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1st International Conference of Adisutjipto on Aerospace electrical engineering and Informatics, ICAAEEI 2024

作者： Kusumaningrum, Anggraini Wahyu, Septian Sahal, Ahmad Pujiastuti, Asih Ayuningtyas, Astika Pratiwi, Elizabeth Anna Adisutjipto Institute of Aerospace Technology Informatics Study Program Yogyakarta Indonesia Adisutjipto College of Technology Informatic Engineering Department Yogyakarta Indonesia Respati University D3 Information Technology Study Program Faculty of Science and Technology Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Aerospace Engineering Study Program Yogyakarta Indonesia

ISBN: (纸本)9798331507930

The design and construction of delivering announcements using a web-based SMS gateway aims to help overcome problems that often occur at pioneer airports;this is because when passengers want to travel, many passengers still arrive late to check-in, even though the passenger ticket has a notification for checking. -in 1 hour before Departure. If a flight's Departure is delayed or accelerated, passengers will only receive this information when they arrive at the airport, whereas if at the time the departure notification is delayed or accelerated, the passenger is still outside the airport or still on their way to the airport, as a result, they will definitely not receive the information. Testing voice announcements on parking, arrival, departure, delay, check-in, and waiting room services, as well as testing SMS broadcasts (30 user numbers and one airline), shows that the announcement application provides information through voice data recording and broadcasts SMS to passengers and families. Testing was carried out at Indonesian Pioner airport for 30 passengers for a week. The percentage of Ease of obtaining test voice information was 81.98%, and the percentage of Ease of obtaining broadcast SMS information was 83.1%. Implementing the announcement is feasible at pioneer airports with a percentage of 82.54%. © 2024 IEEE.

关键词： Text messaging

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Integrating Visual Geometry and Mask Region CNN for Enhanced UAV Detection and Identification 100

Integrating Visual Geometry and Mask Region CNN for Enhanced...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Ahmad, Ishtiaq Narmeen, Ramsha Alawad, Mohamad A. Alkhrijah, Yazeed Ho, Pin-Han Czech Technical University Faculty of Electrical Engineering Prague Czech Republic Imam Mohammad Ibn Saud Islamic University Department of Electrical Engineering Saudi Arabia University of Waterloo Electrical and Computer Engineering Canada Uestc Shenzhen Institute for Advanced Study China

ISBN: (纸本)9798331517786

Unmanned aerial vehicles (UAVs) have been adopted in various applications, including agriculture, public safety, surveillance, and crucial military missions. However, alongside their advantageous nature, UAVs have also been employed for malicious activities, leading to an increased requirement for timely detection and identification. Despite significant progress in UAV detection, challenges persist, particularly concerning various types of UAVs, the payload carried by UAVs, and the traits of their flight. Employing single machine learning for detection and identification has limitations due to the inability to handle diverse datasets and acquire complex relationships. Therefore, in this paper, we introduce a novel integration of the Visual Geometry Group-based convolutional neural network (VGG-CNN) framework employed for detection with the Mask Region-based convolutional neural network (MR-CNN) for identification of UAVs (jointly termed MR-DCNN). For efficient deployment of MR-DCNN, we add diversity to the dataset by performing data augmentation of new images in the training dataset for the detection of various types of UAVs, payload categories, and flight characteristics. The performance evaluation of the MR-DCNN approach was conducted via simulations, revealing superior detection capabilities for malicious UAVs compared to existing methods. © 2024 IEEE.

关键词： Convolutional neural networks

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Unraveling Game Addiction Behavior Among Digital Native Students: Leveraging Machine Learning for Insights 13

Unraveling Game Addiction Behavior Among Digital Native Stud...

