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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 2,901 Documents
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Dental caries classification using depthwise separable convolutional neural network for teledentistry system Trie Maya Kadarina; Zendi Iklima; Rinto Priambodo; Riandini Riandini; Rika Novita Wardhani
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4428

Abstract

Caries may be halted or reversed in their progression by early detection, better hygiene habits, and coadministered drugs. The major clinical procedures for identifying dental caries are visual-tactile examination and dental radiography. However, due to their location, approximate caries exceedingly difficult to detect and affect the clinical assessment. Incorrect interpretations may also hinder the diagnostic procedure. Computational approaches and technology can be used to help dentists assess caries. Teledentistry has the ability to improve dental health care by providing access to dental care services from a remote location. Teledentistry helps identifying various stages of caries lesions using neural network and devices connected to the internet. This research develops an image classification for teledentistry systems using depthwise separable convolutional neural network. The trainable parameters reduction of depthwise separable convolution (DSC) successfully reduces the computational cost of conventional convolutional neural networks (CNN). As a result, the DSC model is reduced by 91.49% when compared to the traditional CNN model. Several DSC models improve conventional CNN accuracies in the training, validation, evaluation, and testing stages.
A proposed approach for diabetes diagnosis using neuro-fuzzy technique Maher Talal Alasaady; Teh Noranis Mohd Aris; Nurfadhlina Mohd Sharef; Hazlina Hamdan
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4269

Abstract

Diabetes is a chronic disease characterized by a decrease in pancreatic insulin production. The immune system will be harmed due to this condition, which will raise blood sugar levels. However, early detection of diabetes enables patients to begin treatment on time, therefore reducing or eliminating the risk of severe consequences. One of the most significant challenges in the healthcare unit is disease diagnosis. Traditional techniques of disease diagnosis are manual and prone to inaccuracy. This paper proposed an approach for diagnosing diabetes using the adaptive neuro-fuzzy inference system (ANFIS) based on Pima Indians diabetes dataset (PIDD). The three stages of the proposed approach are pre-processing classification and evaluation. Normalization, imputation, and anomaly detection are part of the pre-processing stage. The pre-processing was done by normalizing the data, replacing the missing values, and using the local outlier factor (LOF) technique. In the classification stage, ANFIS classifiers were trained using the hybrid learning algorithm of the neural network. Finally, the evaluation procedures use the last stage’s sensitivity, specificity, and accuracy metrics. The obtained classification accuracy was 92.77%, and it seemed rather promising compared to the other classification applications for this topic found in the literature.
Analysis of a Li-ion battery state of charge by artificial neural network Sumithara Arunagirinathan; Chitra Subramanian
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.5175

Abstract

The state of charge (SOC) is a battery residual capacity crucial assessment metric. The need for a precise SOC estimate is very important to ensure the safe functioning of a Li-ion battery and to prevent overload and over-depletion. However, the renewable energy-based standalone application has become a key problem to determine the exact capacity of SOC of the Li-ion battery. To estimate the capacity over time, the battery management system calculates the SOC of a Li-ion battery. This allows for the implementation of intelligent control systems. This paper presents an enhanced radial basis function (RBF) of the SOC battery estimate following the limits and weaknesses of the back propagation (BP) neural network (NN) in estimating battery SOC, such as sluggish convergence speed, poor generalization and can increase the accuracy of the network but it takes time to iterate. Train the enhanced RBF with experimental data in real-time. The trained NN of SOC is compared to actual values and the MATLAB is used to simulate the method to evaluate its accuracy.
Studying the effect of changing Input conditions on MMF using MGDM technique Zahraa H. Mohammed; Radhi Sehen Issa; Sadiq Ahmed
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4340

Abstract

The mode group diversity multiplexing (MGDM) multicast technology uses optical multiple input and output (O-MIMO) technology to provide greater capacity and the ability to transmit information over multi-mode fiber (MMF). The MGDM system has a benefit in terms of capacity expansion, which led to interest in its use in most optical communications. The MGDM exploits the optical fiber bandwidth by inserting spatial light detection, which increases the capacity of the MMF. This research aims to study the optical systems used for the MGDM technology, and to identify the methods of their analysis and design of O-MIMO systems to increase the amplitude of this signal. The conditions of light entry into the optical fiber such as typical spot size, radial displacements, angle, wavelength, and radius of the detectors sections are improved. Numerical MATLAB simulation is used to improve the amplitude of graded index multimode fiber (GI-MMF) and compared to the existing aggregation systems. Moreover, this method was simulated to improve the input and detection conditions to increase the O-MIMO capacity using the MGDM technique. Finally, the capacity of the MGDM system was studied and compared with different channels, and it is noticed that the capacity of the system increases with increasing the number of channels.
Serious game self-regulation using human-like agents to visualize students engagement base on crowd Khothibul Umam; Moch Fachri; Fresy Nugroho; Supeno Mardi Susiki Nugroho; Mochamad Hariadi
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3780

Abstract

Nowadays, the emergence of artificial intelligent (AI) technology for games has been advancely developed. A serious game is a technology employing AI to create a virtual environment in a serious gamification strategy. This research describes AI based virtual classrooms to adopt proper strategies and focusing on maintaining and increasing student engagement by encouraging self-regulation behavior at the learning process. The self-regulation behavior describes student's ability to direct their own learning to achieve learning targets on a path full of obstacles. By employing a human-like agent to visualize student engagement, this visualization aims to provide human-like experiences for users to comprehend student behavior. A reciprocal velocity obstacles (RVO)-based crowd behavior is employed to visualize student engagement. RVO is an autonomous navigation approach for directing the achievement of agents target. The human-like agents behave in various ways to reach the goal points depending on the performances and the obstacles before them. We employ our method in an investigation of students' learning activities in a pedagogically-centered learning environment at Universitas Islam Negeri (UIN) Walisongo, Semarang, Indonesia. The results demonstrate the best scenario changes along with the performances and obstacles faced to reach the goal points as well as the learning target.
A latent semantic analysis method for ranking the results of human disease search engine Loi Chan Quan Lam; Tran Kim Toai; Snasel Vaclav
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4602

