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Development of Automatic Detection and Monitoring Application System for Peristaltic Condition based on Machine Learning and Internet of Thing (IoT) System 7

Development of Automatic Detection and Monitoring Applicatio...

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7th International Conference of Computer and Informatics engineering, IC2IE 2024

作者： Astuti, Winda Giovanni Benedict Davin, K. Nurul Puji, Muhammad Kunaifi, Matthew Andriatin, Yuli Astuti Bina Nusantara University Binus Aso School of Engineering Automotive and Robotics Program Computer Engineering Department Jakarta Indonesia Cilacap Regional General Hospital Gatot Subroto No.28 Cilacap Indonesia

ISBN: (纸本)9798331505530

Poor sanitation and limited access to clean water are the main causes of increased risk of gastrointestinal diseases. In Indonesia, environmental hygiene problems are the main cause of digestive tract disorders, which contribute to more than 25% of deaths each year according to data from the Ministry of Health. Digestive disease is one of the diseases that often appears due to Poor sanitation and limited access to clean water. The development of a digital stethoscope for early detection of problems in the digestive tract would be useful, especially in remote areas. The sound was recorded from a Mel Frequency Cepstral Coefficient (MFCC) technique. The resulting features extracted from the stethoscope sound signal, are used as the inputs for the machine learning which applied three types of method, they are Logistic Regression (LR), combination of Logistic Regression (LR) and K-Nearest Neighbors (KNN), combination of Logistic Regression (LR) and Support Vector Machine (SVM). The experiment of MFCC with three types of machine learning methods above, based the sound from a stethoscope. Experimental results show the combination of Logistic Regression (LR) and K-Nearest Neighbors (KNN) has exceeded results among other methods, with the training and testing accuracy of 90 % and 88 % respectively. They confirm the effectiveness of the proposed intelligent automatic stethoscope sound identification based on Mel-Frequency Cepstral Coefficient (MFCC) and Linear Regression. Furthermore, the system continuously saves and monitors the result of the patient. © 2024 IEEE.

关键词： Logistic regression

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Automatic Cataract and Cholesterol Detection System Based on Recurrent Neural Network (RNN): A Comparison with Convolutional Neural Network (CNN) and Siamese Neural Network (SNN) 1

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19th International Conference of Intelligent Unmanned Systems, ICIUS 2023

作者： Lie, Zener S. Widodo, Jatmiko A. F. Hanesi, Isabel K. P. Moningka, Matthew R. N. Mariana, Y. Astuti, Winda Akmeliawati, Rini Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Product Design program Industrial Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Robotics and Automation Research Group School of Electrical and Mechanical Engineering The University of Adelaide North Terrace Campus Adelaide Australia

ISBN: (数字)9789819765911

ISBN: (纸本)9789819765904

In Indonesia, the majority of blindness cases, approximately 81%, are attributed to cataract. Additionally, 35% of the population has higher cholesterol levels than the standard normal range. The lack of specialized medical equipment, such as the slit-lamp (Ruparelia in Dalhousie Med J 49(1), [1]) bio-microscope, and trained professionals, especially in rural areas, make it difficult to detect these conditions through eye examinations. To address this issue, a supervised machine learning method, the Recurrent Neural Network (RNN), has been utilized to create an iris scanner system that can identify cataract and high cholesterol in a patient. This system will classify eyes into three categories, i.e., cataract, high cholesterol, and normal iris. The computer simulations indicate that this technique is more accurate and requires less training time compared to the existing Convolutional Neural Network (ANN) and Siamese Neural Network (SNN)-based method, while achieving 100% accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Automatic Heliostat Directing System Based On Microcontroller ESP32

Automatic Heliostat Directing System Based On Microcontrolle...

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2023 International Conference on Electrical engineering and Informatics, ICEEI 2023

作者： Medyanto, Richard Sanjaya, Nicholas Liawatimena, Suryadiputra Binus Aso School of Engineering Bina Nusantara University Automotive & Robotics Program Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350381290

Solar energy is one of the most abundant sources of renewable energy in Indonesia. Solar energy is now typically harnessed using solar panels, but the low efficiency of photovoltaic cells requires the development of other alternatives. The heliostat is a sunlight directing device with mirrors that can be used in a concentrated solar power system. Current heliostats require high capital investment due to their large frames and expensive components. This research was undertaken to develop a lower cost heliostat using a smaller frame, ESP32 microcontroller, servo motor and low-cost components. The position of the sun can be determined using an algorithm based on the National Oceanic and Atmospheric Administration (NOAA) solar calculator, and the mirror is moved to maintain the sun's reflection on a target. The result of this research is a set of heliostat prototypes consisting of the frame and control system. Tests were carried out to test the performance of the designed heliostat and it was found that the heliostat has an accuracy of about 60 cm and can raise temperatures up to 3.41°C. The conclusion is that the heliostat can be used in a concentrated solar power system to heat boilers in solar power towers. © 2023 IEEE.

关键词： Solar energy

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Automatic petrol and diesel engine sound identification based on machine learning approaches 1

Automatic petrol and diesel engine sound identification base...

