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International Conference on Wireless and Telematics (ICWT)

作者： Muhammad Taufik Dwi Putra Anugrah Adiwilaga Jezzy Putra Munggaran Muhammad Azka Adhitama Raihan Anwar As'Ad Ahmad Ali Alhafidz Rin Rin Nurmalasari Agus Juhana Computer Engineering Study Program Universitas Pendidikan Indonesia Bandung Indonesia Department of Electrical Engineering UIN Sunan Gunung Djati Bandung Indonesia Multimedia Education Study Program Universitas Pendidikan Indonesia Bandung Indonesia

ISBN: (数字)9798350377057

ISBN: (纸本)9798350377064

This article explores the development and implementation of futuristic bins with automated waste sortation systems to address improper and mixed waste disposal challenges. Despite the existence of categorized waste bins, individuals often dispose of waste improperly, hindering the sorting and decomposition processes. Integrating advanced technologies, such as Artificial Intelligence (AI), futuristic bins offer innovative solutions. Futuristic bins with automated sorting systems facilitate accurate waste sorting, reduce mixing, and optimize waste disposal. These bins increase efficiency, reduce costs, and optimize collection routes while promoting environmental cleanliness and awareness of waste management. The article describes the research methodology, including preliminary study, specification requirements analysis, prototype development, and testing. The results demonstrate the implemented system's accuracy and real-time object detection capabilities. The study also acknowledges limitations and suggests future research directions, including improving communication between object detection algorithms and microcontrollers. Overall, the development of futuristic bins with automated waste sortation systems represents a significant advancement in waste management, with the potential to revolutionize waste management systems and create sustainable cities and communities.

关键词： Accuracy Costs Urban areas Prototypes Object detection Real-time systems Waste disposal

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Multilayer processing architecture of RAM based neural network with memory optimization for navigation system

Multilayer processing architecture of RAM based neural netwo...

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Joint International Conference on Rural Information & Communication Technology and Electric-Vehicle Technology (rICT & ICeV-T)

作者： Ahmad Zarkasi Aciek Ida Wuryandari Rizki Fauzian Study Program Computer System Sriwijaya University South Sumatera Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

Robots also have been trusted to help human to complete difficult jobs, for example, finding for the earthquake, a fire, or a sinking ship victims. The robot must be reliable, clever and moving automatically. The aim of this study is to develop and apply the application of artificial RAM-based neural networks (WNNs) on a mobile robot using a multilayer processing architecture with memory optimizations on to address and input pattern, so that producing smart navigation model which it has a simpler computational load and faster execution time. The gained result from the first study was the percentage of memory optimization in the amount of 50%. This result obtained from the formerly RAM using 8 bit data width has been optimized to 4 bits. Both of the percentage of data optimization pattern is 93.75%. This percentage is obtained from the optimization pattern (pattern taken is 4 bits MSB), each 1 bit data can handle 15 unseen patterns.

关键词： Random access memory Optimization Sensors Nonhomogeneous media Mobile robots Decoding Biological neural networks

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Challenges on Cloud Computing Migration Strategy for Music Industry: A Systematic Literature Review

Challenges on Cloud Computing Migration Strategy for Music I...

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International Conference on informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Hijir Della Wirasti Henki Seta Deden Witarsyah Hamonagan K Prabu Ayu Azzahro Bayu Hananto Faculty of Engineering Industry Telkom University Bandung Indonesia Informatics Study Program Computer Science Faculty Universitas Pembangunan Nasional Veteran Jakarta

Cloud computing has emerged as a transformative technology that offers numerous benefits to various industries, including the music industry. Cloud computing has revolutionized the way businesses operate and has had a significant impact on various industries, including the music industry Cloud computing, with its scalability, flexibility, and cost-effectiveness, presents an opportunity for the music industry to leverage technology to enhance its operations and meet the evolving demands of the digital era. This paper presents a systematic literature review on cloud computing migration strategies specifically tailored for the music industry. The review aims to identify existing research, frameworks, and best practices related to cloud migration in the music industry, as well as highlight the challenges and opportunities associated with such migrations. Through an analysis of relevant literature, this study provides valuable insights to assist music industry stakeholders in developing effective cloud migration strategies.

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The Scientific Progress and Prospects of Artificial Intelligence for Cancer Detection: A Bibliometric Analysis 24

The Scientific Progress and Prospects of Artificial Intellig...