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13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Amriza, Rona Nisa Sofia Fadhilla, Cut Alna Telkom University Department of Information System Purwokerto Indonesia Universitas Samudra Informatics Study Program Faculty of Engineering Aceh Indonesia

ISBN: (纸本)9798350368086

The allure of gaming often leads to excessive engagement, which can result in addictive and problematic behaviors, negatively impacting academic performance and school achievements. While gaming becomes increasingly prevalent, concerns about game addiction have grown, particularly among students. The excessive and obsessive usage of video games that characterizes game addiction can have detrimental effects like academic deterioration, social isolation, and mental health problems. This study seeks to elucidate the complex patterns of game addiction among digital-native students by utilizing advanced machine learning techniques. We employed Random Forest, SVM, Decision Tree, and Naïve Bayes algorithms to classify game addiction data from students. To address the class imbalance, we applied Borderline-SMOTE. Our results demonstrate that decision tree and random forest outperformed other machine learning approaches, achieving an accuracy of 95% in classifying game addiction. These findings contribute to the expanding body of knowledge on game addiction. Furthermore, in order to provide real-time monitoring and intervention for students who are at risk of developing a game addiction, future research may investigate incorporating these findings into educational platforms or applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Batik Industrial Waste Monitoring and Control System Based on the Internet of Things 5

Batik Industrial Waste Monitoring and Control System Based o...

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5th International Conference on electrical, Electronics, Informatics, and Vocational Education, ICEELINVO 2022

作者： Ali, M. Firdaus, M.F. Electrical Engineering Education Department Faculty of Engineering Universitas Negeri Yogyakarta Indonesia Electrical Study Program Vocational School Universitas Negeri Yogyakarta Indonesia

The aims of this research are to (1) Design and build a Batik Industrial Waste Monitoring and Control System Based on the Internet of Things (BIWMCS -IoT);(2) Test the performance of the BIWMCS-IoT system. The design and implementation of the BIWMCS-IoT system consist of five stages, namely: (1) Needs analysis, (2) Design, consisting of electronic planning, hardware, and software;(3) Development, including hardware and software development;(4) Testing, and (5) implementation. The BIWMCS-IoT system consists of a sensor unit to measure the parameters of water turbidity, temperature, Power of Hydrogen (pH), and the flow velocity of the wastewater then obtained by the microcontroller and the results are sent to the Wi-Fi module to adjust the solenoid valve opening and closing. Based on the testing of the system, has been shown that the BIWMCS-IoT system can work well according to the standards in the Minister of Indonesian Environment Regulation Number 05 of 2014. The reading accuracy of the batik waste monitoring system reaches 96.6% with the delivery time to the smartphone less than 30 seconds. This shows that this system can work well to monitor and control the liquid waste produced by the batik industry. © Published under licence by IOP Publishing Ltd.

关键词： Internet of things

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Arduino and LabVIEW Based Ping-Pong Ball Position Control using PID Controller 17

Arduino and LabVIEW Based Ping-Pong Ball Position Control us...

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17th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2023

作者： Ramelan, Agus Rohman, Febri Abdul Sulistyo, Meiyanto Eko Adriyanto, Feri Sartika, Nike Zakaria, Diky Faculty of Engineering Universitas Sebelas Maret Dept. of Electrical Engineering Surakarta Indonesia UIN Sunan Gunung Djati Bandung Dept. of Electrical Engineering Bandung Indonesia Mechatronics and Artificial Intelligence Study Program Universitas Pendidikan Indonesia Bandung Indonesia

ISBN: (纸本)9798350309164

Industrial development requires accurate and reliable control technology. One of the popular control systems is PID control. This paper discusses the implementation of the PID method for ping-pong ball position control by utilizing the theory of the Coanda effect. The control system was designed using Arduino as the controller and LabVIEW as the control interface. Tests were carried out by combining set point values to determine whether the position control system worked properly or not. The test results show that the PID method combined with LabVIEW can work well with the results of the PID control test at successive heights of 15 cm, 20 cm, 25 cm, 30 cm, and 35 cm obtained rise time (tr) 3.49 seconds, 4.58 seconds, 9.08 sec, 9.92 sec, and 8.84 sec. The average tolerance is 1.5 cm for the upper limit and 1 cm for the lower limit. © 2023 IEEE.

