence on electric charges layout. Therefore, some cautious loss allotment schemes are to be incorporated and an acceptable satisfying/penalizing policy for advanced/less PF users, independently. Keeping this in view, the mentioned article proposed a new scheme, i.e., the active power loss allocation (APLA) procedure which allows power loss to the system distributors by considering the load demands, topographical localities, and PFs. A newly modified procedure assigns inducements hardly to all the involved utilizers against change in load PF continuously, where it is evaluated via proper mathematical and statistical study. The efficiency of the newly modified APLA scheme is explored in two dissimilar frameworks of low PF using 33 bus system radially distributed network (RDN). The interpretation is in favor of examined transmitted, distributed, and allows generated PF to be verified subsequently. Comparatively, the results achieved highlight the originality of the present method compared with different standard schemes/frameworks.
Categorical encoder based performance comparison in pre-processing imbalanced multiclass classification Wiyli Yustanti; Nur Iriawan; Irhamah Irhamah
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1705-1715

Abstract

The contribution of this study is to offer suggestions for coding techniques for categorical predictor variables and comprehensive test scenarios to obtain significant performance results for imbalanced multiclass classification problems. We modify scenarios in the data mining process with the sample, explore, modify, model, and assess (SEMMA) framework coupled with statistical hypothesis testing to generalize the model performance evaluation conclusions as enhanced-SEMMA. We selected four open-source data sets with unequal class distributions and categorical predictors. Ordinal, nominal, dirichlet, frequency, target, leave one, one hot, dummy, binary, and hashing encoder methods are used. We use the grid-search technique to find the best hyperparameters. The F1-Score and area under the curve (AUC) are evaluated to select the optimal model. In all datasets with 10-fold stratified cross-validation and 95% to 99% accuracy for each dataset, the results show that support vector machine (SVM) outperforms the decision tree (DT) K-nearest neighbor (KNN), Naïve Bayes (NB), logistic regression (LR), and random forest (RF) algorithms. Probability-based or binary encodings, such as target, Dirichlet, dummy, one-hot, or binary, are best for situations with less than 3% of minor class proportions. Nominal or ordinal encoders are preferred for data with a minor class proportion of more than 3%.

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