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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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Traffic Signs Detection System Using YOLO (You Only Look Once) That Provides Notification

Traffic Signs Detection System Using YOLO (You Only Look Onc...

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Smart Mechatronics (ICSMech), IEEE International Conference on

作者： Yosua Setiawan Muhammad Nurul Puji Winda Astuti Computer Engineering Department Automotive & Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350368918

ISBN: (纸本)9798350368925

Road accidents have become a problem for many road users in many countries. It may affect human daily activity and tasks, potentially influencing productivity and safety. Therefore, reducing road accidents as swiftly and effectively as possible is critical. For this reason, a Traffic signs detection system has been developed in this work to improve road safety. The detection system can detect traffic signs on the road such as traffic lights, speed limits, and no U-turn signs, and then it will provide users with the corresponding text on an LCD and vibration feedback using a motor. Throughout this work, the You Only Look Once (YOLO) algorithm is proposed for detecting traffic signs on the road. Moreover, a camera is used to extract live videos which serve as an input for the Raspberry Pi 4 model B containing custom YOLOv5 and YOLOv8 models. The system then sends the output to an LCD, a vibration motor, and a radio frequency (RF) module as notifications for users. In the end, the performance of the proposed method has been collected and it is found to produce good accuracy within an inconsiderable amount of 1.677 images and 2.263 annotations. Therefore, the accuracy of 0.69 for YOLOv5 and 0.7 for YOLOv8 were obtained from the training and validation process. Furthermore, the system accurately detects the traffic signs on the road.

关键词： YOLO Vibrations Training Road accidents Accuracy Motors Liquid crystal displays Road safety Safety Videos

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Design of an Automatic Price Tag System for Web-Based Retail Business

Design of an Automatic Price Tag System for Web-Based Retail...

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International Conference on Information Management and Technology (ICIMTech)

作者： Suryadiputra Liawatimena Devina Gunawan Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Computer Science Deparment BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

Modern retail businesses face a significant challenge with the inefficiency of manually changing price labels on shelves. This manual process not only consumes valuable time and resources but also increases the likelihood of errors, leading to potential inaccuracies in pricing and a less streamlined shopping experience for customers. The proposed solution is the implementation of an Electronic Shelf Label (ESL) system that automatically displays the prices of goods on retail business shelves. This system connects a website (front end and back end) to e-paper via a Wi-Fi network and microcontroller, allowing retail business owners to update prices more easily. Additionally, buyers can search for desired items through the website. The results are the time to send data from the website to the e-paper, namely, to know the performance of the e-paper used. The average time required from 10 attempts to send data from the website to the e-paper is 20.935 seconds. Since the data is connected to the server and will be updated automatically, using this system will be more efficient than manually changing the price label, although it takes time to transfer data from the website to the e-paper.

关键词： Electric potential Microcontrollers Pricing Manuals Information management Servers Wireless fidelity Faces Business

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Emotion Recognition based on Heart Rate Detection using Logistic Regression for Automation Food Healing Application 8

Emotion Recognition based on Heart Rate Detection using Logi...

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8th International Conference on Education and Technology, ICET 2022

作者： Antonio, Nicholas Jason Nobel, Yosse Ricardo Ho, Hwi-Chie Astuti, Winda Bina Nusantara University Binus Aso School of Engineering Automotive and Robotics Program Computer Engineering Department Jakarta Indonesia Bina Nusantara University Industrial Engineering Department Jakarta Indonesia

ISBN: (数字)9781665492140

ISBN: (纸本)9781665492140

Daily pressure activity especially in work place or any society condition may causing stress or pressure for the people deal with that condition. This stress may causing of decreasing of work performance and achievement in the daily activity. Furthermore in worse condition, people may went to psychiatrist consulting regarding the condition in order to heal the stress or pressure feeling. In this work, food recommendation healing in order to improve the mood based on mood recognition using heartbeat rate detection system is developed. Mood detection results will be used to recommend foods that can improve the mood. the mood detection system work based on automatic intelligent heartbeat rate identification. This system installed on an Android cellphone, so its make the system more flexible and portable. the system has also connected to the web, in order used by doctors to monitor the patient's condition if the patient is being monitoring due to health mental diseases treatment. This system has worked properly with mood detection based on heartbeat rate with 96% and 97%, training and testing accuracy, respectively. Furthermore, this system is connected to the website for monitoring purposes for the psychiatrist to monitor their patient intern of mood changing fluctuation © 2022 IEEE.

