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International Conference on ICT For Smart Society (ICISS)

作者： Doni Purnama Alamsyah Doni Morika Boby Siswanto Norfaridatul Akmaliah Othman Entrepreneurship Department BINUS Business School Bina Nusantara University Jakarta Indonesia Design Interior Study Program School of Design Bina Nusantara University Jakarta Indonesia Computer Science Study Program School of Computer Science Bina Nusantara University Jakarta Indonesia Department of Technology Management Faculty of Technology Management and Technopreneurship Universiti Teknikal Malaysia Melaka Melaka Malaysia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

The Learning Management System (LMS) is a component of the Smart Campus and is implemented with e-learning. E-learning can be effective if it gives satisfaction to the user and improves academic performance. Therefore, it is important to evaluate online learning users to support learning outcomes. The purpose of this study is to examine the relationship model of individual performance with support for user satisfaction and flexibility in an e-learning environment. This study was carried out using a survey to 615 e-learning users. Users are among the universities students in the city of Bandung (Indonesia). Data were collected quantitatively through questionnaires distributed online. Data processing is done with SmartPLS, where there are two model tests, namely the PLS Algorithm and Bootstrapping. Results of this study model test found that user satisfaction can be increased by information quality from e-learning. Online learning has perceived flexibility that is assessed by users and can increase user satisfaction and individual performance. It was found that user satisfaction was able to support the individual performance achievements of students as e-learning users. The results from this study provide valuable insights into the role of user satisfaction as a mediator in controlling individual student performance, highlighting the significance of integrating Smart Campus solutions.

关键词： Surveys Learning management systems Electronic learning Urban areas Education Distributed databases Data processing Data models Information integrity

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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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New Media as a Tools to Improve Creative Thinking: A Systematic Literature Review

New Media as a Tools to Improve Creative Thinking: A Systema...

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International Conference on Information technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Hellik Hermawan Fajar Mahardika Irma Darmayanti R. Bagus Bambang Sumantri Dhanar Intan Surya Saputra Afrig Aminuddin Department of Informatics Faculty of Computer Science Universitas Amikom Purwokerto Banyumas Jawa Tengah Indonesia Computer Technology Study Program Faculty of Science and Technology Institut Teknologi dan Sains Nahdlatul Ulama Pekalongan Indonesia Department of Information technology Faculty of Computer Science Universitas Amikom Purwokerto Banyumas Jawa Tengah Indonesia Information Systems Study Program Faculty Of Science and Technology Universitas Harapan Bangsa Indonesia Faculty of Computer Science Universitas Amikom Yogyakarta Sleman Indonesia

This systematic literature review (SLR) study aims to analyze the role of New Media as a Tool to Improve Creative Thinking, with relevant articles from 2018 to 2023 taken from reputable international journals. It uses three research questions (RQ) to explore New Media's relevance in stimulating creativity. The results indicate that interactive platforms such as social media and online collaboration tools positively influence creative thinking skills. Interaction through New Media allows individuals to share ideas, discuss, and be exposed to various points of view, all of which stimulate creative thinking. In addition, New Media also facilitates the creative process of solving problems and generating original ideas. The immersive experiences offered by this technology can enhance the exploration of ideas and increase the ability to think out-of-the-box. While New Media offers great potential to enhance creativity, there are also potential risks, such as false information, media addiction, and privacy concerns. Therefore, awareness of the wise use of New Media needs to be increased, especially in education and everyday life. Overall, this literature review provides in-depth insight into the role of New Media in enhancing creative thinking skills and driving innovation. With the right approach, New Media can be an effective tool in stimulating creative thinking and encouraging the creation of original ideas beneficial for future social and technological developments.

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Twenty years of e-learning in health science: A bibliometric

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Procedia computer Science 2023年 216卷 604-612页

作者： Fairuz Iqbal Maulana M. Aldiki Febriantono Dwi Risza Budi Raharja Khaeruddin Ridho Herasmara Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Indonesia Electrical Engineering Faculty of Science and Technology Raden Rahmat Islamic University Malang Indonesia

Education about health sciences has historically been limited in the curriculum of health professionals and largely inaccessible to the public. In practice, most of the health science education is still running conventionally. Supposedly with the advancement of technology and the use of the internet everywhere, learning such as e-learning can be important, especially in the health sector. Until this research was conducted, only 514 academic documents about e-learning in health sciences were found for 20 years from 2001 to 2020, obtained in searching on the Scopus database. This study presents a comprehensive overview of studies related to E-learning in the Health Sciences sector. This study uses bibliometric analysis and indexed digital methods to map scientific publications throughout the world. This research employs the Scopus database to gather information, as well as the Scopus online analysis tool and Vosviewer to show the bibliometric network. The method consists with five stages: determining search keywords, initial search results, refinement of search results, initial compilation, and data analysis. Among the most published and indexed articles by Scopus, papers published by researchers in the United States have the highest number of publications (80), followed by United Kingdom (63) and Australia with 45 academic publications. The processed data shows the pattern and trend of increasing the number of international publications in E-learning in Health Sciences field, which Scopus index.