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1st International Conference on automotive, Manufacturing, and Mechanical engineering, IC-AMME 2018

作者： Frederick, Halim Winda, Astuti Solihin, Mahmud Iwan Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Faculty of Engineering Technology and Built Environment UCSI University Cheras Kuala Lumpur56000 Malaysia

Petrol and diesel engine have a significantly different way to convert chemical energy into mechanical energy. In this work, the intelligent system approach is used to automatically identify the type of engine based on the sound of the engine. The combination of signal processing and machine learning technique for automatic petrol and diesel engine sound identification is presented in this work. After a signal preprocessing step of the engine sound, a Fast Fourier Transform (FFT)-based frequency characteristic modelling technique is applied as the feature extraction method. The resulting features extracted from the sound signal, in the form of frequency in the FFT matrix, are used as the inputs for the machine learning, the Support Vector Machine (SVM), step of the proposed approach. The experiment of FFT with SVM-based diesel and petrol engine sound identification has been carried out. The results show that the proposed approach produces a good accuracy in the relatively short training time. Experimental results show the training and testing accuracy of 100 % and 100 % respectively. They confirm the effectiveness of the proposed intelligent automatic diesel and petrol engine sound identification based on Fast Fourier Transform (FFT) and Support Vector Machines (SVMs). © 2019 The Authors, published by EDP Sciences.

关键词： Support vector machines

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MOSFET Based Active Rectifier Design in Wireless Capacitive Power Transfer for Electric Vehicles 1

MOSFET Based Active Rectifier Design in Wireless Capacitive ...

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1st International Conference on Smart Mechatronics, ICSMech 2024

作者： Dragon, Klemens Raphael Lie, Zener Sukra Muharam, Aam Ika, Juan Luciano Perdana, Muhamad Arjuna Amin Hapid, Abdul Binus Aso School of Engineering Bina Nusantara University Automotive and Robotics Program Computer Engineering Department Jakarta Indonesia Badan Riset dan Inovasi Nasional Research Center for Transportation Technology Jawa Barat Bandung Indonesia

ISBN: (纸本)9798350368918

While the rise of electric vehicles reflects a push for clean energy as envisioned by international agreements like the Paris Agreement, wireless charging's convenience can overcome limitations and hasten their adoption, making it a strategic step alongside regulations such Permenhub No. 15/2022 and SDG 9 'Industry, Innovation, and Infrastructure' for cleaner air and sustainable transportation. The aim is to convert the high frequency AC voltage from the coupling capacitor into a stable DC voltage. Further increase the system sensitivity to the load variations, by designing a MOSFET based active rectifier. By changing load parameters, the accuracy of PFC adaptation to the battery State of Charge (SOC) obtained is at 98% with 2% tolerance for errors. It is concluded that the research is said to be successful because it has met the parameters of general research hypothesis where the system is able to convert high frequency AC voltage into a stable DC voltage. © 2024 IEEE.

关键词： State of charge

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Applying CNN Models with Stereo Camera for Object Detection and Distance Estimation in Automatic Micro Electric Vehicle (MEVi) 1

Applying CNN Models with Stereo Camera for Object Detection ...

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1st International Conference on Smart Mechatronics, ICSMech 2024

作者： Harimanto, Farrell Putra Hasanah, Nurul Pradana, Muhammad Khristamto Aditya Puji, Muhammad Nurul Binus Aso School of Engineering Bina Nusantara University Automotive & Robotics Program Computer Engineering Department Jakarta11480 Indonesia National Research and Innovation Agency Kst Samaun Samadikun Research Center for Smart Mechatronics Bandung40135 Indonesia

ISBN: (纸本)9798350368918

Technological advancements in the automotive industry are currently focused on autonomous driving systems or driver assistance systems. Depth estimation is also an important feature of the autonomous driving system as it can accurately estimate the distance to the detected objects. In current developments technologies such as stereo cameras are more popular for a more accurate depth estimation. The process for object detection consists of a combination of classification, localization, and segmentation. Deep convolutional neural networks are the basis of object detection. The methods for object detection are divided into two categories namely the Region Proposal-based method such as RCNN, and the Classification-based method which provides for methods such as YOLO and MobileNet. As a part of the research conducted by the National Research and Innovation Agency (BRIN) on Automatic Micro Electric Vehicles or MEVi, this paper analyzed and compared the performance of several CNN models for object detection as well as the performance of depth sensing using ZED stereo camera which was used for the autonomous system in MEVi. Among several models that had been compared, YOLO v8 model demonstrates high accuracy and moderate speed as indicated by its high mAP (0.50) and FPS (38.46). Based on depth sensing results, the accuracy of distance measurement also depended on brightness and environment with the average of accuracy achieved indoors was 98.2% and outdoors was 96.3%. © 2024 IEEE.

关键词： automotive industry

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Development of Detection Kidney Function Stand Alone System and Design based on Oral Ammonia Levels 7

Development of Detection Kidney Function Stand Alone System ...