关键词： Data handling

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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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Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review

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Operations Research Forum 2024年第3期5卷 58页

作者： Wei, Li Solihin, Mahmud Iwan Saruchi, Sarah ‘Atifah Astuti, Winda Hong, Lim Wei Kit, Ang Chun School of Computer Science and Technology (School of Software) Guangxi University of Science and Technology Guangxi 545006 China Faculty of Engineering Technology and Built Environment UCSI University Kuala Lumpur 56000 Malaysia Faculty of Manufacturing and Mechatronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan 26600 Malaysia Computer Engineering Department Automotive and Robotics Engineering Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

High-precision cylindrical parts are critical components across various industries including aerospace, automotive, and manufacturing. Since these parts play a pivotal role in the performance and safety of the systems they are integrated into, they are often subject to stringent quality control measures. Defects on the interior and exterior wall surfaces of these cylindrical parts can severely undermine their function, leading to degraded performance, increased wear, and even catastrophic failures in extreme cases. This article aims to comprehensively summarize the task definition, challenges, mainstream methods, public datasets, evaluation metrics, and other aspects of surface defect detection for high-precision cylindrical parts, in order to help researchers quickly grasp this field. Specifically, the background and characteristics of industrial defect detection are first introduced. Owing to the unique geometric features of cylindrical part surfaces, algorithms and equipment for image data acquisition used in surface defect detection are elaborated in detail. This article presents an extensive overview of state-of-the-art surface defect detection techniques designed for high-precision cylindrical components, all rooted in deep learning. The methods are systematically classified into three main categories: fully supervised, unsupervised, and alternative approaches, based on their data labeling strategies. Additionally, the paper conducts a comprehensive analysis within each category, shedding light on their unique strengths, limitations, and practical use cases. Concluding the discussion, the paper provides insights into future development trends and potential research directions in this field that will lead to manufacturing innovation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Anomaly detection Computer vision Deep learning Defect detection High-precision cylindrical parts Image processing Optical illumination

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Development of Detection Lung Disease Stand Alone System Based on Convolutional Neural Network (CNN)

Development of Detection Lung Disease Stand Alone System Bas...

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International Conference of Computer and Informatics engineering (IC2IE)

作者： Edgar Caesar Liewarnata Yosica Mariana Sofyan Tan Suryadiputra Liawatimena Winda Astuti Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Product Design Program Industrial Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

Lung disease, especially Tuberculosis (TBC), placed the highest death rate in Indonesia. Tuberculosis (TB) in Indonesia is ranked second after India. Therefore, it is important to reduce or early detection of the lung disease, to prevent this disease and speed up handling. The system can recognize the disease lung identification, and the system applied as standalone system. In this work, the Convolutional Neural Network (CNN) approach for identifying diseases lung identification is proposed. The Mel Frequency Cepstral Coefficient (MFCC) applied to process the stethoscope sounds which will used as input to the CNN. The performance of the proposed system has been investigated and resulted. The accuracy of 99% and 98%, for training and testing accuracy respectively. Furthermore, the system accurately detects lung diseases identification.

关键词： Training Accuracy Tuberculosis Pulmonary diseases Lungs Stethoscope Recording Convolutional neural networks Mel frequency cepstral coefficient Testing

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Simulation of Data Acquisition System Using SimulIDE, Virtual Serial Port and Python

Simulation of Data Acquisition System Using SimulIDE, Virtua...

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International Conference on Education and Technology (ICET)

作者： Muhammad Nurul Puji Winda Astuti Computer Engineering Department Automotive and Robotics Engineering Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

Data acquisition systems are commonplace in the current era of digitalization and Industry 4.0. These systems are typically used to obtain data from sensors, whether analog or digital, which is then stored for processing in subsequent stages. However, the implementation of hardware for creating data acquisition systems can be challenging due to constraints such as laboratory facilities, budget limitations, and other factors. Therefore, in this research, SimulIDE simulation software was employed to simulate electronic components and the Arduino Uno. To make this virtual Arduino appear as a physical one to the host computer, PyVirtualSerialPorts was used as a virtual serial port. The data acquisition system was created using Python programming with several key features: it can store data in CSV format, present data plots and graphs, and display the frequency spectrum of signals. The system achieved a sampling speed of 1000 data points per second, with input signals limited to a maximum of 10 Hz. This study also shows that signal distortion can occur due to excessively frequent data plotting and Fast Fourier Transform (FFT) processing. Therefore, the use of SimulIDE simulator, virtual serial port, and Python can be utilized in the learning process of a simple data acquisition system.

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Analyze and Predict Car Accidents Using Different Machine Learning Algorithms

Analyze and Predict Car Accidents Using Different Machine Le...

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International Conference on Information Management and Technology (ICIMTech)

作者： Farrell Putra Harimanto Chris Andrew Henry William Muhammad Zacky Asy'ari Samuel Albert Artanto Muhammad Nurul Puji Computer Engineering Department Automotive & Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

Traffic accidents can occur from various factors, ranging from the driver themselves to problems with the vehicle. Road and traffic accidents are the most significant factor of fatalities in the world. Most of the causes of accidents are the drivers themselves. Poor physical and mental conditions can significantly impact how to drive. The driving experience is also important; the longer the driver has been driving, the more professional he will be, and vice versa. Smart City solutions can predict the severity of accidents and give information to the authorities in order to reduce the number of fatalities. This paper investigates the factors that may lead to an accident on the road. By looking at data collected from traffic-related accidents and then visualizing it to make it easy to analyze; then, from this data, we will predict the causes of traffic accidents so they can be avoided. The best algorithm based on accuracy, MAE and RMSE is Random Forest Classifier with 0.8461, 0.1663 and 0.4376, respectively.

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