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

作者： Yulistia Ermatita Reza Firsandaya Malik Informatic Doctoral Program Sriwijaya University STMIK MDP Palembang Indonesia Information System Sriwijaya University Palembang Indonesia Computer Science Faculty Sriwijaya University Palembang Indonesia

ISBN: (纸本)9781728129310

Knowledge management System (KMS) as one of the management system to reach effective and competitive advantage, has been applied in many company or institution. One of KMS phase is knowledge discovery systems with specific activity called knowledge transfer. SECI model is one of knowledge transfer which used by many researcher in KMS journal. SECI model developed into inukshuk model which complete the component that is not available in SECI. The problem is, both SECI and inukshuk models are generally used for common institution or company, and not specialized for educational institution such as higher education. This paper will discused and formulate model for knowledge transfer activity based on SECI and Inukshuk model, based on research about higher education knowledge transfer and knowledge management concept in private higher education, which already made by some researchers. The method in this paper using review journal method with observation and analysis approach. Result of this paper is an additional factor for transfer knowledge suggested for Private Higher education, it is People and Policy, added in the knowledge transfer phase in order to emphasis the Socialization and Externalization in SECI Model.

关键词： data mining educational computing educational institutions further education knowledge management Transfer (Psychology) Educational institutions computer uses in education knowledge management Continuing Education data mining higher education KMS gene Externalization

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Backpropagation Network Optimization Using One Step Secant (OSS) Algorithm

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IOP Conference Series: Materials science and Engineering 2020年第1期769卷

作者： Solikhun Mochamad Wahyudi M. Safii Muhammad Zarlis Doctoral Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Sumatera Utara Indonesia. Faculty of Computer Science and Information Technology Universitas Sumatera Utara Indonesia

Education is one of the main indicators in national development efforts. The government in its efforts to realize national goals to educate the nation's life has made a policy for compulsory 9-year schools, namely elementary and junior high schools. To find out how many residents use the education facilities provided by the government can be seen through school enrollment rates (SPR). A high School Participation Rate (SPR) means showing greater opportunities to access education in general. This study aims to optimize artificial neural networks with the One Step Secant (OSS) algorithm. Artificial Neural Network (ANN) is part of the artificial intelligence system (Artificial Intelligence, AI) which is one of the artificial representations of the human brain that always tries to simulate the learning process in the human brain. The sample data used for optimization is SPR Indonesia data by province. Using 4 architectural models with 5 input variables, 1 shadow layer and 1 output. The best results obtained between architectures 5-4-1, 5-8-1, 5-16-1 and 5-32-1 are architectures 5-16-1. Obtained prediction accuracy comparisons using the One Step Secant (OSS) algorithm and standard algorithms namely 96.97% and 100%. The standard algorithm is superior in accuracy, the One Step Secant (OSS) algorithm is superior in terms of iterations.

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Resilient Algorithm In Predicting Fertilizer Imports by Major Countries

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IOP Conference Series: Materials science and Engineering 2020年第1期769卷

作者： Solikhun Mochamad Wahyudi M Safii Muhammad Zarlis Doctoral Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Sumatera Utara Indonesia. Faculty of Computer Science and Information Technology Universitas Sumatera Utara Indonesia

In the last five years (2013-2017) Indonesia's fertilizer production experienced volatile growth, but overall tended to increase at a rate of 1.7% per year. The research aims to optimize artificial neural networks with a resilient backpropagation algorithm (Rprop), artificial neural networks are one of the artificial representations of the human brain that always tries to simulate the learning process in the human brain. Sample data used for optimization is fertilizer import data according to the main country of origin and uses 4 architectures, the best results are obtained between architectures 6-8-1, 6-12-1, 6-16-1, and 6-32-1 is architecture 6-32-1 with 100% accuracy.

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Perceptron Partition Model to Minimize Input Matrix

Perceptron Partition Model to Minimize Input Matrix

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International Conference on science and Innovated Engineering 2018, i-COSINE 2018

作者： Azmi, Zulfian Nasution, Mahyuddin K.M. Zarlis, Muhammad. Mawengkang, Herman Efendi, Syahril Postgraduate Doctoral of Computer Science Universitas Sumatera Utara Padang Bulan USU Medan20155 Indonesia Information Technology Department Universitas Sumatera Utara Padang Bulan USU Medan20155 Indonesia Program Pasca Sarjana Doktoral Ilmu Komputer Universitas Sumatera Utara Indonesia Department of Mathematics Universitas Sumatera Utara Padang BulanUSU Medan20155 Indonesia

Implementation of Neuron Network model using Perceptron has not given optimal result in real time learning. The large number of inputs expressed in matrix form makes the process slower in pattern recognition. So, it takes characteristic to represent all the input matrices by using the partition method. By partitioning each input and with the best weight and using the activation function will produce an output value. And learning is valid in recognizing patterns only 1 iteration only. Further validation is done on the water mill control module with dissolved oxygen input, water pH, salinity and water temperature. With Perceptron Partition learning algorithm more real-time than perceptron model. Testing on the waterwheel input whether rotating or stopping by Matrix Laboratory software simulation. © Published under licence by IOP Publishing Ltd.

