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2nd International Conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Tambunan, Indra Hartarto Apryanto, Noel Riki Institut Teknologi Del Electrical Engineering Study Program Laguboti Indonesia

ISBN: (纸本)9798350351613

This article presents the development and implementation of an Internet of Things (IoT) based Smart Controlled Poultry Farm Management System to enhance the efficiency and welfare of day-old chicks. The system prototype employs a DS18B20 temperature sensor and a microcontroller to automatically monitor and regulate the temperature, feeding, and drinking processes within the cage. Additionally, a web platform is provided for real-time condition monitoring informing the owner of any problems that may occurred. Temperature within the cage is maintained in the range of 30°C to 35°C with a measurement error rate of ±1°C. This temperature range is controlled by switching the fan on and off, as well as opening and closing the curtain. Experimental results indicate that the system has the potential to improve the efficiency of maintaining optimal chick weight and temperature, as well as ensuring consistent feeding times, all of which are crucial for sustainable poultry production. These parameters were monitored over a period of 21 days. © 2024 IEEE.

关键词： Microcontrollers

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Comparative Analysis of Deep Learning Methods for Automated Diagnosis of Pulmonary Diseases from Chest X-Ray Images: A study Based on ICD-10 2

Comparative Analysis of Deep Learning Methods for Automated ...

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2nd International Conference on Data, Information and Computing Science, CDICS 2024

作者： Wijayakusuma, I.G.N. Lanang Sudarma, Made Putra, I. Ketut Gede Darma Sudana, Oka Engineering Study Program Universitas Udayana Bukit Jimbaran Indonesia Electrical Engineering Study Program Universitas Indonesia Bukit Jimbaran Indonesia Information Technology Study Program Universitas Udayana Bukit Jimbaran Indonesia

ISBN: (纸本)9781643685762

This research delves into the comparative assessment of diverse deep learning architectures for the automated identification of pulmonary diseases in chest X-ray images, aligning with the diagnostic framework of the International Classification of Deseases, 10th Revision (ICD-10). The evaluated architectures encompass Convolutional Neural Network (CNN), MobileNetV2, DenseNet, VGG16, and InceptionV3. Through an extensive investigation conducted on a comprehensive dataset comprising 108,948 chest X-ray images, we scrutinize the performance metrics and diagnostic accuracy of each architecture. Notably, our findings reveal MobileNetV2 as the frontrunner, exhibiting an outstanding accuracy rate of 97% for Emphysema disease. This remarkable accuracy underscores the robustness and efficiency of MobileNetV2, even amidst large-scale diasets, positioning it as a promising solution for the automated detection of pulmonary ailments from medical imaging. Additionally, we analyze the computational efficiency and resource requirements of each architecture, providing insights into their practical applicability in real-world clinical settings. The implications of our study extend beyond technical benchmarks, offering valuable insights for healthcare practitioners, researchers, and developers working towards enhancing diagnostic processes in pulmonary healthcare. By elucidating the comparative strengths and limitations of various deep learning architectures, this research contributes to the advancement of computer-aided diagnosis systems, paving the way for more accurate and efficient detection of lung diseases from chest X-ray imaging data. © 2025 The Authors.

关键词： Lung cancer

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Early Detection of Heart Rate Conditions in Autistic Children Using IoT-Based Systems 4

Early Detection of Heart Rate Conditions in Autistic Childre...

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4th International Conference on electrical engineering and Computer Science, ICECOS 2024

作者： Albar, Nizam Melinda, Melinda Away, Yuwaldi Universitas Syiah Kuala Dept. Electrical Engineering Master of Electrical Engineering Study Program Banda Aceh Indonesia Universitas Syiah Kuala Engineering Faculty Dept. Electrical Engineering and Computer Banda Aceh Indonesia

ISBN: (纸本)9798350368253

Early detection of heart rate conditions in autistic children is crucial for anticipating tantrum reactions and providing prompt medical intervention. Previous research designed a prototype for acquiring Electrocardiogram (ECG) signals using the AD8232 sensor, but it required a long time to identify heart rate conditions. This study aims to develop the previous prototype using Internet of Things (IoT) implementation to acquire and identify the heart rate conditions of autistic children in real time. The ECG signal data is analyzed using Butterworth Filter and Artificial Neural Network (ANN) integrated into Raspberry Pi and results are transmitted to the Blynk App. Testing over 20 minutes showed that in the first 10 minutes, the system identified a normal heart rate at 105.19 BPM with 67% accuracy, while in the second 10 minutes, the heart rate rose to 117.19 BPM with tantrum symptoms and 75% accuracy. This system helps medical personnel provide immediate intervention during tantrums. © 2024 IEEE.

关键词： Butterworth filters

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Performance of DCT-DWT Feature Extraction in Recognizing Guitar Chords 3

Performance of DCT-DWT Feature Extraction in Recognizing Gui...

