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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 72 Documents
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Search results for , issue "Vol 13, No 1: February 2024" : 72 Documents clear
Machine learning prediction for academic misconduct prediction: an analysis of binary classification metrics Masrom, Suraya; Abdul Samad, Nor Hafiza; Septiyanti, Ratna; Roslan, Nurshafinas; Rahman, Rahayu Abdul
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5629

Abstract

Academic misconduct is unethical behavior in academic work. To sustain integrity culture and mitigating unethical conducts among higher education institutions community, the academic misconduct detection must be done at an earlier stage. Thus, this study attempted to provide a new empirical contribution with the analysis of binary classification performances metrics to describe the ability of machine learning in predicting academic misconduct. Four machine learning algorithms have been used namely generalized linear model (GLM), logistic regression (LR), decision tree (DT), and random forest (RF). Beside performances comparison, this paper presents the analysis of academic misconduct factors that were constructed based on demography and fraud triangle theory (FTT). The findings showed that all the four machine learning algorithms have obtained good ability in the prediction models with the accuracy at above 80% and below 20% of the classification errors. Rationalization from the FTT attributes has shown as the most important factor in GLM, LR, and DT. In RF, opportunity of FTT attributes have become the most important. Compared to FTT attributes, demography attributes were not providing much benefits to all the machine learning models but remain applicable at very low weight correlations.
An Adam based CNN and LSTM approach for sign language recognition in real time for deaf people Kumer Paul, Subrata; Ala Walid, Md. Abul; Rani Paul, Rakhi; Uddin, Md. Jamal; Rana, Md. Sohel; Kumar Devnath, Maloy; Rahman Dipu, Ishaat; Haque, Md. Momenul
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6059

Abstract

Hand gestures and sign language are crucial modes of communication for deaf individuals. Since most people can't understand sign language, it's hard for a mute and an average person to talk to each other. Because of technological progress, computer vision and deep learning can now be used to count. This paper shows two ways to use deep knowledge to recognize sign language. These methods help regular people understand sign language and improve their communication. Based on American sign language (ASL), two separate datasets have been constructed; the first has 26 signs, and the other contains three significant symbols with the crucial sequence of frames or videos for regular communication. This study looks at three different models: the improved ResNet-based convolutional neural network (CNN), the long short-term memory (LSTM), and the gated recurrent unit (GRU). The first dataset is used to fit and assess the CNN model. With the adaptive moment estimation (Adam) optimizer, CNN obtains an accuracy of 89.07%. In contrast, the second dataset is given to LSTM and GRU and a comparison has been conducted. LSTM does better than GRU in all classes. LSTM has a 94.3% accuracy, while GRU only manages 79.3%. Our preliminary models' real-time performance is also highlighted.
Triangular fuzzy number for similarity measurement of Y chromosome DNA profile Dewi, Meira Parma; Arymurthy, Aniati Murni; Setiawan, Suryana; Soedarsono, Nurtami
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5304

Abstract

This study measures the similarity of the short tandem repeat (STR) profile of human DNA. The similarity measurement had been done to the STR value of the allele loci in DNA profile between the query’s DNA to the reference’s DNA profile. The measurements were conducted on 27 DNA profile loci including the Y chromosome loci (YSTR). The YSTR loci were used as the main comparison of similarity measurements to determine the biological kinship relationship between the query DNA profile and the alleged male biological family. To measure the similarity of two STR values that have shifted due to several factors in the DNA source extraction process, a fuzzy similarity measure was used. The STR values of the DNA profile loci are described as triangular fuzzy numbers. Similarity value of the STR is the intersection of two isosecle that been compared. To conclude that the query has a biological relationship with the male reference, the similarity of the YSTR locus is equal or more than 0.75 and the similarity value of the other 24 DNA profile loci is greater or equal to 0.5. From the trial that have been done, 90% give the right results.
Design of a novel control hysteresis algorithm for photovoltaic systems for harmonic compensation Obulesu, Dakka; Swarupa, Malladi Lakshmi
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6038

Abstract

Solar photovoltaic (PV) system design and integration with an existing AC grid is growing very fast in recent years and used by many of them as they are pollution-free, structure is limited and maintenance free. From the factors considering, the performance of PV system depends upon the inverter output voltage tested for linear, non-linear, with harmonic, and without harmonic loads. Generated due to the nonlinear loads. Better inverter control techniques are developed to maintain grid power quality. This article discusses the analysis and comparison of pulse width modulation (PWM) converters with unique and state of the art nonlinear control schemes and various modulation approaches. The primary objective of this research is controlling an active filtering hysteresis PWM converter with no sensors. A simple structure with hysteresis current control method total harmonic distortion (THD) is lower when compared with the sinusoidal pulse width modulation (SPWM) method. The said claims are supported by employing computer simulations using MATLAB/Simulink and different control approaches, such as proportional plus integral and artificial neural network controllers.
The scheduling techniques in the Hadoop and Spark of smart cities environment: a systematic review Mirza, Nada Massed; Ali, Adnan; Ishak, Mohamad Khairi
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5841

