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4th International Conference on Informatics, Multimedia, Cyber and information System, ICIMCIS 2022

作者： Maulana, Fairuz Iqbal Lestari, Dian Widartha, Vandha Pradwiyasma Haidar, Nirwana Dirgantara, Wahyu School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Airlangga University Faculty of Medicine Surabaya60132 Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia University of Madura Informatics Engineering Study Program Pamekasan Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9781665473279

This study examines the mapping of research data on digital technology in the field of health education using bibliometric analysis method. Data was collected by identifying keywords in the Scopus database and sorting them out to sort the studies from the last 10 years (2012-2021). Through this step, a total of 1482 documents were obtained (articles, journals, proceedings, books, and others). The data is then processed using the VOSViewer instrument to obtain a visualization of the mapping analysis. This study also analyzes the network types of authors and co-authors through the VOSViewer instrument. The result of this study indicates that the most document types published within ten years are Articles (66.5%), Review (22.9%), Conference Paper (4.2%), and others. The most studied subjects are Medicine (55.7%), Nursing (9.0%), Health Professions (8.2%), Social sciences (7.8%), Engineering (3.9%), computer science (2.6%), Environmental science (2.6%), Biochemistry-Genetics and Molecular Biology (2.2%), Psychology (1.6%), and Dentistry (1.3%). This study offers a written communication process and the nature and direction of developing descriptive means of counting and analyzing the various phases of communication as well as recognizing the authorship and direction of its symptoms in documents on the subject of digital technology in the health sector. © 2022 IEEE.

关键词： Mapping

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EpiDoc Data Matching for Federated information Retrieval in the Humanities 18

EpiDoc Data Matching for Federated Information Retrieval in ...

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18th Conference on computer science and Intelligence systems, FedCSIS 2023

作者： Melzer, Sylvia Klettke, Meike Weise, Franziska Harter-Uibopuu, Kaja Moller, Ralf Centre for Study of Manuscript Cultures Universität Hamburg Warburgstraße 26 Hamburg20354 Germany Institute of Information Systems University of Luebeck Ratzeburger Allee 160 Luebeck23562 Germany Faculty for Computer Science and Data Science University of Regensburg Regensburg93053 Germany

ISBN: (纸本)9788396744784

The importance of federated information retrieval (FIR) is growing in humanities research. Unlike traditional centralized information retrieval methods, where searches are conducted within a logically centralised collection of documents, FIR treats each information system as an independent source with its own unique characteristics. Searching these systems together as a centralised source results in lower precision in humanities research, even when the research data itself is structured and stored according to standardised guidelines such as EpiDoc, and requires the need to be able to trace the origin of records to avoid incorrect historical conclusions. Matching of queries against all data sets in each source is proving less effective. A global search index that enables traceable matching of key values deemed relevant would provide a more robust solution here. In this article, we propose a solution that introduces a novel EpiDoc data matching procedure, facilitating traceable FIR across distinct epigraphic sources. © 2023 Polish information Processing Society.

关键词： information retrieval

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Recommendation system based on recognition of prior learning to support curriculum design in online higher education 6

Recommendation system based on recognition of prior learning...

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6th International Conference on information Management and Technology, ICIMTech 2021

作者： Murad, Dina Fitria Hassan, Rosilah Heryadi, Yaya Wijanarko, Bambang Dwi Titan Bina Nusantara University BINUS Online Learning Information Systems Department Jakarta11480 Indonesia Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science Technology UKM Bangi Selangor Malaysia Bina Nusantara University BINUS Graduate Program- Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Online Learning Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Based on the policy of independent study and independent campus, where students can participate in part of the study period and load from within the campus, as well as others outside the campus, so that they can complete all periods and study loads as needed qualification is determined by the study program conducted through assessors. and this requires a variety of processes and takes a lot of time. For this reason, an alternative technique is needed to conduct an automatic Recognition of Prior Learning (RPL) assessment through the application of a recommendation system. The purpose of this research is to find out how the recommendation system approach is used to predict RPL assessments and provide support for curriculum development in tertiary institutions that can meet the learning needs of the digital community. This study has succeeded in classifying learning outcomes based on independent assessment data of prospective students and can provide recommendations for curriculum development in colleges that organize online learning. This study proves that the use of a recommendation system using deep learning in the RPL assessment has an accuracy (97, 24%) and is relatively the same as the assessor's assessment. © 2021 IEEE.