关键词： Position control

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Coordinated-axis Weight for Real-Time Trace Transform

Coordinated-axis Weight for Real-Time Trace Transform

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2023 International electrical engineering Congress, iEECON 2023

作者： Jundang, Nattapong Sitjongsataporn, Suchada Mahanakorn University of Technology The Electrical Engineering Graduate Program Faculty of Engineering and Technology Bangkok Thailand Mahanakorn University of Technology Faculty of Engineering and Technology Department of Electronic Engineering Bangkok Thailand

ISBN: (纸本)9798350332360

This paper presents a method for building Coordinated-axis Weight (CW) that has the advantage can be used to increase the speed for converting image data into the domain of real-time trace transforms. The main idea for this method is to use a feature reduction method for image data that must be used in cooperation with supervised machine learning outcome predictions. But the obtained result still retains the distinctive features of the data image in the form of a trace transform. Start by reducing the number of features from the image data, which will be used to reduce the size of the image to the appropriate image size, which in this article will reduce the image size to 45x45 pixels. The next step is to train the data using a Decision Tree (DT) method by aligning the reduced image data in a 1D model. In addition to the image data, two more features are added, i.e. the φ and ρ positions, to inform the prediction that each position has a different value. And the last step is to bring the data to create a model for predicting the answer. The weights obtained from the model are called Coordinated-axis Weights (CW). The experiments within this paper were experimented using the Caltech-256 database, which was able to measure the MSE, RMSE, and PNSR of the images compared to the original trace transform response images at 4.1, 2.02, and 42.00, respectively, including the highest predictive speed of 0.2 ms per image. © 2023 IEEE.

关键词： Decision trees

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Performance of GNSS positioning in PPP mode using MADOCA precise products

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Geodesy and Geodynamics 2024年第6期15卷 642-651页

作者： Brian Bramanto Rachel Theresia Irwan Gumilar Sidik T.Wibowo Geodetic Science Engineeringand Innovation Research GroupFaculty of Earth Sciences and TechnologyInstitut Teknologi BandungBandung 40135Indonesia Geodesy and Geomatics Engineering Study Program Faculty of Earth Sciences and TechnologyInstitut Teknologi BandungBandung 40135Indonesia Geodetic and Geodynamic Control Network Center Geospatial Information Agency of Indonesia(BIG)Cibinong 16911Indonesia

The Global Navigation Satellite System (GNSS) is widely utilized for accurate *** commonly applied method to obtain precise coordinate estimates is by implementing the relative positioning in network ***,this approach can be complex and ***,The Japan Aerospace Exploration Agency (JAXA) offers freely available satellite orbit and clock correction products called Multi-GNSS Advanced Demonstration Tool for Orbit and Clock Analysis (MADOCA),which can enhance positioning accuracy through the precise point positioning (PPP) *** study focuses on evaluating PPP static mode positioning using MADOCA products and comparing the results with the highly precise relative positioning *** analyzing a network of 20 GNSS stations in Indonesia,we found that the PPP method using MADOCA products provided favorable positioning *** median discrepancies and the corresponding median absolute deviation (MAD) for easting,northing,and up components were estimated as 9±18 mm,10±9 mm,and 3±40 mm,*** results indicate that PPP with MADOCA products can be a reliable alternative for establishing Indonesia's horizontal control networks,particularly for orders 0,1,2,and 3,and for a broad spectrum of geoscience monitoring ***,considerations such as epoch transformations and seismic activities should be taken into account for accurate positioning applications that comply with the definition of the national reference framework.

关键词： Global Navigation Satellite System(GNSS) Precise point positioning(PPP) MADOCA Positioning evaluation

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Reinforced Deep Learning By Discriminant Feature Trace Transform

Reinforced Deep Learning By Discriminant Feature Trace Trans...