关键词： e-learning health science bibliometric vosviewer education

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Javanese Script Single Letters Classification using GoogLeNet Architecture and Adam Optimizer Based on Convolutional Neural Networks (CNN)

Javanese Script Single Letters Classification using GoogLeNe...

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International Seminar on Application for technology of Information and Communication (ISemantic)

作者： Ajib Susanto Christy Atika Sari Ibnu Utomo Wahyu Mulyono Eko Hari Rachmawanto De Rosal Ignatius Moses Setiadi Rabei Raad Ali Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department of Computer Engineering Technology Northern Technical University Mosul Iraq

This study examines the decline in the usage of the Javanese language, which has experienced a decrease in the number of speakers from approximately 82 million in 2007 to 68.2 million in 2015. The convergence of the Javanese script and Optical Character Recognition (OCR) technology is proposed as a solution, allowing for the preservation and accessibility of the Javanese script in the digital age. The integration of Convolutional Neural Networks (CNNs) in Javanese script classification achieved a high accuracy rate of 92.95% in identifying positive and negative cases. The dataset used for training consisted of 8440 sample images, which were divided into 20 subfolders for training and testing. The results presented in Table IV demonstrate the successful implementation of the classification model, achieving a 98.87% sensitivity, 100% precision, and 98.88% specificity. This research contributes to the preservation and understanding of the culturally significant Javanese script while addressing the decline in its usage.

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AlexNet Architecture Based Convolution Neural Network for Realtime Audio to Text Translator of Bisindo Hand Sign

AlexNet Architecture Based Convolution Neural Network for Re...

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International Seminar on Application for technology of Information and Communication (ISemantic)

作者： Dhiki Sujatmiko Christy Atika Sari Eko Hari Rachmawanto Andi Danang Krismawan Bilal R. Altamer Mohamed Ayad Alkhafaji Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Study Program in Animation University of Dian Nuswantoro Semarang Indonesia Department of Computer Science Mosul University Presidency University of Mosul Mosul Iraq Department of Computer Science National University of Science and Technology DhiQar Iraq

Deafness is a condition that results in the loss of hearing function, hindering the reception of information such as oral communication that relies on auditory senses. Consequently, individuals with hearing impairment experience communication barriers and may have limited or no ability to respond. One solution is the use of sign language. In Indonesia, there are two known sign languages: Sibi and Bisindo. Both serve the same function but differ in their style of movement and expression. Bisindo is considered more flexible as it conveys meaning based on the Indonesian language. However, the universal understanding of this language solution is still limited among many people. Therefore, a program is needed to facilitate translation between deaf individuals who use sign language and their counterparts who do not communicate through sign language. CNN (Convolutional Neural Network) is a deep learning algorithm used for training visual input data recognition by computer systems. There are various CNN-based architectures, and one of them is AlexNet. Based on the author's testing, the AlexNet architecture proves to be suitable for real-time sign language translation. The evaluation of the system involved 7,800 datasets and 520 testing instances, with an average accuracy of 468 correct translations. When averaged, the system achieved a 90% accuracy rate, representing a 100% increase in accuracy compared to previous approaches.

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification 19

Deep Learning in Smart Healthcare: A GAN-based Approach for ...

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19th International Conference on Future Networks and Communications, FNC 2024 / 21st International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2024 / 14th International Conference on Sustainable Energy Information technology, SEIT 2024

作者： Tufail, Hina Ahad, Abdul Puspitasari, Ira Shayea, Ibraheem Coelho, Paulo Jorge Pires, Ivan Miguel Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga Indonesia School of Software Northwestern Polytechnical University Shaanxi Xian China Turkey Polytechnic University of Leiria Leiria Portugal Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which affects patients' lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD. © 2024 The Authors. Published by Elsevier B.V.

关键词： Generative adversarial networks

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Integration of Artificial Intelligence in Software Defined Networking technology through Developing Machine Learning Algorithms 3

Integration of Artificial Intelligence in Software Defined N...