引用

7th International Conference of Computer and Informatics engineering, IC2IE 2024

作者： William Hansen, Natasha Angeline Mariana, Yosica Sitepu, Elioenai Astuti, Winda Siswanto, Melinda Bina Nusantara University Binus Aso School of Engineering Automotive and Robotics Program Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Aso School of Engineering Product Design Program Industrial Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Industrial Engineering Department Jakarta11480 Indonesia Bina Nusantara University Biotechnology Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798331505530

The development of detection kidney function system at the first prototype have problem with the dependence on the internet, which make not practical in the development of the system and the initial design was not ergonomic. In this work the development of the second step is done with the improvement of accuracy compared with other methods. In this work the new design of the embedded system is also applied and in term of algorithm, the system applied for woman patient. Three different types of machine learning techniques, namely K-nearest neighbors (KNN), Decision Tree and Random Forest are applied and compared. The AI-based kidney failure severity identification system with KNN algorithm had an average accuracy of 90% and 91% for training and testing accuracy, respectively. The design of the embedded system also renews with stylist, simple and practical. © 2024 IEEE.

关键词： Support vector machines

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Development of Wireless Integrated Early Warning System (EWS) for Hospital Patient 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Steady Oey, Endra Astuti, Winda Andriatin, Yuli Astuti Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Nursery Department Cilacap Regional General Hospital Gatot Subroto No. 28 Central Java Cilacap53223 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

The implementation of the Early Warning System (EWS) in hospitalized patients aims to identify worsening of the patient’s condition based on the assessment of vital signs. Currently, the EWS is still applied manually by nurses by filling out observation sheets to assess the risk of patient deterioration, so that it can be used. There was an error filling in by the human error factor. In addition, the lack of nurses in Indonesia makes the implementation of EWS not optimal. Therefore, a system for measuring vital signs and risk assessment of patient deterioration was created automatically which nurses can monitor through the website. The results of this study are a system design prototype consisting of five data retrieval devices and a Raspberry Pi as a web server. Each device sends data to the server wireless using the MQ Telemetry Transport (MQTT) protocol. The test results show that the system can retrieve vital signs data automatically every hour and displays a risk that is one level lower than manual EWS implementation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Deterioration

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Study on Optimal Machine Learning Approaches for Kidney Failure Detection System Based on Ammonia Level in the Mount 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Phandinata, Nicholas Puji, Muhammad Nurul Astuti, Winda Andriatin, Yuli Astuti Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Nursery Department Cilacap Regional General Hospital Gatot Subroto No.28 Central Jawa Cilacap53223 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Patients with kidney failure can emit bad breath with a certain level of ammonia content. The interpretation of ammonia levels in patients with renal failure was identified in this work. This research used the Chronic Kidney Disease dataset from UCI Machine Learning Repository. The dataset was processed first to get the value of eGFR (estimated Glomerular Filtration Rate) and ppb (parts per billion) of ammonia. Based on the eGFR value, the severity of kidney failure was divided into 5 categories, namely normal (Stage 1), mild (stage 2), moderate (stage 3), severe (stage 4) and failure (stage 5). The values of eGFR features are used as input for the machine learning technique in order to predict the level of kidney failure. Four different types of machine learning techniques, namely Support Vector Machine (SVM), Naïve Bayes (NB), Artificial Neural Network (ANN) and K-nearest neighbors (KNN), are applied and compared. The last process was to identify kidney failure using the K-Nearest Neighbors (KNN) method based on eGFR and ppb of ammonia dataset. AI-based kidney failure severity identification system with KNN algorithm had an average accuracy of 89.9% and 95.65% for training and testing accuracy, respectfully. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Support vector machines

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Design and Development of an Internet of Things (IoT)-Based Application and System to Detect Stunting in Infants and Toddlers

Design and Development of an Internet of Things (IoT)-Based ...

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HCI and UX Conference in Indonesia (CHIuXiD), International

作者： Theresa Tiffany James Jeremia Zener Sukra Lie Automotive and Robotics Engineering Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350380439

ISBN: (纸本)9798350380446

Currently, the process of monitoring children’s growth in Indonesia relies on manual methods for collecting anthropometric data. These methods pose a risk of data recording errors. Additionally, the ratio of healthcare workers to children in Indonesia is 1:90, a significant disparity that hinders efforts to reduce the prevalence of stunting in the country. To support the third Sustainable Development Goal (SDG), which emphasizes “Good Health and Well-being,” it is crucial to address issues in the stunting management process, from data collection to monitoring. The design and development of this iOS-based mobile application aim to assist in monitoring child growth and provide guidance and steps for prevention and treatment. Besides application development, it also involves the design and development of an Internet of Things (IoT) system to aid in the collection of children’s anthropometric data. The research methodology includes studying stunting cases in Indonesia, anthropometric data calculation methods, stunting prevention and management steps, application design and development, automatic anthropometric data collection, and online data storage and transmission using IoT technology. The developed application and system have been tested and successfully integrated. The IoT device has an accuracy rate of 99.83% for weight measurement calibration and 98.68% for height or length measurement calibration. From the testing results of the child condition calculation method in the application, an accuracy rate of 100% was achieved.

关键词： Weight measurement Pediatrics Accuracy Prevention and mitigation Data collection Mobile applications Calibration Internet of Things Monitoring Testing

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