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2023 International electrical engineering Congress, iEECON 2023

ISBN: (纸本)9798350332360

Classification of objects using YOLO deep learning is currently very popular. When the discovered object is rotated, however, the pre-trained YOLO model frequently generates incorrect answer predictions. In order to improve the accuracy of the YOLO deep learning pre-trained model, we present in this paper a method for rotating an object's image back to its initial gain using the DFTF algorithm. The DFTF algorithm is designed to take the results of the trace transform and accumulate the values for each column in order to predict the object's degree of rotation. The results of this calculation are then used to rotate the object back to its normal axis. The experiment is therefore a collaboration between YOLO and DFTF with the aim of more accurately predicting the object's responses. Five groups of data will be chosen for experimentation, each of which can be used to determine the precision of the experimental image with rotational issues. The average of accuracy is 98.56%. © 2023 IEEE.

关键词： Forecasting

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Performance Evaluation of Coverage Prediction in 4G Networks Using Machine Learning Classification Algorithm 10

Performance Evaluation of Coverage Prediction in 4G Networks...

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10th International Conference on Wireless and Telematics, ICWT 2024

作者： Yuliana, Hajiar Iskandar Hendrawan Ridwan, Azwar Mudzakkir Charisma, Atik Somantri, Nivika Tiffany Universitas Jenderal Achmad Yani Faculty of Engineering Department of Electrical Engineering Indonesia Institut Teknologi Bandung Doctoral Program of School of Elecrtical Engineering and Informatics Indonesia Bandung Indonesia UIN Sunan Gunung Djati Bandung Department of Electrical Engineering Indonesia

ISBN: (纸本)9798350377057

Network coverage prediction is an important aspect of 4G network planning and optimization. In this study, we conducted a comprehensive analysis of the performance of various machine learning algorithms in predicting 4G network coverage. The data collected includes information on location, signal intensity, topography, and other important variables. Data pre-processing is performed to clean, normalize, and select relevant features. Next, we compared the performance of several machine learning algorithms, including Logistic Regression, KNN, Naïve Bayes, Decision Tree, SVM, XGBoost, AdaBoost, and Bayesian Network Classifier. The model training process is performed using subsets of training, validation, and testing data, with evaluation performed using various evaluation metrics such as accuracy, precision, recall, F1-score, RMSE, MAE, and R-squared. The experimental results show that Random Forest stands out with excellent performance, while Naïve Bayes tends to produce lower performance. The conclusion of this study provides valuable insights into the potential applications of machine learning algorithms in predicting 4G network coverage with accuracy and reliability. © 2024 IEEE.

关键词： Adversarial machine learning

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Java Bali Grid Mean Time To Failure Prediction using Machine Learning 1

Java Bali Grid Mean Time To Failure Prediction using Machine...

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1st International Conference on Technology, engineering, and Computing Applications, ICTECA 2023

作者： Sesotyo, Priyo Adi Pandjaitan, M.M. Lanny W. Lukas Universitas Katolik Indonesia Atma Jaya Professional Engineer Study Program Faculty of Engineering Jakarta12930 Indonesia Universitas Semarang Electrical Engineering Dept. Semarang50196 Indonesia Universitas Katolik Indonesia Atma Jaya Electrical Engineering Master Program Jakarta12930 Indonesia Jakarta12930 Indonesia

ISBN: (纸本)9798350360554

The role of Mean Time To Failure (MTTF) is critical to maintain cost-efficient in smart grids, as the predictive maintenance means for ensuring their reliability. Java Bali grid is the most important power network in Indonesia, because it holds 75% energy sales and the foundation of the Indonesian government, business and industrial sectors. One method for prediction in the machine learning is the Linear Regression, as part of the Supervised Learning technique. The regression model extracts the grid overload variable from MV distribution system, where connected load (MVA) divided by the number of MV customers. The model constructs the Linear Regression function, where the slope and intercept values found from the correlation between predicted response and actual response. After confronting the function f(x) and the coefficient of determination (R2), the test results for the MTTF prediction values for Java-Bali's region shows the higher accuracy when the R2 shows highest value. © 2023 IEEE.

关键词： Forecasting

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