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3rd FORTEI-International Conference on electrical engineering, FORTEI-ICEE 2024

作者： Sumarno, Linggo Electrical Engineering Study Program Sanata Dharma University Yogyakarta Indonesia

ISBN: (纸本)9798331542207

This work deals with optimization problems in the field of audio signals with particular emphasis on recognizing guitar chords. As an extension to the previous works done in this field, the primary goal was to minimize the feature extraction length without loss of recognition performance. In this case, this work used two such well known methods in audio signal processing as the Discrete Cosine Transform (DCT) and the Discrete Wavelet Transform (DWT). This method is based on the iterative analysis of three main system attributes: the frame blocking length;the choice of wavelet functions;and the DWT coefficient selection offset. After careful tuning of these parameters, this work was able to optimize the feature extraction length to only three, which however represents a modest improvement on the previous works in this field. The system developed in this research obtained recognition accuracy of up to 91.43% when the optimized feature extraction length was employed. The significance of this study extends to the non-conventional applications of guitar chord recognition. Shortening the feature extraction length is expected to improve more usages in the different applications of the music technology. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Patient Face Recognition for Medical Robots Based on Convolutional Block Attention Module

Patient Face Recognition for Medical Robots Based on Convolu...

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2024 International Conference on Control and Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence, CERIA 2024

作者： Turnip, Arjon Aina, Adnnia Nafis Qulub Sitompul, Erwin Padjadjaran University Department of Electrical Engineering Sumedang Indonesia Study Program of Electrical Engineering President University Cikarang Indonesia

ISBN: (纸本)9798331511166

The shortage of medical personnel to care for patients has led to a decline in public health services. This issue has been exacerbated by the COVID-19 pandemic, which has claimed numerous lives, particularly among healthcare workers. To address this challenge, this study explores the application of a Deep Learning method, specifically the Convolutional Neural Network (CNN), integrated with the Convolutional Block Attention Module (CBAM) for patient face recognition in Medical Robot Assistive (MRA) systems. The findings demonstrate that combining CNN with CBAM offers a highly efficient solution for optimizing face recognition in MRAs. This approach not only delivers high accuracy but also enhances the visual representation of the CNN model and reduces training time. Moreover, it improves the safety and effectiveness of interactions between the MRA and patients during face recognition. The proposed method has been tested and achieved an impressive accuracy of 95 % at an optimal distance of 30 cm. © 2024 IEEE.

关键词： COVID-19

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System Design of Microcontroller Based Smart Automatic Transfer Switch of Solar Panel Control

System Design of Microcontroller Based Smart Automatic Trans...

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2023 International Conference on Smart-Green Technology in electrical and Information Systems, ICSGTEIS 2023

作者： Wayan Sukadana, I. Giriantari, Ida Ayu Dwi Ariastina, Wayan Gede Swamardika, Ida Bagus Alit Abiyasa, Agus Putu Universitas Pendidikan Nasional Electrical Engineering Study Program Denpasar Indonesia Universitas Udayana Electrical Engineering Study Program Denpasar Indonesia

ISBN: (纸本)9798350382822

Solar energy is a renewable energy that is currently being widely used. In general, independent solar power plant systems that are installed in residential homes use an off-grid system to simplify the installation process. However, the off-grid system has a weakness that the load on the independent solar power plant system would be limited and it is difficult to transfer the source of electrical energy when the storage battery capacity on the solar panel has run out. Transferring the source of electrical energy can be conducted but the load will be in a momentary blackout during the load transfer process. This would have a negative impact on sensitive loads such as compressors and refrigerators. To avoid the problem, a system for transferring electrical energy sources is required using the phase synchronization method at the source that will change by using the ESP32 as a control center which has supporting functions such as reading parameters, calculating the amount of electrical power used, recording data (data logging), and storing data on electronic media such as MicroSD Card. The result of this study was a Smart ATS system design that could transfer loads with a power rating of 450W using a source synchronization method that was able to display parameter data and record data regularly every 10 minutes. After testing the system, a saving value in the use of electrical energy was obtained by 0.1 kWh of the total use of 0.5kWh in which that the system created could save 20% of the use of electrical energy © 2023 IEEE.

关键词： Digital storage

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Arrhythmia Disease Detection Through ECG Signal Monitoring with Decision Tree Method

Arrhythmia Disease Detection Through ECG Signal Monitoring w...

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2024 International Conference on Control and Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence, CERIA 2024

作者： Sitompul, Erwin Noor, Muhammad Aqshal Said Turnip, Arjon President University Study Program of Electrical Engineering Cikarang Indonesia Padjadjaran University Department of Electrical Engineering Sumedang Indonesia

ISBN: (纸本)9798331511166

Arrhythmia is a heart disorder that is a leading cause of death worldwide. Early detection and proper management are essential to reduce its impact. With Machine Learning, cardiovascular disease detection can be performed more quickly, accurately, and efficiently. This study aims to classify patient data with arrhythmia using Machine Learning algorithms, specifically the Decision Tree method. The recorded electrocardiography (ECG) signals were processed through filtering and feature extraction to produce data that is easy to classify. The results showed that data processing using the Decision Tree method achieved an accuracy rate of 92.96%. © 2024 IEEE.

