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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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Exploring the potential of skeleton and machine learning in classroom cheating detection Nha Tran; Minh Nguyen; Tri Le; Tuong Nguyen; Tinh Nguyen; Tuan Nguyen
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.pp1533-1544

Abstract

In recent years, there have been certain drawbacks in the behavior of students in school cultural activities, especially the frequent occurrence of cheating in exams. Although many manual cheating detection methods have been applied, cheating behaviors have become increasingly sophisticated and difficult to detect. This makes manual cheating detection methods unable to completely prevent cheating in exams. Therefore, applying advanced technologies to automatically detect cheating cases becomes necessary to help supervisors better control the exam process. In this study, we propose a model architecture to monitor students’ cheating activities during exams. Firstly, we apply YOLO - Pose to detect skeleton keypoints, which are then passed through machine learning models such as support vector machine (SVM), decision tree (DT) classifier, random forest (RF), extreme gradient bo osting (XGBoost), and propose the Ac Long short - term memory (LSTM) to detect cheating behavior. The experimental results show the feasibility of the model on various metrics. The model is tested on a dataset of cheating behaviors in the classroom, which is designed to simulate paper - based exams. Moreover, the results showed that the proposed method can detect cheating behavior in a short amount of time and can be deployed for real - world applications.
An efficient and low cost realization of LoRa based real-time forest protection system Gobinda Prasad Acharya; Lavanya Poluboyina; Jayaprakasan Veeragamoorthi; Chattopadhyay Joydeb
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1452-1462

Abstract

The forest is a natural habitat for a variety of fauna and flora, and helps to maintain the ecosystem equilibrium. However, wildfire incidents and deforestation lead to forest degradation. Moreover, most of the existing methods, to preserve the forest resources, are ineffective due to their large establishment cost, more power consumption, and poor coverage. This paper brings out a sustainable solution by developing a forest protection system (FPS) that uses internet of things (IoT) technology together with long range (LoRa) communication. The work focuses on the development of an IoT framework for the detection of any intrusion into the forest as well as the detection of fire incidents in the vicinity of the equipment. Powering the equipment through solar energy makes the system cost-effective. The system is examined in terms of acquisition of data from sensor nodes pertaining to forest protection, relaying the same to the cloud using LoRa wide area network (LoRaWAN) technology and analyzing using cloud based visualization tools. The developed system has been deployed at Eturnagaram Wildlife Sanctuary, Mulugu district, Telangana, India for validation in the forest environment. The obtained results have shown that the system has an accuracy of 97.14% for intrusion detection and 100% for fire detection.
Development of biomechanical behaviour of magnesium alloys for biomedical context Hamza Abu Owida; Feras Alnaimat; Bassam Al-Naami; Jamal Al-Nabulsi; Nidal Turab
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.pp98-108

Abstract

Magnesium alloys, which belong to the category of biodegradable metals, have a significant amount of potential to be utilized as implant materials, and as a result, they draw a lot of attention. This article is a review that summarizes the mechanical properties of magnesium alloys that are used in medical applications. This article illustrates the mechanical behaviors of magnesium alloys that are used in biomedical applications as well as the ways that may be used to improve the mechanical characteristics of biodegradable magnesium alloys. In conclusion, the difficulties that will need to be overcome in the creation of biodegradable magnesium alloys are discussed.
Grid impact analysis on wind power plant interconnection in strengthening electricity systems Senen, Adri; Kurniawan, Arif; Dini, Hasna Satya; Anggaini, Dwi
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.pp32-41

Abstract

The Timor system is one of the large systems in the East Nusa Tenggara region. Based on the general plan for electricity supply for 2021-2030, there is a plan to interconnect a 2x11 MW wind power plant. The addition of wind power plants will pose a considerable threat to the system due to the intermittency nature of renewable energy plants. Therefore, a comprehensive grid impact study is needed to convince network managers that adding wind farms will not cause disruptions to the system either locally or in general and is expected to strengthen the electricity system. The power flow simulation results, installing a 2x11 MW wind farm on the Timor system can improve voltage quality and reduce losses on both 70 and 150 kV systems. For transient stability, the frequency value on the Timor system still meets the grid code requirements. In addition, the simulation results of the intermittency impact of the wind power plant output show that the Timor system is still in a stable condition. The stability of the rotor angle of the existing power plant when the transient stability simulation is carried out shows that it is still in a balanced condition.
The influences of LaVO4:Eu3+,Cr3+ red phosphors on white light-emitting diode applications Nguyen Hung Khanh; Nguyen Le Thai; Thuc Minh Bui; Huu Phuc Dang; Huynh Thanh Thien
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.pp787-794

