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Convolve4D: A Novelty Approach to Improve Convolutional Process

IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Suryadiputra Liawatimena Edi Abdurahman Agung Trisetyarso Antoni Wibowo Wiedjaja Atmadja Fandy Effendi Ivan Sebastian Edbert Computer Science Department BINUS Graduate Program - Doctor ofComputer Science Bina Nusantara University Jakarta Indonesia 11480 ComputerEngineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia 11480 ComputerScience Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Convolutional neural networks (ConvNet or CNN) are deep learning algorithms that can process input images, assign meaning to various aspects or objects in the image (biases and learnable weight) and recognize one image from another. The bigger kernel size will take more time to process the *** present a novelty way to use a 4D rank tensor to improve a convolutional process. At the early stage of the Convolve4D development, the edge detection with 3×3 kernel and The Laplacian of Gaussian (LoG) with 5×5 kernel size was used to demonstrate the convolutional process improvement. The Convolve4D needs more elaboration to be used into a CNN algorithm. The advantage of convolve4D is only need 9 loops to calculate 81 outputs, whereas convolve2D need 9 × 9 × 3 × 1 × 7 × 7 = 11.907 loops. The result is 18.5% shorter when using a 5×5 kernel; it reduces from 0.54 seconds to 0.44 seconds for the edge detection convolution process.

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Data Engineering Pipeline to Analyse Jakarta's Air Quality during COVID-19-Caused Lockdown Periods

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IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Reza Rahutomo Bens Pardamean Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Jakarta lifted up lockdown after passing more than 50 days of large-scale social activity restriction and initiated phase opening to new normal. To analyse Jakarta's air quality after passing lockdown, a pipeline of data engineering is needed. By acquiring time series data from ***, a time-series database system is developed with Python programming language and its fundamental libraries namely Pandas, NumPy, SQLite. After PM 2.5 data are pre-processed into average per-hour and grouped by applicable periods (pre-lockdown, lockdown, and phase opening), a pattern of PM 2.5 in South Jakarta is revealed by using data visualization library Matplotlib. The apex of PM 2.5 occurs earlier during lockdown (04:00) and phase opening (02:00) rather than when it was normal or pre-lockdown (08:00) even though the nadir of PM2.5 still occurs at the same time (16:00 – 17:00).

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Implementation of online learning during the COVID-19 pandemic in primary schools

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AIP Conference Proceedings 2023年第1期2594卷

作者： A. Anugrahana E. Liswantiani D. Sudigyo T. Suparyanto B. Pardamean Elementary School Teacher Education Study Program (PGSD) Faculty of Teacher Training and Education Sanata Dharma University Yogyakarta Indonesia 55281 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The COVID-19 pandemic requires face-to-face learning to shift to online or distance learning. Therefore, the elementary school, which is the most basic level of education affected by this phenomenon, where this research is carried out. This study aimed to analyze the process of implementing online learning, the supporting and inhibiting factors for teachers, students, and parents in implementing online learning during the COVID-19 pandemic. In addition, good practices for online learning were also analyzed in this study to understand these phenomena in elementary schools. The research method utilized descriptive qualitative research and was performed at a private primary school in Yogyakarta. The subjects consisted of teachers, students, and parents that were involved in the online learning process. Online questionnaires and interviews were used to collect data. The results of this study show the enormous impact of the COVID-19 pandemic on the learning process. Learning that is usually carried out face-to-face (conventional) has now been converted into online learning in elementary school.

