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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Setiawan, Vionna Thendyliputra, Raynaldo Santami, Sherly Angelina Hansen, Daniel Warnars, Harco Leslie Hendric Spits Ramdhan, Arief Doucet, Antoine School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Telkom University Bandung40257 Indonesia Universite de la Rochelle Laboratoire L3i France

ISBN: (纸本)9798350397284

Disability is a complex problem affecting many lives in Indonesia, especially people with hearing impairments. Disability is also a complex phenomenon, starting from the type of disability, degree of disability, and age of persons with disabilities to interactions and environmental impacts on disability. Moreover, there are many testimonies from around the world that people with disabilities are more likely to be weak and less likely to get an education. This paper aims to present a mobile application that offers sign language images and videos of any text in a learning environment that is shaped like a dictionary module to increase their vocabulary, learn a new sign language, and pronounce a word. This mobile application is specially designed for deaf people who have limitations in learning and educational training. This mobile application is equipped with several features such as user registration for the deaf and normal people who like to learn sign language, discussing through forums, using a sign language dictionary containing sign language text and pictures, using interactive learning videos, registering for an event activity, including giving input, and also make a donation. © 2023 IEEE.

关键词： Environmental impact

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Mobile Application to Help Abandoned Pets

Mobile Application to Help Abandoned Pets

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Blockchain and Distributed systems Security (ICBDS), IEEE International Conference on

作者： Salicca Dewi Rahajeng Siti Julianingsih Nurfitriyani Zakin Izzani Akbar Calvin Leonardo Harco Leslie Hendric Spits Warnars Laurens Spits Warnars Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

As social beings, humans like to interact with each other, including other creatures of God, such as animals, and keep them as pets. Many pets, such as dogs, cats, birds, fish, rabbits, and so on, include unusual pets such as reptiles, pigs, insects, etc. As a result of the lack of human awareness of pets and the nature of keeping animals, which is only for fun, it is not soulful to keep animals. So when people are bored, they abandon their pets, which causes new problems in human life today. In this case, we created a mobile application that can be shared with pet lovers where they can share information about abandoned pets and the possibility of owning pets from these abandoned pets. Mobile applications were designed using use case diagrams to show business processes, class diagrams to show database models, and user interfaces to present a similar interface to users. This mobile application will become a platform that can gather all people who care about pets and make pets a part of humans.

关键词： Rabbits Animals Insects Entertainment industry User interfaces Media Fish Mobile handsets Mobile applications Security

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AI-Powered Precision: Revolutionizing Skin Cancer Detection with Advanced Machine Learning 6

AI-Powered Precision: Revolutionizing Skin Cancer Detection ...

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6th IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2024

作者： Abd-Elaziz, Mariam M. Elshafie, Basma M. Abdullah, Hagar Gerges, Karen Y. Ghanem, Ingy A. Fouad, Khaled M. El-Ghamry, Amir Moawad, Ibrahim F. El-Metwally, Sara New Mansoura University Faculty of Computer Science and Engineering Dakahlia Egypt Mansoura University New Mansoura University Faculty of Engineering Computer and Communication Program Faculty of Computer Science and Engineering Egypt Benha University Information Systems Faculty of Computers and Artificial Intelligence Egypt Mansoura University New Mansoura University Faculty of Computer and Information Sciences Faculty of Computer Science and Engineering Egypt School of Engineering and Computer Science University of Hertfordshire Gaf Egypt Ain Shams University Faculty of Computer and Information Sciences Cairo Egypt College of Computer Science and Engineering Taibah University Department of Artificial Intelligence Medina42353 Saudi Arabia

ISBN: (纸本)9798350378511

Skin cancer's increasing incidence rates necessitate advanced diagnostic tools. This research uses MobileNet architecture to develop an enhanced system for skin cancer detection. MobileNet's efficient CNN architecture, combined with Keras for model lifecycle management and TensorFlow for real-time inference, forms the foundation of this study. Using 10,015 dermatoscopic pictures from seven different classifications of skin cancer in the HAM10000 dataset, the methodology includes several key stages: image preprocessing to correct illumination and adjust resolution, data augmentation to balance the dataset, and model training using Transfer Learning. The MobileNet model was trained over 50 epochs with a comprehensive architecture incorporating multiple specialized layers. By combining large-scale data analysis and adaptive learning, this methodology demonstrates The revolutionary possibilities of AI and ML in improving skin cancer diagnostics and public health outcomes. Keywords Skin Cancer Detection, Dermatoscopic Images, Machine Learning, Deep Learning © 2024 IEEE.