Abstract

Processing extensive and diverse data in real-time is a significant challenge in the context of smart cities. Timely access to information and efficient analytics is essential for smart city services to make data-driven decisions and enhance urban living. Scheduling algorithms play a crucial role in ensuring the prompt delivery of services and efficient task completion. This paper explores various scheduling techniques, including static, dynamic, and hybrid schedulers, and compares their objectives and performance. Additionally, the study examines two prominent data processing frameworks, Hadoop and Spark, and compares their capabilities in handling big data in smart cities. With its ability to process large amounts of data quickly and efficiently, Spark has shown superiority over Hadoop in real-time data processing and performance optimization. The paper concludes by highlighting the strengths and limitations of each framework. It discusses the need for further research in optimizing scheduling techniques and exploring hybrid artificial intelligence scheduling for Spark. Overall, the findings contribute to a better understanding of data processing in real-time and provide insights for researchers and practitioners in smart cities.
Bayesian probabilistic modeling in robosoccer environment for robot path planning Steffi, Diana; Mehta, Shilpa; Venkatesh, Kanyakumari Ayyadurai
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6080

Abstract

The main goal of a route planning approach is to find a trajectory that safely transports the robot from one site to the next. Furthermore, it should provide an energy-efficient path so the computer can calculate it rapidly. This study develops a path-planning system for robots to approach the ball without collision. The Bayesian optimization algorithm (BOA) is used to identify the shortest path between the robot and the ball. BOA employs a probabilistic model to seek the optimum of an uncertain objective function efficiently. The performance of the BOA-based path planning system is compared to other optimization algorithms such as genetic algorithm, ant colony optimization, and firefly algorithm. BOA’s acquisition functions such as expected improvement, probability of improvement (PI), and upper confidence bound, are investigated. The exact locations of the robots and the ball are fed into optimization problems to discover the optimum path. The results reveal that the BOA system outperforms other systems in terms of computational time for planning the optimum path in dynamic situations and BOA-PI is the fastest algorithm.
A hybrid facial features extraction-based classification framework for typhlotic people Chopparapu, SaiTeja; Seventline Joseph, Beatrice
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5628

Abstract

Facial features play a vital role in the real-time cloud-based applications. Since, most of the conventional models are difficult to detect heterogeneous facial features due to high computational memory and time for the internet of things (IoT) based video surveillance mechanisms. Video based facial features identification and extraction include a large number of candidates features which are difficult to detect the contextual similarity of the facial key points due to noise and computational memory. In order to resolve these issues, a hybrid multiple features extraction measures are implemented on the real-time video dataset to extract key points using the cloud-based classifier. In this work, a hybrid classifier is used to classify the key facial points in the cloud computing environment. Experimental results show that the proposed hybrid multiple feature extraction-based frameworks have better computational efficiency in terms of error rate, recall, precision, and accuracy than the conventional models.
Automated lighting design in the classroom Widya Pratama, I Putu Eka; Soehartanto, Totok; Ari Wibowo, Daniel Dany; Tjandra, Nabiilah Aziizah; Nizar, Mochammad
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6384

Abstract

Lamp automation is utilized to adjust the lighting in the classroom by adjusting to the level of sunlight intensity outside the classroom. The light intensity will be adjusted to the utilization of lights as indicated by the need to enlighten a room. Therefore, this research aims to depict the design of a light intensity measurement system using a BH1750 sensor that will be carried out to measure the intensity of sunlight in units of light (lux). The signal from the sensor will be transmitted to a mini pc which functions to process measurement data and display it on the graphical user interface (GUI). As a result, the sensor will instruct the dimmer as an actuator to control the classroom lights according to the lighting from the sun. This system is already installed in the classroom and can save energy around 35 kWh a year.
Modeling two loops RLC circuit AC power source using symbolic arithmetic differential equations Hassan, Inaam Rikan; Abed, Ghuson S.; H. Sabry, Ahmad
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5321

Abstract

As oscillator applications, resistance-inductor-capacitor (RLC) circuits are employed in a diversity of settings. A low-pass, band-stop, band-pass, or high-pass filters can all be designed using an RLC circuit. A two-loop RLC circuit could not be represented mathematically in prior studies. Laplace transform is one type of integral transformation, which is able to resolve both second order non-uniform and uniform linear differential equations. This work solves the differential equations (DEs) of a two loops RLC circuit of an alternating voltage source by using two alternative approaches, Laplace transform (LT) and deep learning convolutional neural network (DLCNN). Initially, two DE have been declared. Next, Laplace transform is computed to solve these equations with symbolic variables for the first loop current and capacitor charge. Finally, we substitute the numerical values of the circuit elements for the symbolic variables. The charge and current initially decline exponentially. On the other hand, they oscillate over a long period of time. The capacitor charge and current initially decline exponentially and oscillate over a long period of time. The qualities of the result can be examined with a symbolic result, which is not possible with a numeric result.
Impact of business intelligence on incident management in the control center of a security company Daza, Alfredo; De la Paz Ttito, Elmer Marcelino; Alarcón Cajas, Yohan Roy
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5819

Abstract

The main objective of this article is to implement business intelligence (BI) technology to improve incident management in the control center of a security company; making use of the Ralph Kimball method for the development of the data mart, contemplating the phases: planning of projects, definition of requirements, design of technological architecture, performing a dimensional modeling a star with 9 dimensions and 1 table made, also used the structured query language (MySQL) database, extract, transform, and load (ETL). Development the pentaho data integration, which allowed the migration, transformation and cleaning of the data from the online transaction processing (OLTP) and online analytical processing (OLAP) database, obtaining as a result that the BI managed to increase the level of the service by 28.42% and reducing the frequency index by 25%.

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