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24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023

作者： Maulana, Fairuz Iqbal Adi, Puput Dani Prasetyo Lestari, Dian Ariyanto, Sandy Vikki Purnomo, Agung Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia National Research and Innovation Agency Telecommunication Research Center Jakarta11480 Indonesia Universitas Airlangga Faculty of Medicine Surabaya60132 Indonesia University of Madura Informatics Engineering Study Program Pamekasan Indonesia Bina Nusantara University BINUS Business School Undergraduate Program Entrepreneurship Department Jakarta11480 Indonesia

ISBN: (纸本)9798350313956

Using the Scopus database, this study aims to investigate the use of artificial intelligence for cancer detection in the last ten years from 2013 to 2022. The researchers used bibliometric analysis combined with VosViewer and Rstudio software quantification method for literature analysis. The results of the flushed articles show data such as publication year, journal, country, keywords, and authors, to the highest number of citations. Common keywords used by researchers are artificial intelligence, medical, and human. Researchers limited the findings through keywords such as in the last ten years, documents are journals, and English only so that 1868 articles were obtained. The results found that Harvard Medical School affiliation had the highest number of articles, with 101 articles, by subject area, Medicine had a proportion of 41.9% (n=1320). This bibliometric study will be useful for other researchers to examine the development of research on Artificial Intelligence for Cancer Detection in the last ten years. © 2023 IEEE.

关键词： Diseases

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Internet of Medical Things: A Bibliometric Analysis of Research Publications from 2018–2022 4th

Internet of Medical Things: A Bibliometric Analysis of Resea...

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4th International Conference on Electronics, Biomedical engineering, and Health informatics, ICEBEHI 2023

作者： Maulana, Fairuz Iqbal Lestari, Dian Adi, Puput Dani Prasetyo Arifin, Mohammad Nazir Purnomo, Agung Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Faculty of Medicine Universitas Airlangga Surabaya60132 Indonesia Telecommunication Research Center National Research and Innovation Agency Jakarta11480 Indonesia Informatics Engineering Study Program University of Madura Pamekasan Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9789819714629

The field of the Internet of Medical Things (IoMT) is experiencing significant growth as it involves the integration of medical devices and systems with the internet and various digital technologies. This study provides a bibliometric analysis of scholarly articles pertaining to the Internet of Medical Things (IoMT) during the time frame of 2018 to 2022. The analysis is conducted using data sourced from the Scopus database. A total of 1165 documents were identified and analyzed to gain insights into the current state of research in this field. The analysis includes trends in publication output, top authors and institutions, and key research topics. The results of the analysis indicate a steady increase in publication output on IoMT over the past five years, with the highest number of publications in 2022. The top authors in this field were Mohanty, S.P., Kougianos, E., and Pirbhulal, S., while the top institutions were King Saud University, Chinese Academy of Sciences, and University of North Texas. Co-citation analysis revealed that wearable devices, telemedicine, and data analytics were among the key research topics in IoMT. This bibliometric analysis offers useful insights into the present research landscape of the Internet of Medical Things (IoMT) spanning the years 2018 to 2022. The findings can be beneficial for researchers, practitioners, policymakers, and funding agencies in understanding the trends and priorities in this field. The top authors and institutions identified in this analysis can serve as valuable resources for collaboration and staying updated on the latest developments in IoMT. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Digital devices

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Analyzing the Effectiveness of Smartphone Applications on Disease Management and Healthcare Systems

Analyzing the Effectiveness of Smartphone Applications on Di...

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International Conference on Application of Information and Communication Technologies (AICT)

作者： Rabei Raad Ali Wisam Subhi Al-Dayyeni Christy Atika Sari Eko Hari Rachmawanto Candra Irawan Omar M. Jihad Department of Computer Engineering Technology Northern Technical University Mosul Iraq School of Information Technologies and Engineering ADA University Baku Azerbaijan Study Program in Informatics Engineering Faculty of Computer Science University of Dian Nuswantoro Semarang Indonesia Study Program in Information Systems Faculty of Computer Science University of Dian Nuswantoro Semarang Indonesia

ISBN: (数字)9798350387537

ISBN: (纸本)9798350387544

In today’s complex global environments, it is highly challenging to achieve effective healthcare management. The critical challenges faced by healthcare management are being met by the revolution of m-health where mobile cloud computing plays a major role. The taxonomy of mobile computing comprises operational aspects, end-user issues, and service quality and mobility management. The use of smartphone technologies and applications has become a highly significant approach to improving healthcare management. Mobile health services such as mobile pathology, mobile neurosurgery, cancer treatment, and behavioral/psychological disorders are gaining significance where smartphone applications are being used. Portability, flexibility, and convenience are major characteristics of mobile computing that have helped patients and doctors to develop better relationships through coordination and communication. The relationship between smartphone technologies and applications and healthcare management can be understood in several broad aspects. This article aims to analyze the current and future implications of these technologies on healthcare and disease management systems.