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Predicting Metabolic Syndrome With Machine Learning Models of Hybrid Analysis

Predicting Metabolic Syndrome With Machine Learning Models o...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Wu, Yi-Syuan Wang, Wei-Yun Wu, Hao-Yi Chen, Chao-Wen Chu, Chi-Ming Wu, Cheng-Wei Division of Trauma and Surgical Critical Care Department of Surgery Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan Master Program of E-Learning for Multimedia and Network Communications National I-Lan University Yilan Taiwan National Defense Medical Center Tri-Service General Hospital Songshan Branch School of Nursing Department of Nursing Taipei Taiwan National Defense Medical Center Department of Nursing Tri-Service General Hospital Taipei Taiwan Kaohsiung Medical University Hospital Kaohsiung Medical University Trauma and Critical Care Service Department of Surgery Kaohsiung Taiwan National Defense Medical Center School of Public Health Taipei Taiwan National I-Lan University Department of Computer Science and Information Engineering Yilan Taiwan

ISBN: (纸本)9798350324174

The study uses machine learning to determine the accuracy of different algorithms for predicting MetS. Early prediction of MetS through routine health screening is an important goal for preventive medicine. The subjects in this study were employees aged 20 to 65 years old who signed a consent form for in-hospital health screening data were collected from 2015 to 2020. Total of 3538 individuals who underwent health screening were included in this study. Baseline characteristics of the 315 participants diagnosed with metabolic syndrome and 3222 participants without metabolic syndrome. This is a machine learning approach to identify metabolic syndrome using biochemical values and lifestyle habits. We found that the F1 score for detection of metabolic syndrome by neural network is the most accurate and higher compared to other models. Our study also identified some new indicators, including heart rhythm, RBC, WBC, Hb, and uric acid, as candidates for better detection of metabolic syndrome. © 2023 IEEE.

关键词： Forecasting

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Covid-19 Series: A rule-based decision support system for analysis behavior of people while working from home

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IOP Conference Series: Materials science and Engineering 2020年第1期1007卷

作者： Henderi Rani Putri Merliasari Harco L.H. S. Warnars Sugiyatno Department of Informatics Engineering University of Raharja Indonesia Master of Information Technology University of Raharja Computer Science Department Graduate Program – Doctor of Computer Science Bina Nusantara University Indonesia Department of Informatics Bhayangkara Jakarta Raya University Indonesia

COVID-19 makes the community must carry out activities such as school, work, and worship at home. However, the long-running activities from home make people experience boredom which can lead to stress. On the other side, the entertainment obtained by the public through smartphones by reading articles by their interests can reduce boredom. This paper proposed a rules-based decision support system to help the people to make choices of their activities from home while COVID-19 e rules-based approach for make an application decision support system. Ruled base used in application to selected process through characteristics following the interests of the community. This decision support system is implemented in mobile web applications. The system can display articles based on interests by questions or statements through the front end system. The results showed that most users of the application in a happy condition while working from home.

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Weeds e-Catalog as a Tool for Identification of Weeds in Plantation

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IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Hangger Gahara Mawandha Teddy Suparyanto Bens Pardamean Agrotechnology Department Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Agricultural Engineering Department Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Universitas Bina Nusantara Jakarta Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta

Weeds are one of the organisms that interfere with plant growth. Information about the identity of weeds becomes very important on plantations. Although weeds data digitization has been done a lot, currently there is still not much weeds data information system can be accessed online. Weeds management is often dealt with weeds herbarium or weeds photographs. In an effort to provide a good information system for farmers, this research aims to create a database of various types of weeds and an information system that can be accessed online. The methodology for developing a weed catalog information system uses the Software Development Life Cycle (SDLC). Weed samples in database were collected through systematic random sampling in the form of images and text. The result of this study is a Weeds Electronic Catalog or Weeds e-Catalog that can facilitate the information of weeds identity such as names, classifications, morphology, life cycles, and habitats of various types of weeds that grow on plantation land. Weeds e-Catalog can be used by plantation practitioners and farmers to make decisions in controlling weeds.

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Quantum-Inspired Acceleration for Image Reconstruction on Ising Machines

Quantum-Inspired Acceleration for Image Reconstruction on Is...

