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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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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%.
Technology integration through acceptance of e-learning among preservice teachers Ika Ratih Sulistiani; Punaji Setyosari; Cholis Sa'dijah; Henry Praherdhiono
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.pp1821-1828

Abstract

The rapid evolution of online learning necessitates the agility of future educators. Future teachers’ technological attitudes and abilities will be affected by the explication of the role of technology in learning in teacher education. Therefore, preservice teachers must be prepared to implement technology in their future classrooms. This study investigated the effect of technological pedagogical content knowledge (TPACK) competency on the technology acceptance model (TAM)-measured intention to use from two perspectives: self-confidence and self-regulation. Thus, six hypotheses are proposed to investigate the relationship between TPACK proficiency and the intent to use technology. The population of 224 preservice teachers at one Indonesian tertiary institution was analyzed using structural equation modeling (SEM) to confirm five of the six hypotheses. It is stated that attitudes toward TPACK competence positively correlate with self-regulation, self-efficacy, and intent to use technology. In addition, the derived model can account for approximately 53.8% of the intention to integrate technology in the classrooms of preservice teachers.
A 2 shape slot microstrip patch antenna for global positioning system satellite communication applications Md Rubel Basar; Md Al-Amin; Effat Mirza; Partha Pratim Debnath; Md Rabiul Awal
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.pp1392-1399

Abstract

In this paper, a compact two-shape slot microstrip antenna is proposed for global positioning system (GPS) satellite application. The proposed antenna is designed with FR 4 substrate with a height of 1.6 mm, a dielectric constant εr of 4.3 within a compact size 56 x 56 mm2 area. The design parameters are optimized to achieve good performance. At the optimum setting of design parameters, this antenna shows good characteristics to cover L1 band 1,575.42 MHz, ± 12 MHz frequency which is used for GPS satellites. At the 1,572 MHz resonance frequency, this antenna achieves a minimum return loss of -30 dB covering a frequency bandwidth of 50 MHz (1,550 MHz – 1,600 MHz) at -10 dB reference level which ensures 100% bandwidth coverage of the L1 band. Besides, the proposed antenna achieved a maximum gain of 8.3 dBi and a beam width of 1010 at -3 dB point. In terms of other performance, such as voltage standing wave ratio (VSWR), directivity, radiation pattern, the proposed antenna shows good performance for the application of GPS satellites.
Development of dam controller technology water level and alert system using Arduino UNO Mazratul Firdaus Mohd Zin; Farid Zuhri Kamal; Syila Izawana Ismail; Ku Siti Syahidah Ku Mohd Noh; Abdul Hafiz Kassim
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.pp1342-1349

Abstract

Recently, various water level detection monitoring systems integrations were implemented to various places such as rivers and reservoirs to avoid floods. Thus, it is determined to construct a project primarily focused on water level management, named dam controller technology (DCOTech). DCOTech is a system that controls the amount of water in a reservoir using a microcontroller ATMEGA328p and several functions to prevent flash floods. Water sustainability requires proper monitoring via sensors and a controller. Moreover, a buzzer is used in DCOTech to give a warning signal to the people around and the residents. The water level sensor was constructed with a metal plate attached at both the bottom and top edges of the reservoir. The results obtained met expectations; whenever the sensor detects the water level is low, the green light emitting diode (LED) is turned on; otherwise, when the sensor detects the water level is high, the drain valve is opened, and simultaneously, turned on the buzzer to alert the surrounding. The goal of this project is to integrate a control system into an autonomous water level controller. This study aims to provide a solution to unexpected floods and to notify inhabitants when the water level is dangerously high.
GPS-based fall detection system for old and specially-abled people Mazin N. Farhan; Mohammed G. Ayoub; Ali Rakan Al-Jader
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.pp1545-1550

Abstract

Falls are a serious public health concern for older people across the world. Modern telemedicine now depends heavily on remote monitoring of older patients and the ability to spot threats to human health. If a fall is not assisted in time, it can significantly reduce an older person's mobility, independence, and his/her quality of life. Older people who experience post-traumatic problems or mortality frequently do so because of falls. As a result, preventing falls consequences or providing essential help on time may depend on the early identification of falls. In this article, we propose an internet of things (IoT) based system that makes use of low-power wireless sensor networks, smart devices and cloud computing to detect falls and track positions for older and specially-abled people. The tracking is done by sending links of positions from the proposed system every 15 seconds to a specified google drive. On the other hand, an alert message will be delivered to the caregiver whenever a fall is happened. Thus, a MPU-6050 sensor and NEO-6M global positioning system (GPS) module are used with ESP32 microcontroller for the aforementioned purposes. A pilot study with several protocols was carried out to validate the cost-effective proposed system and achieved good results.
A unified power flow controller-based robust damping controller considering time delay in electrical power systems Mahmoud Zadehbbagheri; Rahim Ildarabadi; Tole Sutikno
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.pp1295-1310

