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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 27, No 1: July 2022" : 64 Documents clear
Thai digit handwriting image classification with convolutional neural networks Kheamparit Khunratchasana; Tassanan Treenuntharath
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp110-117

Abstract

This paper aims to determine the efficiency in classifying and recognizing Thai digit handwritten using convolutional neural networks (CNN). We created a new dataset called the Thai digit dataset. The performance test was divided into two parts: the first part determines the exact number of epochs, and the second part examines the occurrence of overfits in the model with Keras library's EarlyStoping() function, processed through Cloud Computing with Google Colaboratory, and used a Python programming language. The main parameters for the model were a dropout of 0.75, mini-batch size of 128, the learning rate of 0.0001, and using an Adam optimizer. This study found the model's predictive accuracy was 96.88 and the loss was 0.1075. The results showed that using CNN in image classification and recognition. It has a high level of prediction efficiency. However, the parameters in the model must be adjusted accordingly.
Performance evaluation of unmanned aerial vehicle communication by increasing antennas of cellular base stations Rajesh Kapoor; Aasheesh Shukla; Vishal Goyal
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp222-237

Abstract

The utilization of unmanned aerial vehicles (UAVs) increases with increased performance of their communication link with the ground remote station. Integrating UAVs with existing cellular networks provides the possibility of enhanced performance of communication links. The base stations of existing cellular networks are installed with fixed number of antennas. The performance of UAV communication links can be further enhanced by increasing antennas of cellular base stations of existing networks using multiple antenna techniques such as multi ple input multiple output (MIMO). In this proposed scheme, Massive MIMO technology is used for UAV communications, wherein hundreds of antennas are mounted on cellular base stations. This set up provides significant advantage in terms of enhancement in per formance of UAV communication links, as compared to existing methods of UAV communication. In this paper, performance evaluation of UAV communication links is carried out by increasing the number of antennas at base stations of existing cellular networks. For this evaluation, firstly basic multiple antennas techniques such as point - to - point MIMO and multi - user MIMO (MU - MIMO) are covered based on existing studies and findings. Subsequently, an antenna dependent closed form expression for uplink channel capac ity of massive MIMO based UAV communication links is derived, with few numerical results.
High performance time series models using auto autoregressive integrated moving average Redha Ali Al-Qazzaz; Suhad A. Yousif
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp422-430

Abstract

Forecasting techniques have received considerable interest from both researchers and academics because of the unique characteristics of businesses and their influence on several areas of the economy. Most academics utilize the autoregressive integrated mov ing average (ARIMA) approach to forecasting the future. However, researchers face challenges, such as analyzing the data and selecting the appropriate ARIMA parameters, especially with large datasets. This study investigates the use of the automatic ARIMA (Auto ARIMA) function for forecasting Brent oil prices. It demonstrates the benefits of using Auto ARIMA over ARIMA for determining the appropriate ARIMA parameters based on measures such as root mean square error ( RMSE ) , mean absolute error ( MAE ) , and aka ike information criterion ( AIC ) without requiring the attention of an expert data scientist as it bypasses several steps needed for manual ARIMA. Auto ARIMA produced an RMSE of 12.5539 and an AIC of 1877.224, which are comparable to the values resulting fr om the manual ARIMA with the help of expert data scientists; thus, it saves analysis time and offers the best model result.
Quadratic vector support machine algorithm, applied to prediction of university student satisfaction Omar Chamorro-Atalaya; Guillermo Morales-Romero; Yeferzon Meza-Chaupis; Elizabeth Auqui-Ramos; Jesús Ramos-Cruz; César León-Velarde; Irma Aybar-Bellido
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp139-148

Abstract

This study aims to identify the most optimal supervised learning algorithm to be applied to the prediction of satisfaction of university students. In this study, the IBM SPSS - 25.0 software was used to test the reliability of the satisfaction questionnaire and the MATLAB R2021b software through the classification learner technique to determine the supervised learning algorithm. The experimental results determine a Cronbach's Alpha reliability of 0.979, in terms of the classification algorithm, it is validate d that the quadratic vector support machine (SVM) has better performance metrics, being correct in 97.8% (a ccuracy) in the predictions of satisfaction of university students, with a r ecall (sensitivity) of 96.5% and an F1 score of 0.968. Likewise, when eva luating the classification model by means of the receiver operating characteristic curve (ROC) technique, it is identified that for the three expected classes of satisfaction the value of the area under the curve (AUC) is equal to 1, in such sense the pred ictive model through the SVM Quadratic algorithm, has a high capacity to distinguish between the 3 classes ; i) d issatisfied, ii) s atisfied and iii) v ery satisfied of satisfaction of university students.
Outlier tolerant adaptive sampling rate approach for wireless sensor node Sunil Kumar Selvaraj; Venkatramana Bhat Pundikai
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp281-289

Abstract

Nowadays the wireless sensor network (WSN) has been used for variety of applications and still lot of research in progress around the corner for the betterment of the wireless sensor network technology. In this paper, one such issues related to energy consumption in sensor node due to fixed sampling interval of sensing unit and its impact on redundant data is discussed with a possible solution. The association of sampling interval and its impact on energy dissipation in sensor node enforces the need for study on energy efficient adaptive sampling interval approach. The lack of serious consideration of outlier in sensor data degrades the performance of the existing adaptive sampling interval approach. The result of the proposed approach of in-network clustering algorithm shows the better efficiency towards detecting the outlier in real time. The results also showcase the better efficiency of proposed approach in terms of rapid optimization of sampling interval compared to simple variance based approach.
Modelling and proportional-integral-derivative controller design for position analysis of the 3-degree of freedom Nur Syahirah Eshah Budin; Khairuddin Osman
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp62-70