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Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Yen-Jui Chang Hao-Yuan Chen Ying Chang Lu Lien-Po Yu Chao-Ming Fu Ching-Ray Chang Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan Department of Physics National Taiwan University Taipei Taiwan Fujitsu Taiwan Ltd. Taipei Taiwan Department of Computer Science University of London London United Kingdom Institute for Information Industry Taipei Taiwan Fo Guang University Yilan Taiwan Quantum Information Center Chung Yuan Christian University Taoyuan City Taiwan

The study investigates a quantum-inspired approach to image reconstruction using Ising machines and demonstrates its significant improvements over the contrastive divergence method in Restricted Boltzmann Machines tra... 详细信息

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

关键词： Training Quantum computing Image reconstruction

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Blowfish algorithm and Huffman compression for data security application

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IOP Conference Series: Materials science and Engineering 2018年第1期453卷

作者： Yaya Sudarya Triana Astari Retnowardhani Faculty of Computer Science Universitas Mercu Buana Jakarta Indonesia Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University

In its development, information technology has become an important in people's everyday lives. Security and confidentiality of the data on computer networks today become a very important issue and continues to grow. Some of the cases relating to computer network security today become a job that requires handling fee and security has been tremendous. Vital systems, such as the defense system, the banking system, the hospital system, and other systems, requires such a high level of security. This is mainly due to the advancement of the field of computer networks with the concept of open system so that anyone, anywhere and at any time, have the opportunity to access these vital areas. To maintain the security and confidentiality of messages, data, or information in a computer network would require some encryption to create messages, data, or information that is not read or understood by any person, except for eligible recipients.

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Multi-criteria decision making on succesfull of online learning using AHP and regression

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Journal of Physics: Conference Series 2019年第1期1175卷

作者： Fatsyahrina Fitriastuti Uci Rahmalisa Abba Suganda Girsang Department of Informatics Engineering Janabadra University Yogyakarta Indonesia Department of Informatics Engineering STMIK Hang Tuah Pekan Baru Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

This paper purposes comparing the view of system builder (expert system) using analitycal hierarchy process (AHP) and view of user (students) using regression method in elearning system STIKes-STMIK Hang Tuah Pekanbaru. This study uses some attributes to analyze and evaluate which affect the acceptance of elearning system success. In AHP, the attributes are categorized as criteria and grouped into some dimensions. These criteria and dimenssions are evaluated by some experts using analitycal hierarchy process (AHP) to assess priorities for elearning system. There are four dimensions, system quality (four criteria), learning information quality (three criteria), service quality (three criteria) and service support (three criteria) dimension. This system is also evaluated by filling out the questionnaire by students as user of elearning system using regression method. The attributes are used as independent variable, while the succesfull of elearning is dependent variable. The result of using AHP and regression system has a different. It also can show there are different view between users and system builder which should be considered by STIKes-STMIK Hang Tuah Pekanbaru.

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The accuracy of the F statistic for structural health monitoring

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AIP Conference Proceedings 2020年第1期2227卷

作者： Fergyanto E. Gunawan Benfano Soewito Nico Surantha Tuga Mauritsius Nobumasa Sekishita 1Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480 2Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 3Information Systems Management Department BINUS Graduate Program – Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 4Department of Mechanical Engineering Toyohashi University of Technology Toyohashi Aichi 441-8580 Japan

Understanding the reliability of engineering methods is crucial for its adoption and deployment. This research focuses on the reliability of the Power Spectral Density (PSD) method via the use of the F statistic for damage detection. To the author best knowledge, the method is rather classic but its realibility has not been discussed in the context of a large data size. Priory, the research anticipates that the accuracy is a function of the damage level. In this study, we evaluate 3500 cases with five levels of structural integrity, namely, healthy condition and damaged conditions with 1%, 5%, 10%, and 20% damage levels. The dataset is established via a numerical analysis of a seven degree-of-freedom system loaded with a concentrated dynamic force with random magnitude. A spring on the system is reduced in its stiffness to simulate damages. Our significant findings are the following: it is challenging for the PSD-based method to differentiate the healthy condition from the damaged conditions when the damage level is small. However, the reliability is high at 95% probability when the structural integrity has dropped by five percent.

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