关键词： Recommender systems

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Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models on the Poverty Levels in Central Java in 2023

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2746-2757页

作者： Alya, Najma Attaqiya Almaulidiyah, Qothrotunnidha Farouk, Bailey Reshad Rantini, Dwi Ramadan, Arip Othman, Fazidah Engineering Department Data Science Technology Study Program Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multi-discipline Universitas Airlangga Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

Poverty is considered a serious global issue that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. As a developing country, poverty is a complex issue faced by Indonesia. Tackling poverty in Indonesia has become a key focus of the government’s strategic priorities. This issue of poverty is experienced by all provinces in Indonesia, including Central Java. The poverty rate in Central Java stands at 10.77% or 3.79 million people. The causes of poverty are explained by several factors. Using these various factors, a mapping model can be conducted to determine the number of poor populations in Central Java. This research employs Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) approaches to compare the effectiveness of these models in analyzing the number of poor populations in Central Java in 2023. The kernel function weighting used in this study is Fixed Gaussian. The results showed that the MGWR model outperformed both the OLS regression and GWR models, achieving an Akaike information Criterion (AIC) of 62.766, an R2 of 82.3%, and a Mean Squared Error (MSE) of 0.177. Consequently, it can be inferred that the MGWR model was more suitable for explaining the poverty levels in Central Java. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Logistic regression

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Modeling dynamic systems with memory: What is the right time-order?

Modeling dynamic systems with memory: What is the right time...

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8th Bayesian Modeling ApplicationsWorkshop, BMAW 2011

作者： Lupínska-Dubicka, Anna Druzdzel, Marek J. Faculty of Computer Science Bialystok University of Technology Wiejska 45A 15-351 Bialystok Poland Decision Systems Laboratory School of Information Sciences and Intelligent Systems Program University of Pittsburgh Pittsburgh PA 15260 United States

Most practical uses of Dynamic Bayesian Networks (DBNs) involve temporal inuences of the first order, i.e., inuences between neighboring time steps. This choice is a convenient approximation inuenced by the existence of efficient algorithms for first order models and limitations of available tools. We focus on the question whether constructing higher time-order models is worth the effort when the underlying system's memory goes beyond the current state. We present the results of an experiment with a series of DBN models monitoring woman's monthly cycle. We show that higher order models are significantly more accurate. However, we have also observed overfitting and a resulting decrease in accuracy when the time order chosen is too high.

关键词： Bayesian networks

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Integrated-Regression and Whale Optimisation Algorithms to post stroke rehabilitation analysis: A case study for serious games

Integrated-Regression and Whale Optimisation Algorithms to p...

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2021 IEEE Symposium on computers and Informatics, ISCI 2021

作者： Ismail, Waidah Grosan, Crina Abdullah, Zul Hilmi Aldailamy, Ali Y. Zainal, Nurezayana Hendradi, Rimuljo Faculty Science and Technology Universiti Sains Islam Malaysia Bandar Baru Nilai Negeri Sembilan Malaysia International Halal and Fatwa CenterUniversiti Sains Islam Malaysia Bandar Baru Nilai Negeri Sembilan Malaysia Faculty of Information Technology Inti International University Bandar Baru Nilai Negeri Sembilan Malaysia Department of Computer Science Brunel University Uxbridge United Kingdom Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Malaysia Information System Study Program Universitas Airlangga Surabaya Indonesia

ISBN: (纸本)9781665402767

Stroke is one of the leading causes of death and long-term disability worldwide. The primary goal of post-stroke rehabilitation is to maximize the independence of the affected individuals by facilitating both neurological and compensatory functional recoveries in their daily lives. This paper presents an integrated study, using multiple linear regression (MLR) and whale optimization algorithm, for optimizing the post-stroke rehabilitation performance using Medical Interactive Rehabilitation Assistant (MIRA). MIRA is one of the serious games using virtual reality. The research objective finding the best games settings for MIRA. Analysis of variance and F-test were performed to justify the goodness of fit of the developed mathematical models, as well as the significance of the individual MIRA input settings of the games. Results demonstrated that the developed MLR models for average acceleration and distance were significant, with P=0.018 and P © 2021 IEEE.

关键词： Optimization

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Making large Cox's proportional hazard models tractable in Bayesian networks 8

Making large Cox's proportional hazard models tractable in B...