关键词： Neural networks

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Implementation of Fuzzy-based Model for Prediction of Prostate Cancer

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Journal of Physics: Conference Series 2021年第1期1751卷

作者： Rusliyawati Kurnia Muludi Admi Syarif Agus Wantoro Doctoral Program of Mathematics and Science Faculty Lampung University Jl. Sumantri Brojonegoro No 1 Bandar Lampung Indonesia. Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia J. ZA. Pagaralam No. 9-11 Bandar Lampung Indonesia. Department of Computer Science Faculty of Mathematics and Science University of Lampung Jl. Sumantri Brojonegoro No 1 Bandar Lampung Indonesia

Cancer is one of the leading causes of death worldwide. One type of cancer that causes death in the male population is prostate cancer. This disease occurs only in men because in women they do not have a prostate appearance. The aim of this study was to compare the accuracy of the model with the predictions of prostate cancer specialists. Prediction is made based on prostate specific antigen data, age, and patient prostate volume. The independent variables in this study were prostate specific antigen, age, and prostate volume. The dependent variable is the risk of prostate cancer using a fuzzy model. The novelty of this study is that the model has a low, moderate, high, and very high prostate cancer risk level output. In the previous article only PCR values were produced. The results show that the proposed fuzzy model provides a PCR value that is within the PCR interval predicted by a specialist doctor can be used properly to help diagnose and analyze the possibility of prostate cancer and is one of the considerations for doctors to decide whether or not a biopsy is needed for these patients.

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Develop learning methods imr (integrated material realistic) alternative mathematics learning during a pandemic

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Journal of Physics: Conference Series 2021年第4期1918卷

作者： I.M. Rusmana Zainuri Wardono A.L Nulhakim Doctoral Program Universitas Negeri Semarang Indonesia Department of Mathematic Education Faculty of Mathematics and Natural Science Universitas Indraprasta PGRI Jakarta Indonesia Department of Mathematic Education Faculty of Mathematics and Natural Science Universitas Negeri Semarang Indonesia Department of Informatics Engineering Faculty of Engineering and Computer Science Universitas Indraprasta PGRI Jakarta Indonesia

The learning methods used by teachers in schools in Serang City are very ineffective, especially during the pandemic. This is because not integrated content of material in one subject with other subjects. Never mind between subjects, sometimes the material is not well integrated. The purpose of this study is to provide an alternative learning method during a pandemic, namely a method that integrates material with the realities of life which is abbreviated as IMR (Integrated Realistic Material). The method in this research is Research and Development (RnD) with the ADDIE model. In making this learning method according to the stages from the ADDIE study. The results of this study are in the form of learning methods that are integrated with the realities of life during the pandemic.

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Multigoal-oriented error estimation and mesh adaptivity for fluid-structure interaction

arXiv

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arXiv 2021年

作者： Ahuja, K. Endtmayer, B. Steinbach, M.C. Wick, Thomas Discipline of Computer Science and Engineering Indian Institute of Technology Indore India Leibniz Universität Hannover Institut für Angewandte Mathematik Welfengarten 1 Hannover30167 Germany Leibniz Universität Hannover Germany Université Paris-Saclay ENS Paris-Saclay LMT - Laboratoire de Mécanique et Technologie Gif-sur-Yvette91190 France Johannes Kepler University Linz Doctoral Program on Computational Mathematics Altenbergerstr. 69 LinzA-4040 Austria Austrian Academy of Sciences Johann Radon Institute for Computational and Applied Mathematics Altenbergerstr. 69 LinzA-4040 Austria

In this work, we consider multigoal-oriented error estimation for stationary fluid-structure interaction. The problem is formulated within a variational-monolithic setting using arbitrary Lagrangian-Eulerian coordinates. Employing the dual-weighted residual method for goal-oriented a posteriori error estimation, adjoint sensitivities are required. For multigoal-oriented error estimation, a combined functional is formulated such that several quantities of interest are controlled simultaneously. As localization technique for mesh refinement we employ a partition-of-unity. Our algorithmic developments are substantiated with several numerical tests such as an elastic lid-driven cavity with two goal functionals, an elastic bar in a chamber with two goal functionals, and the FSI-1 benchmark with three goal *** Codes 65N30, 65N50, 74F10 © 2021, CC BY-NC-ND.

