cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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 65 Documents
 1   2   3   4   5 
Search results for , issue "Vol 12, No 3: June 2023" : 65 Documents clear
Machine learning with task-technology fit theory factors for predicting students’ adoption in video-based learning Suraya Masrom; Rahayu Abdul Rahman; Norhayati Baharun; Syed Redzwan Sayed Rohani; Abdullah Sani Abd Rahman
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.5037

Abstract

Nowadays, various innovative educational and instructional tools have been created to deliver learning material including video content. One of the important issues with video-based learning is to devise effective teaching strategies to ensure higher level of learning can be achieved by the students. Getting insight and predicting the students’ video-based learning adoption will help the educators. Thus, this study aims to examine the potential of using machine learning prediction models on video-based learning adoption in higher education institutions. Five machine learning algorithms were used to be empirically compared namely generalized linear model (GLM), random forest (RF), decision tree (DT), gradient boosted tree (GBT), and support vector machine (SVM). The performance of each machine learning algorithm in predicting the students’ learning adoption with video-based learning has been observed based on the attributes of task-technology fit theory. The findings indicated that the task-technology fit is useful in helping the machine learning algorithm to achieve high accuracy in the prediction of video-based learning adoption. The GBT is the best outperforming algorithm, followed with RF and SVM. This paper presents a fundamental research framework useful for helping educators and researchers to enhance student interest and retention on video-based learning.
Evaluation of feature scaling for improving the performance of supervised learning methods Tsehay Admassu Assegie; Vadivel Elanangai; Josephin Shermila Paulraj; Mani Velmurugan; Daya Florance Devesan
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.5170

Abstract

This article evaluates the performance of the support vector machine (SVM), decision tree (DT), and random forest (RF) on the dataset that contains the medical records of 299 patients with heart failure (HF) collected at the Faisalabad Institute of Cardiology and the Allied hospital in Pakistan. The dataset contains 13 descriptive features of physical, clinical, and lifestyle information. The study compared the performance of three classification algorithms employing pre-processing techniques such as min-max scaling, and principal component analysis (PCA). The simulation result shows that the performance of the DT, and RF decreased with dimensionality reduction while the SVM improved with dimensionality reduction. The SVM achieved 84.44%. Thus, feature scaling improves the performance of the SVM. The RF performs at 82.22%, the DT at 81.11%, and the SVM shows an improvement of 1.64% with scaled features, compared to the original dataset.
An accurate traffic flow prediction using long-short term memory and gated recurrent unit networks Mohamed S. Sawah; Shereen Aly Taie; Mohamed Hasan Ibrahim; Shereen A. Hussein
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.5080

Abstract

Congestion on roadways is an issue in many cities, especially at peak times, which causes air and noise pollution and cause pressure on citizens. So, the implementation of intelligent transportation systems (ITSs) is a very important part of smart cities. As a result, the importance of making accurate short-term predictions of traffic flow has significantly increased in recent years. However, the current methods for predicting short-term traffic flow are incapable of effectively capturing the complex non-linearity of traffic flow that affects prediction accuracy. To overcome this problem, this study introduces two novel models. The first model uses two long-short term memory (LSTM) units that can extract the traffic flow temporal features followed by four dense layers to perform the traffic flow prediction. The second model uses two gated recurrent unit (GRU) units that can extract the traffic flow temporal features followed by three dense layers to perform the traffic flow prediction. The two proposed models give promising results on performance measurement system (PEMS), traffic and congestions (TRANCOS) dataset that is firstly used as metadata. So, the two models can do this in specific cases and are able to suddenly capture trend changes.
Adaptive position control of DC motor for brush-based photovoltaic cleaning system automation Sorfina, Ummi; Islam, Syed Zahurul; Ching, Kok Boon; Soomro, Dur Muhammad; Yahaya, Jabbar Al-Fatta
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4926

Abstract

In this paper, we have developed an automatic brush-based PV cleaning system to control and synchronize the 3 motors together with a smooth periodic of cleaning while moving it horizontally over the PV surface. The mechanical design involved installing linear guides at the top and bottom of the rail to support the aluminium plate that holds the carrier motors and rotating brush. Two different movements of translational and rotational motion of the motors are managed by an algorithm programmed in Arduino Mega. In investigating the performance of motor parameters and dust removal rate, we conducted an experiment by spreading dry sand over the PV surface. Results showed that the torque of the cleaning brush motor increases with the increase in load. The obtained torque of the carrier motor was found to be 9.167 Nm ( stall torque, 9.8 Nm) with a full load of 18 brushes. The torque is inversely proportional to the speed but directly proportional to power. The required power to move the 2.93 kg of cleaning system was 19.20 W with 3.015 Nm of torque. The system achieved 86.8% of the dust removal rate from the four cycles of cleaning operations.
The impacts of green LaBSiO5: Tb3+, Ce3+ phosphor on lumen output of white LEDs Ha Thanh Tung; Huu Phuc Dang; Phung Ton That
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4772