Abstract

In this present paper, the LaVO4:Eu3+,Cr3+ (LV:Eu,Cr) red-orange phosphorsare introduced in WLED fabrication. The LV:Eu,Cr phosphor is synthesized with high-temperature solid-phase reaction. The luminescence properties of the phosphor are monitored with ultraviolet-visible and near-infrared (UV-vis-NIR) measurements. The heat generating in the phosphor sample is also investigated with different power densities of the 980 nm laser. The phosphor exhibits absorption bands at 254 nm and 316 nm, suitable for UV light-emitting diodes (UV LED) applications. The Cr3+ concentrations have noticeable effects on the luminescence of the phosphor. The increasing Cr3+ dosage initiates reduction in Eu3+ emission intensity and luminescence lifetime average. The heat-activated amount in the phosphor is also significant with the higher concentration of Cr3+ ions. When using the LV:Eu,Cr phosphor in white light-emitting model (WLED), the dominant red-orange emission centered at 595 nm is observed, in addition to the blue peak at 453 nm. The luminosity, color rendition, and color uniformity of the WLED light are also discussed. The findings indicate that the phosphor can be combined with other high-efficiency blue and green phosphors to obtain the improved color rendition and luminous performances and used in heat-creating optical applications.
Implementation of a long range-based monitoring system for environmental remote safety schemes Mahmood Jalal Ahmad Alsammarraie; Adnan Hussein Ali; Aqeel A. Al-Hilali; Hind Q. Mohammad Monir; Mohannad Sameer Jabbar; Haitham Bashar Qasim
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.pp1462-1475

Abstract

The effects of poor air quality on people are profound. Air pollution and health issues are linked to millions of deaths. The World Health Organization (WHO)and the environmental protection agency (EPA)have produced several regulations and guidelines to control and enhanceair quality. Several authors have recently suggested low-cost tools for instantly assessing air quality. the abundance of inexpensive ways to assess air quality and collect sensor data. The design and employment of remote safety monitoring for humidity, temperature, and carbon monoxide has been merged into a single tool in this work. The results findings can be observed on the actual measured instrument’s organic light-emitting diode (OLED)show and the liquid crystal display (LCD)remote monitoring system. This device uses ESP32 microcontroller with long range (LoRa)wireless communication technology.The DHT22 sensor is used to detect humidity and temperature, while the MQ-7 sensor is used to monitor carbon monoxide levels. Testing measurements are calibrated using instruments that meet industrial standards (AS8700A CO meter and 4,000 NV weather tracker) used in environmental station. If the abnormalities in the recorded parameters are detected, then alert will be triggered. This instrument can help patients and workers to check their surroundings and provide remote monitoring to health service providers.
A novel ensemble approach for Twitter sentiment classification with ML and LSTM algorithms for real-time tweets analysis Thotakura Venkata Sai Krishna; Tummalapalli Siva Rama Krishna; Srinivas Kalime; Chinta Venkata Murali Krishna; Sadineni Neelima; Raja Rao PBV
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1904-1914

Abstract

Social media sentiment classification was an essential consideration in natural language processing (NLP) for evaluating normal people’s perspectives on a given topic. With Twitter’s massive rise in popularity in recent years, the capacity to extract information about public sentiment from tweets became a major focus. This paper not only analyzed public sentiment through data from Twitter but introduced a novel ensemble approach in the methods employed for Twitter sentiment classification. Real-time tweets on various topics, including “covid,” “crime,” “spam,” “flipkart,” “migraine,” and “airlines,” were extracted and thoroughly examined to gain insight into public opinions. Leveraging the Twitter API for real-time tweet extraction, natural language processing techniques were applied to clean the tweet data. Subsequently, we applied several machine learning (ML) algorithms Naïve Bayes, decision tree (DT), random forest (RF), logistic regression (LGR), and deep learning (DL) algorithms recurrent neural network (RNN), LSTM, and GRU individually. Later, we proposed a novel ensemble of ML and DL algorithms for sentiment classification, with a novel emphasis on ensemble techniques and enhanced the accuracy with a significance compared to individual ML or DL model applied. The experimental results demonstrated that our novel ensemble approach achieved high accuracy when compared to existing work.
AGV maneuverability simulation and design based on pure pursuit algorithm with obstacle avoidance Singhanart Ketsayom; Dechrit Maneetham; Padma Nyoman Crisnapati
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.pp835-847