Abstract

A closed-loop system or which can also be known as a feedback system helps the system to achieve the desired output by comparing the input and the output values. If any difference is detected, the closed-loop system will create an error signal and automatically responds to it. Other than that, the proportional-integral-derivative (PID) controller has a feedback mechanism. Thus, this creates the curiosity whether the closed-loop system and PID which both have the characteristic of a feedback system, can give the same. In this paper, the comparison of the model of 3 degree of freedom (DOF) Mitsubishi RV2-AJ is being made between two models of a robot arm that has a closed-loop system but only one that is embedded with PID controller while the other one is not, these two are simulated for different positions. The new model is created by using Solidworks which is later exported to Matlab-Simulink. The results from MATLAB-Simulink show that the model which is equipped with a PID controller has better results in terms of the rise time and percentage of overshoot. These results confirm the effectiveness of PID controller in producing smaller errors in the systems even when both models are created together with closed-loop systems.
Configuration of an IoT microhydraulic power generation system for education Zaira Pineda-Rico; Pedro Cruz Alcantar; Ulises Pineda-Rico; Francisco Javier Martinez-Lopez
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp44-51

Abstract

Internet of things (IoT) involves the communication of all kinds of things embedded with sensors, electronics, software and people connected to the internet. Knowledge of IoT in the classroom provides an experience for engineering students to explore different career options. Under this scope, an IoT platform on the Arduino UNO and Raspberry Pi 3 development boards was built for academic purposes. The IoT platform was configured to monitor a microhydraulic power generation system used for the study of small-scale hydraulic power production, using a hydraulic head provided by a system of hydraulic pumps in series and/or parallel connection. The platform was designed considering a monitoring station for the acquisition of analog, digital, SPI and PWM data; a control station that receives data from the monitoring station and sends data to the cloud. The communication between modules was established using a publication/subscription system. The platform allows to registrate, visualize and process data directly in the cloud. Meaning that the IoT systems connected to this platform can be monitored from a cell phone, tablet or PC with internet access, promoting immediate access to the emerging information generated in the operating system.
Converting 2D magnetic resource imagining brain tumors to 3D structure using depth map machine learning techniques K. A. Mohamed Riyazudeen; Mohamed Sathik
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp513-520

Abstract

The use of medical imaging technology aids clinicians in recognizing and assessing patient problems, as well as improving treatment procedures. However, while conducting complex procedures such as the excision of brain tumors, the knowledge and biological research gathered from 2D images are insufficient. Converting 2D images to 3D images may assist doctors in determining the size, shape, and sharp area of tumor cells in the brain. The feasibility of translating 2D medical image data to a 3D model is described in this work. A suggested framework for predicting the size, shape, and location of a brain tumor using a minimized genetic machine learning method, and then converting the tumor information into 3D images using a depth map estimation approach after detecting the tumor information. When the tumor is located, the left and right view data are combined to form a 3D magnetic resonance imaging reconstruction. We used mixed reality methods to minimize file size while preserving the greatest quality of the model during a brain surgical operation.
Improving the efficiency of machine learning models for predicting blood glucose levels and diabetes risk Kriengsak Yothapakdee; Sarawoot Charoenkhum; Tanunchai Boonnuk
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp555-562

Abstract

Fasting blood glucose is used as an indicator in the process of predicting diabetes risk. This research aims to, i) create a model for predicting blood glucose level using data mining algorithms, ii) a selection algorithm was used to select a feature from the correlation of the data, and iii) to compare the model's performance with the classical methods. All clinical data ware recorded and compiled in a database by hospital staff from 2014-2019. In our previous research, the blood glucose prediction model had an acceptable accuracy where 18 patient features were used as input data to the data mining process. In this research, we demonstrated that the random forest classifier and extra tree classifier algorithms have an outstanding in discarding non-critical attributes. And the process of reducing the number of those features has impacted the glycemic prediction model with higher efficiency. Seventeen machine learning algorithms are used to find the best performance models. Our results clearly show that the improved prediction model is more efficient. This experiment has shown that improvements to our proposed model were able to predict blood glucose levels with 99.69% and 99.63% accuracy for random forest classifier, extra tree classifier, and Gaussian process classifier, respectively.
Modeling and simulation of electro-hydraulic telescopic elevator system controlled by programmable logic controller Istabraq Hassan Abed Al-Had; Farag Mahel Mohammed; Jamal A.-K. Mohammed
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp71-78

Abstract

Traditional hydraulic cylinders are widely used in industry as load lifting tools. There is difficulty in employing these cylinders in narrow installation space like building elevators as well as they require a long working stroke, so solving this problem requires using the telescopic hydraulic cylinder instead. This cylinder is a unique hydraulic cylinder design that uses a sequence of decreasing diameters to create a long operating stroke in a compact-retracted form. In this work, the Auto Station software is used to build a telescopic hydraulic cylinder model that includes hydraulic elements such as double-acting hydraulic telescopic cylinders, pump, valves, pipeline, and filter, as well as electrical parts such as programmable logic controller (PLC), push-bottoms, and position sensors. The proposed model is operated by a PLC controller and has three floors with an overall height of around 300 cm for lifting a 100 kg payload. The accuracy and validity of this model in lifting big weights were demonstrated by the analytical findings of characteristic curves for cylinder position and velocity. This model can be used a basic reference to analyze and construct hydraulic cylinders with any number of stages. The findings of simulations reveal that a quick change in pressure due to phase change causes multi-phase vibration.

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