Abstract

The traditional solid-state technique was used to create LaBSiO5 phosphors doped with Ce3+ and Tb3+ at 1,100 °C. These phosphors' phase purity and luminous characteristics are looked at. Under ultraviolet (UV) light stimulation, LaBSiO5: Tb3+ phosphors emit bright green light, whereas LaBSiO5 samples incorporated with Ce3+ emit blue-violet light. With UV ray stimulation, LaBSiO5 samples incorporated with Ce3+ as well as Tb3+ emit blue-violet as well as green illumination. The 5d-4f shift for Ce3+ is responsible for the blue-violet radiation, while the 5D4→7F5 transition of Tb3+ is responsible for the green radiation. The mechanism for power conversion between Ce3+ and Tb3+ was examined since there is a spectral overlap among the stimulation line for Tb3+ and the emitting line for Ce3+.
Simulation of SDN in mininet and detection of DDoS attack using machine learning P. Karthika; Karmel Arockiasamy
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.5232

Abstract

Most contemporary businesses are embracing software defined networking (SDN), a developing architecture that enables an aerial-like perspective of the entire network. SDN operates by virtualizing the network and provides advantages including improved performance, visibility, speed, and scalability. SDN attempts to divide the network control plane from the forwarding plane. The control plane, which includes one or more controllers and incorporates complete intelligence, is thought of as the brain of the SDN. However, SDN has challenges with controller vulnerability, flexibility, and hardware security. But distributed denial of service (DDoS) assaults constitutes a serious threat to the SDN. Transmission control protocol-synchronized (TCP-SYN) floods, a common cyberattack that can harm SDNs, can deplete network resources by opening an excessive number of illegitimate TCP connections. In this research, we provide an OpenFlow port statistic-based architecture for machine learning (ML) enabled TCP-SYN flood detection. This research showed that ML models like support vector machine (SVM), Navie Bayes, and multi-layered perceptron can distinguish between regular traffic and SYN flood traffic and can mitigate the impacts of the attacking node on the network. Results showed that the multilayered perceptron can classify the traffic with highest accuracy of 99.75% for the simulation dataset.
Noise estimation using an artificial neural network in the urban area of Jaen, Cajamarca Wendy Díaz; Anali Tarrillo; Candy Ocaña; Lenin Quiñones
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4633

Abstract

Jaen is a city in constant urban growth which generates an increase in vehicular traffic and active noise pollution. The research presents the development of an artificial neural network (ANN) to estimate the noise produced by vehicular traffic in the urban area of the city. Consequently, information was collected from two investigations coded as T1 and T2, for which a matrix of 10 variables was elaborated with 210 and 273 data respectively. Random random sampling was performed to divide the data matrix into 80% (training) and 20% (validation). Weka software and the multi-layer perceptron (MLP) training algorithm were used to model the ANN. An ANN for T1 with 6-19-1 architecture and an ANN for T2 with 6-15-1 architecture were obtained. The performance of the ANNs was evaluated using the correlation coefficient (R), coefficient of determination (R2) and root mean square error (RMSE). The results show that the MLP networks are able to estimate the sound pressure level with values of R=0.9927, R2=0.9854 and RMSE=0.7313 for T1, R=0.9989, R2=0.9978, and RMSE=0.1515 for T2.
Compact low profile 5.8 GHz MPA for on-body applications Siba Monther Yousif; Anwer Sabah Mekki; Ahmed Jumaa Lafta
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4518

Abstract

A compact microstrip patch antenna (MPA) with a T shape monopole technique is designed, simulated, and measured. By using fire retardant material (FR-4) as a substrate with a low profile, the proposed antenna is designed and simulated to be used for on-body biomedical applications. A center frequency of 5.78 is achieved with a gain of 11.78 dB and a matching impedance of -47.47 dB. A 1.48 W/Kg (10 gm) as a specific absorption rate (SAR) is achieved and 29.69 dB front to back ratio with a bandwidth of 3.376 GHz. The antenna was examined in free space as well as on-body using CST-MW software. The proposed antenna is fabricated and examined. Finally, a comparison is done among simulated results, measured results, and the dual-band dual-mode antenna. The proposed antenna overcomes the latter work in terms of small size, high matching impedance, high front to back ratio, and operating bandwidth.
Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm Vivine Nurcahyawati; Zuriani Mustaffa
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4830