关键词： Adversarial machine learning

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Deaf Helper Mobile Application for Interaction of Hearing Disorders Communities 2

Deaf Helper Mobile Application for Interaction of Hearing Di...

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Setyawan, Adrian Naphan, Gilbert Xervaxius Dynata, Kevin Friry, Jovano Edmund Warnars, Harco Leslie Hendric Spits Bina Nusantara University School of Information System Information Systems Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665400527

People with hearing loss in this world have not received much serious attention from the authorities. This makes these sufferers confused in choosing learning media to interact with and isolated from their social environment. This application was created to help people with hearing loss to be noticed and understood from the way they communicate using sign language through the mobile application called Assistant for the Deaf, which has many features such as registration, interactive videos, sign language translator, forums, customer service, library, information, history, events, donations, and shops. The application is designed using use case diagrams and class diagrams modeling the database, and the implementation used Android Studio and MySQL database. © 2022 IEEE.

关键词： Audition

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A Design Study on Integrated Complex Coefficient Filters with Peripheral Circuits for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and systems 2025年第1期145卷 16-24页

作者： Shouno, Kazuhiro Sato, Lina Fukagawa, Kanta Tanimoto, Hiroshi Muto, Cosy Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan

This paper proposes a measurement technique for an integrated complex filter. The proposed method is based on two measurement methods with integrated circuitry for calibration. It is accomplished by applying square waves to an FUT (Filter Under Test) instead of sinusoidal waves or by superposing transfer characteristics obtained by applying a single sinusoidal wave to an FUT. In both of the above methods, the output signal connected to the external measurement equipment via a 50 Ω coaxial cable and so on. In this case, the output signal of the FUT should be amplified by internal or external amplifiers, which degrades the response. However, it is cancelled out by the proposed calibration circuitry with some analog switches. In addition to the above, for the RC polyphase filter, the source resistance and the load capacitance degrade the measurement results and their effect is discussed. The validity of the proposal is confirmed through computer simulation for a third-order RCPF whose passband is 8–40 MHz fabricated by Phenitec 0.6 µm CMOS process. It is shown that the measurement error is −1.10 dB for the square-wave method and −1.00 dB and −0.43 degrees for the superposition method at 40 MHz. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Capacitance

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Mammogram Breast Cancer Classification Based on Deep-Convolutional Neural Network

Mammogram Breast Cancer Classification Based on Deep-Convolu...

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2023 International Seminar on Application for Technology of information and Communication, iSemantic 2023

作者： Amadea, Avissa Aurellia Sari, Christy Atika Rachmawanto, Eko Hari Sari, Wellia Shinta Noori, Mohammed S. Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Information Systems Semarang Indonesia Northern Technical University Department of Computer Engineering Technology Mosul Iraq National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

Based on WHO's data, breast cancer is one of the most deadly diseases that has claimed many victims, especially women. This disease begins with the presence of an undetected and eventually turns into malignant (cancer). This happens due to ignorance of the importance of having a medical check-up even though in good health. Doctors and researchers can prevent the development of tumor cells through treatment that begins with radiological examinations to identify the possibility of a person being affected by this disease. One of the most frequently used techniques is Mammography. This technique can detect the presence of tumor cells using advanced technology and several methods in displaying the patient's diagnostic results on low-dose X-rays in the form of mammogram images. The technology is inseparable from the methods used to identify the presence of tumor cells. In this study, we have proposed the CNN method based on the deep-CNN model to identify mammogram images in the detection of breast cancer cells with average evaluation results in terms of accuracy, precision, recall, specificity, and f-measure on mammogram image datasets of 99.52%, 99.72%, 99.31%, 99.72%, and 99.5%. These results showed that this method has a good performance in breast cancer detection. © 2023 IEEE.

关键词： Diseases

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Performance Variations in Digital Talent: Gender-Based Insights from STEM and AI Education Backgrounds

Performance Variations in Digital Talent: Gender-Based Insig...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Erwin Halim Azani Cempaka Sari Irma Kartika Wairooy Muhamad Keenan Ario Said Achmad Devi Fitrianah Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

In the digital era, digital talent development has become increasingly vital for organizations and economies to thrive. While much focus has been placed on technical skills, this report emphasizes the often-overlooked role of soft skills in developing digital talent. Through an extensive literature review and data analysis, the study identifies that soft skills are crucial in enhancing digital talent performance. This research explores the factors that influence Digital Talent Performance in Indonesia, specifically comparing the performance levels of male and female digital talents. Data were collected from college students in the Jabodetabek area (cities surrounding Jakarta, Indonesia) using a purposive sampling method, with an online questionnaire distributed to 378 respondents. The study utilized Structural Equation Modeling (SEM) to analyze the collected data using SmartPLS 4.1. software. Key variables examined include Communication Skills, Teamwork, Emotional Intelligence, Academic Achievement, Problem-Solving, Digital Readiness, Digital Skills, Digital Technology, and Digital Talent Performance. Out of the nine hypotheses tested, one was not statistically significant. The results suggest that a combination of both technical and soft skills significantly affects digital talent performance, with important gender-based differences in performance levels emerging from the analysis. This research contributes to understanding Indonesia's complex dynamics of digital talent development.