关键词： Fluid structure interaction

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Data Warehouse Design for Soil Nutrients with IoT Based Data Sources

Data Warehouse Design for Soil Nutrients with IoT Based Data...

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

作者： Abdul Rahman Ermatita Dedik Budianta Faculty of Engineering Sriwijaya University doctoral program student(lecture at AMIK MDP) Inderalaya Indonesia Faculty of Computer Science Sriwijaya University Inderalaya Indonesia Faculty of Agriculture Sriwijaya University Inderalaya Indonesia

ISBN: (纸本)9781728129310

Plant fertility is very dependent on the levels of nutrients present in the soil. Measurement of soil nutrients can be done through laboratory tests and can also be done using IoT technology. IoT equipment is supported by sensors that can be used to measure soil nutrients and the location of the soil is measured. Data from land measurements made through IoT technology can be stored and displayed in real-time through the cloud. This paper designs data sources derived from IoT -based nutrient measurement data to build data warehouse locations and soil nutrients. A data warehouse that is formed later can be done for analysis of the types of plants and fertilizers needed at a soil location.

关键词： agriculture cloud computing data handling data warehouses Internet of Things soil Data warehousing soil nutrients Internet of things data processing Cloud Computing Data sources agriculture Soil plant fertility

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Implementation of Apriori Algorithm for Analysis of Consumer Purchase Patterns 1

Implementation of Apriori Algorithm for Analysis of Consumer...

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1st International Conference on computer science and Applied Mathematic, ICCSAM 2018

作者： Panjaitan, Suprianto Sulindawaty Amin, Muhammad Lindawati, Sri Watrianthos, Ronal Sihotang, Hengki Tamando Sinaga, Bosker Teknik Informatika STMIK Pelita Nusantara Medan Sumatera Utara Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Program Doctoral Vocational Technology Education Postgraduate Engineering Faculty Padang State University Padang West Sumatera Indonesia Manajemen Informatika AMIK Logika Sumatera Utara Indonesia Manajemen Informatika AMIK Labuhan Batu Sumatera Utara Indonesia

Consumer purchasing patterns are a form of purchases made by consumers, whether someone or a lot of people to get the desired item by making a purchase transaction. One characteristic of the purchase pattern is the existence of acquiring something through exchanging money. This study aims to create an application that is used in determining consumer purchasing patterns by applying a priori algorithms and using Visual Basic 2010 as a tool for determining consumer purchase patterns. This application uses a priori algorithm calculation method where the sample consumer purchase data will be sorted and calculated by providing the value of the minium support and configuration parameters and based on the results of confidence the largest number of conclusions such as: can be used as information for determining sales, the application of a priori algorithms can provide information pattern combination item set from consumer purchase data that is with support above 15% and confidence above 50% on item set. © Published under licence by IOP Publishing Ltd.

关键词： Condensed matter physics

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Classification of Land Cover from Sentinel-2 Imagery Using Supervised Classification Technique (Preliminary Study) 3

Classification of Land Cover from Sentinel-2 Imagery Using S...

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3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Miranda, Eka Mutiara, Achmad Benny Ernastuti Wibowo, Wahvu Catur Information Systems Department Doctoral Program in Information Technology Gunadarma University School of Information Systems Bina Nusantara University Jakarta Indonesia Faculty of Computer Science and Information Technology Gunadarma University Jakarta Indonesia Computer Science Faculty Universitas Jakarta Indonesia

ISBN: (纸本)9781538658215

This paper intended to classify land cover of high-resolution satellite image using supervised classification method. The object of this research was the land cover image of the central Java area in Indonesia which chosen as an area observed with the cloud cover consideration. The Satellite image was obtained from Sentinel-2 through https://***. Supervised Classification and ArcMap 10.5 was used to classified image object. This research classified the land cover into four classifications class namely Water, Forest, Urban and Bare Land due to show the main classes in the Land Cover Classification of RSNI-l National Standardization Body of Indonesia, RSNI-l was used as reference for classification process. The confusion matrix used to calculate classification accuracy value. Subsequently, confusion matrix results compared to the truth information. The truth information was derived from the actual value of RASTERVALUE obtained from Google Earth. This research shown supervised classification maximum-likelihood classification has been classified land cover into four class (forest, water body, urban and bare land) with overall accuracy = 1 and kappa value = 0.4896. This result showed this research got moderate kappa accuracy value but high overall accuracy value. High accuracy value reached due to fully supervised experiment during classification process. © 2018 IEEE.

关键词： Standardization

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