关键词： Pathology Cloud computing Taxonomy Cancer treatment Medical services Benchmark testing Neurosurgery Information and communication technology Mobile computing Diseases

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White Bread Mold Detection using K-Means Clustering Based on Grey Level Co-Occurrence Matrix and Region of Interest

White Bread Mold Detection using K-Means Clustering Based on...

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

作者： Qori Alfatah Putra Christy Atika Sari Eko Hari Rachmawanto Nur Ryan Dwi Cahyo Edy Mulyanto Mohamed Ayad Alkhafaji Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department of Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department of Computer Science National University of Science and Technology DhiQar Iraq

This research paper presents a bread mold identification system that utilizes digital image processing techniques, specifically K-means clustering and thresholding, to accurately detect and classify mold-infected areas on bread. The system aims to provide valuable information regarding the safety of consuming mold-contaminated bread, enabling consumers to make informed decisions and reduce the risk of health issues associated with consuming mold-infested bread. the K-means clustering algorithm for mold classification, considering factors such as the number of iterations and the distance metric within the feature space to determine its accuracy. The GLCM feature extraction technique is employed to extract texture information from bread images, encompassing features such as standard deviation (110.62), contrast (1.46), energy, and homogeneity (0.96 and 0.77, respectively). Experimental results demonstrate that the Euclidean Distance yielded the highest result of 72.73, the Cosine Distance obtained the highest result of 8.25, and the City Block Distance obtained the highest result of 92.11. These findings showcase the effectiveness of the system in accurately detecting and classifying mold-infected areas on bread, thereby aiding in ensuring food safety and reducing health risks for consumers.

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Development Model for Knowledge Management System (KMS) to Improve University's Performance (Case Studies in Indonesia University of education)

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International Journal of computer Science Issues 2012年第1 1-1期9卷 1-6页

作者： Munir Rohendi, Dedi Study Program of Computer Science Faculty of Mathematics and Sciences Education Indonesia University of Education Bandung Indonesia Mechanical Engineering Education Department Faculty of Technology and Vocational Education Indonesia University of Education Bandung Indonesia

Knowledge Management System (KMS) is one way to organize and documented the knowledge of institution. The purpose of this study is to develop a prototype KMS in organizing and documenting the knowledge in the university and to do a document aggregation based on the total number, subjects and writers (lecturers). The development method of KMS prototype is using sharepoints which are able to collect, store and publish all digital data available at the university which can be accessed online. Aggregation is using the percentage between the number of documents, subjects, and writers (lecturers). The aggregation's result between the number of digital files compared to the number of courses and the number of lecturers are each below 10%. In the future, the university still needs to encourage all lecturers or academic community to increase the collection of digital files that can indirectly improve the quality and educational services. © 2012 International Journal of computer Science Issues.

关键词： Knowledge based systems

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Decision Support System To Determine The Student Achievement Scholarship Recipients Using Fuzzy Multiple Attribute Decision Making (FMADM) With SAW

Decision Support System To Determine The Student Achievement...

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International Conference on Cyber and IT Service Management (CITSM)

作者： Helmi Kurniawan Ashari P Swondo Eka Purnama Sari Khairul Ummi Yufrizal Fhery Agustin Faculty of Engineering and Computer Science Universitas Potensi Utama Medan Indonesia English Education Study Program Universitas Potensi Utama Medan Indonesia Manajement Study Program Universitas Potensi Utama Medan Indonesia

Higher education is an education unit that organizes higher education. College students are called students, while college educators are called lecturers. Higher education as a place of learning and self-development Students provide facilities to improve student achievement in the form of student achievement scholarships. This scholarship program is expected to stimulate students' interest in learning to be better. In determining scholarship recipients, the student division selects prospective scholarship recipients based on existing criteria. In the selection process, the student division has difficulty choosing the data of prospective recipients. Because each type of scholarship criteria and has different criteria weights. Calculation of the value of weighting criteria requires precision. Based on the problems that occur, the author designed a decision support system to determine the recipients of achievement scholarships using the SAW method. This system can help student affairs to carry out criteria for recipients of achievement scholarships based on administrative data of prospective scholarship recipients in determining criteria for weighting value calculations. With this system, it can minimize errors to determine prospective scholarship recipients who excel. After testing the use of support systems for deciding scholarship decisions using the SAW method, it can be concluded that a decision support system can assist the student's part in carrying out their duties.

关键词： Scholarships Surface acoustic waves Decision support systems Additives Decision making Analytical models

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

作者： Avissa Aurellia Amadea Christy Atika Sari Eko Hari Rachmawanto Wellia Shinta Sari Mohammed S. Noori 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 Department of Computer Engineering Technology Northern Technical University Mosul Iraq Department of Computer Science National University of Science and Technology DhiQar Iraq

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.

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