Abstract

Nowadays, different power systems are connected to each other due to technical and economic reasons and form a complex developed network. Such networks measure and send system data to decision-making centers for control and protection. This information transmission is accompanied by a delay and causes the performance of the system’s damping controllers to be affected. To improve power system dynamic stability, the supplementary controller in flexible alternating current transmission system (FACTS) devices is designed to account for information transmission delays. In these methods, to increase the controllability and observability of the inter-area mode, the remote signals extracted from the wide area measurement system (WAMS) are used as the input of the modulator. WAMS systems’ time delay reduces power system stability and even causes instability, so it’s crucial to find the maximum delay margin that ensures stability. Therefore, it is necessary to carefully study the role of FACTS tools in stabilizing the power system and damping interregional fluctuations, considering the delay. This research shows that supplementary controller design can dampen frequency and load angle fluctuations in multi-zone power systems despite information transmission delays. The method works in power system analysis toolbox (PSAT) simulation and MATLAB programming.
Selection of efficient machine learning algorithm on Bot-IoT dataset for intrusion detection in internet of things networks Imane Kerrakchou; Adil Abou El Hassan; Sara Chadli; Mohamed Emharraf; Mohammed Saber
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.pp1784-1793

Abstract

With the growth of internet of things (IoT) systems, they have become the target of malicious third parties. In order to counter this issue, realistic investigation and protection countermeasures must be evolved. These countermeasures comprise network forensics and network intrusion detection systems. To this end, a well-organized and representative data set is a crucial element in training and validating the system's credibility. In spite of the existence of multiple networks, there is usually little information provided about the botnet scenarios used. This article provides the Bot-IoT dataset that embeds traces of both legitimate and simulated IoT networks as well as several types of the attacks. It provides also a realistic test environment to address the drawbacks of existing datasets, namely capturing complete network information, precise labeling, and a variety of recent and complex attacks. Finally, this work evaluates the confidence of the Bot-IoT dataset by utilizing a variety of machine learning and statistical methods. This work will provide a foundation to enable botnet identification on IoT-specific networks.
Remotely controlled water channel system for laboratory education utilizing internet of things and SCADA technologies Hasan Allawi abd Al-asadi; Amer Abdulmahdi Jabbar Chlaihawi
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.pp1266-1273

Abstract

This paper was discussed the hardware components of a remotely controlled water channel management system for laboratory education utilizing the internet of things(IoT) and supervisory control and data acquisition systems (SCADAs). The system consists of various sensors and devices included the air pressure sensor (mpx5010dp), water pressure sensor, ultrasonic distance sensor (JSNSR04T) and solenoid water valve plastic. The air pressure sensor (mpx5010dp) was used to detect the water level in the manometer of the pitot tube, a water pressure sensor for detecting the pressure of water, an ultrasonic distance sensor (JSN-SR04T) for detecting the level of water in the channel, a solenoid water valve plastic with relay for protecting the motor when the water level in the tank reduces to 5 cm. These components work together to monitor and control the water channel system remotely, providing users with a more scalable, flexible, and accessible solution for laboratory education. Also, a web page was developed with an IoT and SCADA system for remote control and management of a water channel system that was used for teaching hydraulic and hydrological concepts to students.
Image classification using machine learning Debani Prasad Mishra; Sanhita Mishra; Smrutisikha Jena; 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.pp1551-1558

Abstract

The objective of this paper is to implement different tools available in machine learning/artificial intelligence to classify faces and identify different features, highlights, and correlations or similarities between different celebrity faces which can apply in everyday security purposes to identity virtually if the authorized personnel is using certain access or not. The material present in this paper is a literature review of a machine learning model developed by the students. This is a classical problem of machine learning executed using a support vector machine. Images are separated based on sub-images. Each sub-image has been classified into a responsive class by an artificial neural network. The website then fetches the data from the back end and classifies the image into the corresponding personal.
A machine learning model for predicting recovery rates of COVID-19 patients Amany Abdo; Kholoud Mohamed Elzalama; Ahmed Elsayed Yakoub
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.pp1656-1664

Abstract

During disease epidemics, any trial to improve healthcare systems entails preserving lives. Therefore, predicting which patients are at high risk becomes critical and challenging when confronted with a novel virus. The recent COVID-19 changed many people’s perspectives on how to approach diseases. According to the lack of medical resources, it is important to identify the patients who need instant medical care. This research proposes a machine learning model to identify high-risk patients that require specific medical attention. Specifically, extreme gradient boosting (XGboost), random forest (RF), and logistic regression (LR) are used in the ensemble method to classify COVID-19 patients at high risk. The dataset consists of 361 medical records for severe COVID-19 patients which have included 195 survivors. The most correlated features (neutrophils (%), hypersensitive c-reactive protein, lactate dehydrogenase, age, procalcitonin, and neutrophils count) are selected to be used in classification. Different machine learning classifiers are applied to the mentioned dataset to find out the optimum classifiers to be used in the ensemble method. 98% is the most optimal accuracy achieved with the proposed model.

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