关键词： Numerical analysis Education Urban areas Sampling methods Mathematical models Software Teamwork Problem-solving Artificial intelligence Systematic literature review

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CAMRI Loss: Improving the Recall of a Specific Class without Sacrificing Accuracy

CAMRI Loss: Improving the Recall of a Specific Class without...

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作者： Nishiyama, Daiki Fukuchi, Kazuto Akimoto, Youhei Sakuma, Jun Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba-shi305-8577 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Department of Computer Science Faculty of Engineering Information and Systems University of Tsukuba Tsukuba-shi305-8577 Japan

In real world applications of multiclass classification models, misclassification in an important class (e.g., stop sign) can be significantly more harmful than in other classes (e.g., no parking). Thus, it is crucial to improve the recall of an important class while maintaining overall accuracy. For this problem, we found that improving the separation of important classes relative to other classes in the feature space is effective. Existing methods that give a class-sensitive penalty for cross-entropy loss do not improve the separation. Moreover, the methods designed to improve separations between all classes are unsuitable for our purpose because they do not consider the important classes. To achieve the separation, we propose a loss function that explicitly gives loss for the feature space, called class-sensitive additive angular margin (CAMRI) loss. CAMRI loss is expected to reduce the variance of an important class due to the addition of a penalty to the angle between the important class features and the corresponding weight vectors in the feature space. In addition, concentrating the penalty on only the important class hardly sacrifices separating the other classes. Experiments on CIFAR-10, GTSRB, and AwA2 showed that CAMRI loss could improve the recall of a specific class without sacrificing accuracy. In particular, compared with GTSRB's second-worst class recall when trained with cross-entropy loss, CAMRI loss improved recall by 9%. © 2023 The Institute of Electronics, information and Communication Engineers.

关键词： Deep learning

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Analysis Implementation of Chatbots to Increase Knowledge of Stunting Prevention in Indonesian Society with Bibliometric 9

Analysis Implementation of Chatbots to Increase Knowledge of...

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9th International Conference on information Management and Technology, ICIMTech 2024

作者： Sutedja, Indrajani Handayani, Lutfi Maryuni Hendry Faisal, Mohammad Reza School of Information Sytems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Midwifery Study Program Binawan University Faculty of Nursing and Midwifery Jakarta13630 Indonesia Satya Wacana Christian University Faculty of Information Technology Salatiga50711 Indonesia FMIPA Lambung Mangkurat University Department of Computer Science Banjarmasin70123 Indonesia

ISBN: (纸本)9798350390025

The healthcare industry uses AI, because it is more efficient. In Indonesia, the accuracy of measurements and transmission of children's growth and development is often wrong. Many small hospitals usually don't have length measuring equipment, so the results are inaccurate. This research aims to determine the effectiveness of the stunting prevention program in Indonesia using an information system. The methodology used is a qualitative method, to conduct subjective research and analysis. The results of the research are to improve the website/mobile application information system which can display a child's growth and development history and simple educational information related to nutrition. Digital recording methods can speed up the process of updating data with a central database in real time. The value of this research is to reach the public to utilize technology and obtain personalized and interactive information directly to individuals. The theoretical implications of this research focus on information dissemination and understanding how chatbots influence knowledge acquisition and behavior change in the context of stunting prevention can contribute to advancing these theories in the field of public health. Managerial implications can provide cost effectiveness as Chatbots offer potentially cost-effective solutions and data-driven insights where managers can leverage chatbot analytics. © 2024 IEEE.

关键词： Artificial Intelligent Indonesian information System Stunting stunting stunting chatbot and bibliometric stunting prevention VOS Viewer

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Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

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computers, Materials & Continua 2023年第6期75卷 5255-5270页

作者： JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Safa Alsafari Ayman Yafoz Romany F.Mansour Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080002Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Department of Computer Science and AI Faculty of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and *** radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer *** lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is *** current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on *** data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s ***,the OSDL model is applied to classify the CXRs under different severity levels based on CXR *** learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the *** model,applied in this study,was validated using the COVID-19 *** experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

关键词： Artificial intelligence chest X-ray COVID-19 optimized synergic deep learning preprocessing public health

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