Abstract

The human disease search engine based on the search query about disease factors (symptom, cause, position happening symptoms, i.e.,) helps users to conveniently diagnose the disease they may have anytime, anywhere. Therefore, the disease results returned by the search engine need to be accurate and ranked reasonably so that users can know which disease has the highest probability for their search query. We propose a method to arrange the returned diseases based on the latent semantic analysis (LSA) technique. This method helps to rank the disease results reasonably and meaningfully because it not only exploits the matching term frequency-inverse document frequency (TF-IDF) scores between the disease factors in the query and the disease results, but it also exploits the implicit relationship between the disease factors in the search query and the disease factors in the disease results.
Artificial intelligence system for driver distraction by stacked deep learning classification Qibtiah, Raja Mariatul; Zin, Zalhan Mohd; Hassan, Mohd Fadzil Abu
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.3595

Abstract

Increasing efforts in the transportation system have recently improved driver safety and reduced crash rates. Lack of attention and fatigue directly affect the driver's consciousness. Driver distraction is an essential driver-specific factor in the practical applicability of forward collision warning (FCW). However, there are still too many false alarms generated by the existing FCW system to be mitigated. This paper proposes facial detection to identify features and test anomalies' prediction against drivers using stacked convolutional neural network (CNN) layers. The proposed model used overlapping HAAR and stacked CNN features to identify classifications of eye areas, such as open or closed. In addition to the sliding query window's overall intensity information. The conventional HAAR function, which elevates the brightness of nearby regions, is still preferable. This method considers current intelligent transportation system-based solutions to minimize distraction effects by continuously comparing with flexible thresholds. The experimental results are analyzed from accurate driving datasets. At 456 iterations, the results acquired over 80% accuracy, while loss is near zero. The implication of driver's risk tolerance is further explored in this manner. Several risks are connected to driving any type of transportation system.
A machine learning approach in Python is used to forecast the number of train passengers using a fuzzy time series model Solikhin Solikhin; Septia Lutfi; Purnomo Purnomo; Hardiwinoto Hardiwinoto
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3518

Abstract

Train passenger forecasting assists in planning, resource use, and system management. forecasts rail ridership. Train passenger predictions help prevent stranded passengers and empty seats. Simulating rail transport requires a low-error model. We developed a fuzzy time series forecasting model. Using historical data was the goal. This concept predicts future railway passengers using Holt's double exponential smoothing (DES) and a fuzzy time series technique based on a rate-of-change algorithm. Holt's DES predicts the next period using a fuzzy time series and the rate of change. This method improves prediction accuracy by using event discretization. positive, since changing dynamics reveal trends and seasonality. It uses event discretization and machine-learning-optimized frequency partitioning. The suggested method is compared to existing train passenger forecasting methods. This study has a low average forecasting error and a mean squared error.
Energy efficiency scheme for relay node placement in heterogeneous networks As’ari, Aziemah Athirah; Apandi, Nur Ilyana Anwar; Muhammad, Nor Aishah; Rashid, Rozeha A.; Sarijari, Mohd Adib; Salleh, Jamaliah
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4050

Abstract

Relay node (RN) placement expands the network coverage and capacity and significantly reduces the energy consumption of heterogeneous networks (HetNets). Energy efficiency is the system design parameter in HetNets as it determines network operators' energy consumption and economic value. Relay is one of the energy-saving methods, where it can reduce the transmit power by breaking a long transmission distance into several short transmissions. However, placing an RN without a proper transmission distance may lead to a waste of energy. Thus, investigating an optimum RN placement in HetNets is crucial to ensure energy efficiency and maintain network performance. This paper presents an energy efficiency scheme for the RN based on four commonly used network topologies of indoor HetNets. The minimum energy consumption algorithm is proposed based on a comparison of distance and links of the RN. The results show that the circular network topology is an optimal network model with an efficiency factor increase of 6% that can be used to design the energy efficiency indoor HetNet.
Implementation of proportional–integral control in Baglog steamer temperature control Mila Fauziyah; Supriatna Adhisuwignjo; Dinda Ayu Permatasari; Nadira Aisyah Ibrahim
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3630

Abstract

Sterilization is the oyster mushroom cultivation process. Sterilization is used to kill nuisance microorganisms that can inhibit mushroom growth. The sterilization process is 8 hours at a temperature of 70–95 oC. This process of frequent breakdown is caused by the unstable temperature sterilization space and is controlled manually. Based on these problems, the right solution is to use a steamer that can be controlled automatically using the proportional–integral (PI) control method. PI controller consists of proportional gain and integral gain. To determine the value of proportional gain and integral gain, this study used the Ziegler-Nichols tuning method using the S curve. The results of the PI control parameters obtained the value of Kp=25.2 and Ki=0.302. Thus, producing a transient response graph with Mp=94.5; Os=0.45; PO=0.47; Tr=16,440 s. The system can work according to setpoint 95 oC and maintain a stable temperature according to the setpoint with these results. And the sterilization time becomes fast from 8 hours to 6 hours.

Page 154 of 291 | Total Record : 2901

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