Abstract

High dimensionality in data sets is one of the challenges faced in classification, data mining, and sentiment analysis. In the data set, many dimensionalities require effort to simplify. Many of these dimensionalities have a major impact on the complexity and performance of the algorithms used for classification. Various challenges were encountered, including how to determine the optimal combination of pre-processing techniques, how to clean the dataset, and determine the best classification algorithm. This study uses a new approach based on the combination of three powerful techniques which are: tokenizing-lowercasing-stemming (for series of preprocessing), support vector machine (SVM) for supervised classification, and fuzzy matching (FM) for dimensionality reduction. The proposed model was realized using 3 different datasets, namely Amazon product review, movie review, and airline review from Twitter. This study provides better findings than the previous results. Improved performance is generated by SVM combined with FM, resulting in 96% accuracy. So that the SVM-FM combination can be said to be the best combination for sentiment analysis on the given data set.
Bitcoin trading indicator: a machine learning driven real time bitcoin trading indicator for the crypto market Ashikur Rahaman; Abu Kowshir Bitto; Khalid Been Md. Badruzzaman Biplob; Md. Hasan Imam Bijoy; Nusrat Jahan; Imran Mahmud
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4486

Abstract

As opposed to other fiat currencies, bitcoin has no relationship with banks. Its price fluctuation is largely influenced by fresh blocks, news, mining information, support or resistance levels, and public opinion. Therefore, a machine-learning model will be fantastic if it learns from data and tells or indicates if we need to purchase or sell for a little period. In this study, we attempted to create a tool or indicator that can gather tweets in real-time using tweepy and the Twitter application programming interface (API) and report the sentiment at the time. Using the renowned Python module "FBProphet," we developed a model in the second phase that can gather historical price data for the bitcoin to US dollar (BTCUSD) pair and project the price of bitcoin. In order to provide guidance for an intelligent forex trader, we finally merged all of the models into one form. We traded with various models for a very little number of days to validate our bitcoin trading indicator (BTI), and we discovered that the combined version of this tool is more profitable. With the combined version of the instrument, we quickly and with little error root mean square error (RMSE: 1,480.58) generated a profit of $1,000.71 USD.

Page 2 of 7 | Total Record : 65

 1   2   3   4   5 

Filter by Year

2023 2023


Reset
Filter By Issues
All Issue Vol 14, No 6: December 2025 Vol 14, No 5: October 2025 Vol 14, No 4: August 2025 Vol 14, No 3: June 2025 Vol 14, No 2: April 2025 Vol 14, No 1: February 2025 Vol 13, No 6: December 2024 Vol 13, No 5: October 2024 Vol 13, No 4: August 2024 Vol 13, No 3: June 2024 Vol 13, No 2: April 2024 Vol 13, No 1: February 2024 Vol 12, No 6: December 2023 Vol 12, No 5: October 2023 Vol 12, No 4: August 2023 Vol 12, No 3: June 2023 Vol 12, No 2: April 2023 Vol 12, No 1: February 2023 Vol 11, No 6: December 2022 Vol 11, No 5: October 2022 Vol 11, No 4: August 2022 Vol 11, No 3: June 2022 Vol 11, No 2: April 2022 Vol 11, No 1: February 2022 Vol 10, No 6: December 2021 Vol 10, No 5: October 2021 Vol 10, No 4: August 2021 Vol 10, No 3: June 2021 Vol 10, No 2: April 2021 Vol 10, No 1: February 2021 Vol 9, No 6: December 2020 Vol 9, No 5: October 2020 Vol 9, No 4: August 2020 Vol 9, No 3: June 2020 Vol 9, No 2: April 2020 Vol 9, No 1: February 2020 Vol 8, No 4: December 2019 Vol 8, No 3: September 2019 Vol 8, No 2: June 2019 Vol 8, No 1: March 2019 Vol 7, No 4: December 2018 Vol 7, No 3: September 2018 Vol 7, No 2: June 2018 Vol 7, No 1: March 2018 Vol 6, No 4: December 2017 Vol 6, No 3: September 2017 Vol 6, No 2: June 2017 Vol 6, No 1: March 2017 Vol 5, No 4: December 2016 Vol 5, No 3: September 2016 Vol 5, No 2: June 2016 Vol 5, No 1: March 2016 Vol 4, No 4: December 2015 Vol 4, No 3: September 2015 Vol 4, No 2: June 2015 Vol 4, No 1: March 2015 Vol 3, No 4: December 2014 Vol 3, No 3: September 2014 Vol 3, No 2: June 2014 Vol 3, No 1: March 2014 Vol 2, No 4: December 2013 Vol 2, No 3: September 2013 Vol 2, No 2: June 2013 Vol 2, No 1: March 2013 Vol 1, No 4: December 2012 Vol 1, No 3: September 2012 Vol 1, No 2: June 2012 Vol 1, No 1: March 2012 List of Accepted Papers (with minor revisions) More Issue