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8th International Conference on Probabilistic Graphical Models, PGM 2016

作者： Kraisangka, Jidapa Druzdzel, Marek J. Decision System Laboratory School of Information Sciences Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Faculty of Computer Science Bialystok University of Technology Wiejska 45A Bialystok15-351 Poland

Cox's proportional hazard (CPH) model is a statistical technique that captures the interaction between a set of risk factors and an effect variable. While the CPH model is popular in survival analysis, Bayesian networks offer an attractive alternative that is intuitive, general, theoretically sound, and avoids CPH model's restrictive assumptions. Existing CPH models are a great source of existing knowledge that can be reused in Bayesian networks. The main problem with applying Bayesian networks to survival analysis is their exponential growth in complexity as the number of risk factors increases. It is not uncommon to see complex CPH models with as many as 20 risk factors. Our paper focuses on making large survival analysis models derived from the CPH model tractable in Bayesian networks. We evaluate the effect of two complexity reduction techniques: (1) parent divorcing, and (2) removing less important risk factors based on the accuracy of the resulting models. 2016 Microtome Publishing. All rights reserved.

关键词： Bayesian networks

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An intelligent applied Fuzzy Logic to prediction the Parts per Million (PPM) as hydroponic nutrition on the based Internet of Things (IoT)

An intelligent applied Fuzzy Logic to prediction the Parts p...

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International Conference on Informatics and Computing (ICIC)

作者： Purnawarman Musa Herik Sugeru Haya Fadhila Mufza Faculty of Computer Science and Information Technology Universitas Gunadarma Indonesia Faculty of Industrial Technology Study Program of Agrotechnology Universitas Gunadarma Indonesia

Hydroponic plants require regular monitoring of plants that grow well and can even cause plants to die. Because too late in providing nutrition or getting too little or too little food or routine monitoring in the hydroponic system. The innovation proposed in the agricultural sector that we suggest is the process of monitoring hydroponic plants in real-time. This study aims to predict the excess or lack of nutrient content based on the results of routine monitoring and has the analytical ability to determine the prediction of the value of Parts per Million (PPM) to find the nutritional value of plant media. Measurement of nutritional value using Electrical Conductivity (EC) and the potential of Hydrogen (pH) in hydroponic plant media. In this research, using the NFT hydroponic system as the application of fuzzy logic with the Mamdani method to determine the predictions of PPM values in providing nutrients at plants to adjusting the nutritional at each plant (e.g., for water spinach plants are need 1050 until 1400 PPM). The implemented is method Internet of Things (IoT) to remote sensing on measure using a smartphone as the representation of the measurement results on the hydroponic system.

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Model of Customer Relationship Management systems Evaluation Using Factor Analysis

Model of Customer Relationship Management Systems Evaluation...

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International Conference on information Management and Technology (ICIMTech)

作者： Wahyu Sardjono Desi Maya Kristin Gustian Rama Putra Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Study Program Faculty of Mathematics & Natural Sciences Pakuan University Bogor Indonesia

Relationship with customers is one of the most crucial factors in business continuity and development. Many current businesses have started delving into Customer Relationship Management (CRM) to establish and maintain their sales and customer relationships. This research was conducted on one retail company that implements CRM but experiencing a gap in their unit sales performance between their targeted sales and the realization. This study aims to analyze the gaps causing factors using Factor Analysis Method, constructing a suitable regression model, and proposing the company's necessary strategy. The research was conducted quantitatively using a questionnaire from 264 respondents. The result of this study showed that there are five causing factors which are CRM Reliability, Underutilization of CRM, CRM Capability, CRM Unpreparedness, and Customer Relation Quality, as well as forming a regression model of the company's CRM performance.

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Development of Key Performance Indicators in e-Learning Implementation in Higher Education Institutions

Development of Key Performance Indicators in e-Learning Impl...

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International Conference on information Management and Technology (ICIMTech)

Companies or organizations usually use key performance indicators (KPI) as indicators to determine their performance. The company's performance achievements are reflected in the set indicators that will describe the overall performance, as well as higher education institutions can also set key performance indicators, to see their performance achievements. The use of e-learning as a tool to support teaching and learning processes to be more effective and efficient is an example of which performance needs to be measured. This article discusses how the process of developing key performance indicators is carried out through the stages of designing questionnaires and distributing questionnaires to students, data is processed using the method of factor analysis to obtain the main indicators determining performance and the method of regression analysis is also used to build performance models. obtain KPI determinants, and then develop KPI models. The results show that there are indicators of sharing knowledge, managing knowledge, understanding knowledge, and utilization knowledge as performance measurement indicators.

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