关键词： Random forests

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Modeling the Percentage of NEET in Indonesia with Spatial Cauchy Regression through the Bayesian Analysis Approach

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IAENG International Journal of Applied Mathematics 2024年第7期54卷 1288-1295页

作者： Rantini, Dwi Fakhruzzaman, Muhammad Noor Ningrum, Ratih Ardiati Othman, Fazidah Choir, Achmad Syahrul Ramadan, Arip Alya, Najma Attaqiya Putri, Elfira Rahma Pratama, Muhammad Alfian Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology andMultidiscipline Universitas Airlangga Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia Statistical Computing StudyProgram Politeknik Statistika STIS Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Indonesia

Indonesia has entered a period of demographic bonus. Human resources must be optimized. The number of children who do not in employment, education or training (NEET) in each province needs attention. Several factors that could contribute to the decline in the NEET percentage are literacy rates and the number of adolescents with computer skills. Increasing these two factors is believed to be able to reduce the percentage of NEET in every province in Indonesia. To find out the relationship between these two factors and how much influence they have on the percentage of NEET, this research is modeled by Cauchy regression and includes spatial effects. The results of the analysis show that the best model is when the spatial effect is modeled by Fernandez Steel Skew Normal conditionally autoregressive (FSSN CAR). This result is seen from the smallest value of the Watanabe Akaike Information Criterion (WAIC) in this model, which is 190.5. The parameter estimated shows that the higher the literacy rate and the number of adolescents with computer skills, the lower the percentage of NEET in each province in Indonesia. The results of this research can be useful for the Indonesian government to increase the number of educational facilities related to these two factors. © (2024), (International Association of Engineers). All rights reserved.

关键词： Bayesian CAR Cauchy Education NEET

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Heart Rate Health Classification in Patients During Physical Activities Using K-Nearest Neighbors

Heart Rate Health Classification in Patients During Physical...

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2024 International Conference on Control and Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence, CERIA 2024

作者： Turnip, Arjon Juliansyah, Willy Sitompul, Erwin Universitas Padjadjaran Electrical Engineering Department Sumedang Indonesia President University Study Program of Electrical Engineering Cikarang Indonesia

ISBN: (纸本)9798331511166

Cardiovascular diseases, encompassing a range of disorders affecting the heart and blood vessels, are the leading cause of death worldwide. The fact that cardiovascular diseases account for at least 32% of all global deaths per year is a significant indicator of its impact. This emphasizes the urgency of early detection approaches, enabling patients to receive faster diagnoses and earlier treatment. In this report, the authors utilize the K-Nearest Neighbors method to classify patients' health conditions from electrocardiogram (ECG) data during sitting, walking, and running activities. The objective is to enhance the accuracy of heart rate and arrhythmia detection while contributing to global heart disease prevention efforts. The results of the K-Nearest Neighbors classification demonstrate excellent performance, with an accuracy of 91.9%, precision of 90%, recall of 90%, and an F1-score of 90%. © 2024 IEEE.

关键词： Patient treatment

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L-Heat Pipe Simulation for Lithium-Ion Electric Vehicle Battery Cooling Systems 4

L-Heat Pipe Simulation for Lithium-Ion Electric Vehicle Batt...

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4th International Symposium on Advances and Innovation in Mechanical engineering, ISAIME 2023

作者： Septiadi, Wayan Nata Astawa, Ketut Eleazar, Rolland Pierce Ramadhani, Dandi Bhaswara, Nabiel Rafa Angel Jayendra, Kadek Dananta Study Program of Mechanical Engineering Faculty of Engineering Universitas Udayana Bali Bukit Jimbaran80361 Indonesia

The Li-ion battery is electrical storage in the form of chemical energy and is considered the strongest candidate for electrical-based automotive applications. The performance of the battery greatly affects the performance of electric vehicles. There is a need for effective thermal management to control battery temperature below a defined temperature such as 50°C. The thermal management system in the battery can be done by passively cooling it using heat pipes and heat sinks. This research was conducted by simulating the Makara Electric Vehicle's electric scooter battery module which was applied with a heat pipe-based cooling system. The simulated module focuses on heat pipes and 18650 lithium-ion batteries with discharge rate variations of 1C, 2C., and 3C. The method used is literature study and CFD simulation. The results show that the heat pipe cooling system can reduce battery temperature at each discharge rate variation of 12,85%, 16,23%, and 23,74%. Heat Pipe can absorb the highest heat of 68,1178 kW when the module uses a 3C battery. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Cooling systems

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