Abstract

This paper discusses the simulation of an automated guided vehicle (AGV) with the differential-drive mobile robot (DDMR) concept. Using this wheel configuration, the AGV can maneuver in tight workspaces. However, controlling a self-driving AGV with obstacle avoidance is not easy. Therefore, this paper proposes a control system to drive an AGV with several process stages. First, a kinematic model is formulated to represent the AGV with the concept of two wheels that can be controlled differentially. In the second stage, the pure pursuit control method is applied to the model so that the AGV can follow the waypoint coordinates determined and combine them with obstacle avoidance. Finally, the effectiveness of the control system was verified using simulation. The look-ahead parameter with a value of 0.2 meters shows optimal results so that pure pursuit control can reach all waypoint coordinates. Based on this simulation, the AGV prototype was then designed, assembled, and equipped with an internet of things-based obstacle avoidance system. While the simulation proves promising, the anticipated challenges identified in the AGV field test, such as GPS inaccuracies and signal obstructions, underscore the need for ongoing improvements in real-world applications.
Advancing cryptography: a novel hybrid cipher design merging Feistel and SPN structures Venkataramanna, Ramya Kothur; Hosur Sriram, Manjunatha Reddy; Reddy, Bharathi Chowda
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp751-760

Abstract

In the dynamic field of cryptography, lightweight ciphers play a pivotal role in overcoming resource constraints in modern applications. This paper introduces a lightweight cryptographic algorithm by seamlessly merging the proven characteristics of the Feistel cipher CLEFIA with the advanced substitution-permutation network (SPN) framework of RECTANGLE for key generation. The algorithm incorporates a specially optimized feather S-box, balancing efficiency and security in both CLEFIA and RECTANGLE components. The RECTANGLE key generation, vital for the proposed lightweight technique, enhances overall cryptographic security and efficiency. Meticulous consideration of resource limitations maintains the algorithm's lightweight nature, making it well-suited for applications with restricted computational resources. To validate the efficacy of the lightweight algorithm, extensive evaluation on encrypted data is conducted using National Institute of Standards and Technology (NIST) tools, known for assessing cryptographic algorithm quality. Results reveal a high degree of randomness, indicative of robust resistance against cryptographic attacks. This manuscript provides a comprehensive examination of the lightweight algorithm, emphasizing key attributes, security enhancements, and successful integration of the optimized feather S-box. Rigorous testing, particularly NIST tool-based randomness analysis, offers empirical evidence of the algorithm's resilience against attacks, establishing its suitability for secure data encryption in resource-limited environments. 
Optimization of the algorithms use ensemble and synthetic minority oversampling technique for air quality classification Aziz Jihadian Barid; Hadiyanto Hadiyanto; Adi Wibowo
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.pp1632-1640

Abstract

Rapid economic development, industrialization, and urbanization in Indonesia have caused a large increase in air pollution with negative impacts on the environment and public health. The aim of this research is to use machine learning techniques to categorize air quality and generate an air quality index (AQI) using a dataset that includes six prevalent air pollutants. Next steps are preprocessing and data extraction, K-nearest neighbors (KNN) classification, support vector machine (SVM), and random forest (RF) models are implemented. Furthermore, synthetic minority oversampling technique (SMOTE) is incorporated into the ensemble learning process to improve the results. This research uses K-fold cross validation for improve classification accuracy and reduce overfitting. Research findings show that the application of SMOTE brings a significant increase in model accuracy, effectively solving the problem of imbalanced data sets. These insights provide direction for effective air quality monitoring systems and informed decision making in air pollution management.

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