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3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Naibaho, Lamhot Saxena, Pulkit Raj Sharma, Kuldeep Alfurhood, Badria Sulaiman Pallavi, L. Pant, Bhasker Faculty of Letters and Languages Christian University of Indonesia English Language Education Study Program Indonesia Teer Thanker Mahaveer University College of Computing Science and Information Technology Uttar Pradesh Moradabad India Lovely Professional University Department of Computer Science and Engineering Punjab Jalandhar Phagwara India College of Computer Information Sciences Princess Nourah Bint Abdulrahman University Department of Computer Sciences Saudi Arabia B v Raju Institute of Technology Cse Telangana Narsapur India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

A future networking design called "software-defined networking"combines network programmability with centralized administration (SDN). Network administration is currently handled by SDN, which, at the regulator, which acts as the cable network processor and divides the data plane into a control plane and a data aircraft. Recent breakthroughs in artificial intelligence (Al) have shown a stronger inclination for the science community to benefit from their ability to give learning capabilities and enhance Indicator. The author of this research provides a comprehensive analysis of initiatives underway to incorporate Al with SDN. The study concluded that the three primary Al thread where scientific research was centered were computer science, conceptual, and fuzzy reasoning systems. The authors of this paper explore the several fields in which these approaches may be used, their potential future applications, and the innovations made possible by the integration of AI-based methods into the SDN paradigm. By choosing the right SDN controller, any large organization may lower network complexity, implementation expenses, and maintenance costs. The focus of this article is software defined networking's benefits and downsides (SDN). Following is a basic explanation of artificial intelligence and a list of some of its most significant applications in SDN. © 2023 IEEE.

关键词： Software defined networking

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Implementation of Malaria Detection Using MobileNetV2 Pretrained Model for Improved Deep Learning Based Diagnostics

Implementation of Malaria Detection Using MobileNetV2 Pretra...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Rahmat Hidayat Achmad Solichin Adhitia Erfina Sudin Saepudin Muhamad Muslih Mohd Zainuri Saringat Master of Computer Science Budi Luhur University Jakarta Indonesia Information Systems Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Tun Hussein Onn Malaysia University

ISBN: (数字)9798331529376

ISBN: (纸本)9798331529383

The Plasmodium parasite, which causes malaria, is an acute fever illness that infects people when a female Anopheles mosquito bites them. It is predicted that malaria would claim 619,000 lives in 2021, with 96% of those deaths occurring in the African continent. We can achieve this by using a microscope to examine thick and thin blood smears. The proficiency of a microscope examiner is crucial for doing microscopic examinations. Consider how time-consuming, ineffective, and costly it would be to examine thousands of malaria cases. Consequently, Creating an automated method for detecting malaria parasites is the aim of this study. We employ a MobileNetV2 pretrained model with CNN technology. Because it has been trained on dozens or even millions of data points, this pretrained model is incredibly light but dependable. There are two main benefits of automatic malaria parasite detection: firstly, it can offer a more accurate diagnosis, particularly in locations with limited resources; secondly, it lowers diagnostic expenses. The optimizer utilizes Adam Weight, the criteria uses NLLLoss, and the model is trained using 32 for batch_size. In the fourteenth epoch, we obtained the maximum accuracy score of 96.26% based on the training data. The outcomes of the predictions demonstrate how excellent this score is. EfficienceNet, DenseNet, AlexNet, and other pretrained models are among the alternatives that scientists are advised to try training with.

关键词： Training Adaptation models Accuracy Costs Malaria Microscopy Data visualization Training data Cost function Data models

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Early Detection and Classification of Melanoma Based on Android MobileNet V2 Convolutional Neural Network

Early Detection and Classification of Melanoma Based on Andr...

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International Seminar on Application for technology of Information and Communication (ISemantic)

作者： Jasmine Iswarini Dyah Prabandari Christy Atika Sari Eko Hari Rachmawanto Candra Irawan Bilal R. Altamer Mohamed Ayad Alkhafaji Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Study Program in Information Systems University of Dian Nuswantoro Semarang Indonesia Mosul University Presidency University of Mosul Mosul Iraq Department of Computer Science National University of Science and Technology DhiQar Iraq

Melanoma is a malignant form of cancer that affects the skin and has a particularly high mortality rate, so it requires early detection to increase the level of safety for users. Diagnosis and detection of skin cancer are usually done through manual screening and visual inspection. This process requires a long time, has high complexity, is subjective, and is prone to errors. CNN is one of the algorithms with advantages in accurate classification. In this research, early detection and classification of melanoma cancer were carried out based on two classes, namely benign and malignant using the Convolutional Neural Network method. Our proposed method yields an accuracy of 81.11% for the validation data. The accuracy results obtained can be improved by using more datasets and increasing the number of layers used. This study uses the CNN method using MobileNet V2 architecture to detect melanoma skin cancer. The class used is benign and malignant.

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