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Perception analysis of the Indonesian society on twitter social media on the increase in BPJS kesehatan contribution in the Covid 19 pandemic era

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Journal of Physics: Conference Series 2021年第1期1722卷

作者： F C Permana Z M Wicaksono C Kurniawan A S Abdullah B N Ruchjana Study Program of Multimedia Education Universitas Pendidikan Indonesia Jl. Dr. Setiabudi No.229 Bandung 40154 Indonesia. Student of Master of Management Program Universitas Padjadjaran Jl. Raya Bandung Sumedang km 21 Jatinangor Sumedang 45363 Indonesia. Department of Computer Science Universitas Padjadjaran Jl. Raya Bandung Sumedang km 21 Jatinangor Sumedang 45363 Indonesia. Department of Mathematics Universitas Padjadjaran Jl. Raya Bandung Sumedang km 21 Jatinangor Sumedang 45363 Indonesia.

In the conditions of the Covid-19 Pandemic, the president of Indonesia, Ir Joko Widodo, on May 13, 2020, announced an increase in BPJS Kesehatan contribution. This announcement made everyone in the community boisterous, including Indonesians on social media twitter. Currently, Indonesia is ranked fifth in the world using social media twitter, so data mining on Twitter is a good opportunity to see the public's response to a developing issue. This paper will discuss perceptions analysis of the Indonesian people on social media regarding the issue of increasing the contribution of BPJS Kesehatan. The issue of increasing the contribution of BPJS Kesehatan has been a topic of conversation on Twitter for a long time. After 30 days of data crawling, 145,359 tweets were obtained. This amount of data proves that the Indonesian people are very active in issuing opinions regarding the issue of increasing contribution BPJS Kesehatan. Various kinds of differences of opinion in each conversation are classified into three types of opinion using the Naive Bayes method. The three types of opinions are classified into positive opinions, negative opinions, and neutral opinions. The opinion classification results obtained were 73% containing negative opinions, 18% positive opinions, and 9% neutral opinions. This can serve as an early warning for the government to see the public's response in every policy taken. So that each policy can be evaluated for the better.

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Input-output equations and identifiability of linear ODE models

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arXiv 2019年

作者： Ovchinnikov, Alexey Pogudin, Gleb Thompson, Peter CUNY Queens College Department of Mathematics 65-30 Kissena Blvd QueensNY11367 United States CUNY Graduate Center Ph.D. Programs in Mathematics and Computer Science 365 Fifth Avenue New YorkNY10016 United States Courant Institute of Mathematical Sciences New York University New YorkNY10012 United States Higher School of Economics Faculty of Computer Science Moscow109028 Russia LIX CNRS École Polytechnique Institute Polytechnique de Paris 1 rue Honoré d'Estienne d'Orves Palaiseau91120 France CUNY Graduate Center Ph.D. Program in Mathematics 365 Fifth Avenue New YorkNY10016 United States

Structural identifiability is a property of a differential model with parameters that allows for the parameters to be determined from the model equations in the absence of noise. The method of input-output equations is one method for verifying structural identifiability. This method stands out in its importance because the additional insights it provides can be used to analyze and improve models. However, its complete theoretical grounds and applicability are still to be established. A subtlety and key for this method to work correctly is knowing whether the coefficients of these equations are identifiable. In this paper, to address this, we prove identifiability of the coefficients of input-output equations for types of differential models that often appear in practice, such as linear models with one output and linear compartment models in which, from each compartment, one can reach either a leak or an input. This shows that checking identifiability via input-output equations for these models is legitimate and, as we prove, that the field of identifiable functions is generated by the coefficients of the input-output equations. Finally, we exploit a connection between input-output equations and the transfer function matrix to show that, for a linear compartment model with an input and strongly connected graph, the field of all identifiable functions is generated by the coefficients of the transfer function matrix even if the initial conditions are *** Codes 12H05, 34A55, 92B05, 93C15, 93B25, 93B30 Copyright © 2019, The Authors. All rights reserved.

关键词： Transfer functions

Additive multivariate Gaussian processes for joint species distribution modeling with heterogeneous data

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arXiv 2018年

作者： Vanhatalo, Jarno Hartmann, Marcelo Veneranta, Lari Department of Mathematics and Statistics and Organismal and Evolutionary Biology Research Program University of Helsinki Finland Department of Mathematics and Statistics Department of Computer Science University of Helsinki Finland Natural Resources Institute Finland Finland

Species distribution models (SDM) are a key tool in ecology, conservation and management of natural resources. Two key components of the state-of-the-art SDMs are the description for species distribution response along environmental covariates and the spatial random effect that captures deviations from the distribution patterns explained by environmental covariates. Joint species distribution models (JSDMs) additionally include interspecific correlations which have been shown to improve their descriptive and predictive performance compared to single species models. However, current JSDMs are restricted to hierarchical generalized linear modeling framework. Their limitation is that parametric models have trouble in explaining changes in abundance due, for example, highly nonlinear physical tolerance limits which is particularly important when predicting species distribution in new areas or under scenarios of environmental change. On the other hand, semi-parametric response functions have been shown to improve the predictive performance of SDMs in these tasks in single species models. Here, we propose JSDMs where the responses to environmental covariates are modeled with additive multivariate Gaussian processes coded as linear models of coregionalization. These allow inference for wide range of functional forms and interspecific correlations between the responses. We propose also an efficient approach for inference with Laplace approximation and parameterization of the interspecific covariance matrices on the euclidean space. We demonstrate the benefits of our model with two small scale examples and one real world case study. We use cross-validation to compare the proposed model to analogous semi-parametric single species models and parametric single and joint species models in interpolation and extrapolation tasks. The proposed model outperforms the alternative models in all cases. We also show that the proposed model can be seen as an extension of the current state-

关键词： Hierarchical systems

DEA and Fuzzy Simple Additive Weighting for Benchmarking Qualitative Data 1

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DEA and Fuzzy Simple Additive Weighting for Benchmarking Qua...

1st International Conference of SNIKOM 2018, ICoSNIKOM 2018

作者： Abdullah, Dahlan Hartono, H. Ongko, Erianto Erliana, Cut Ita Irwansyah, Defi Iskandar, Akbar Sari, Ayu Esteka Abdussakir Yuniningsih Siregar, Nurintan Asyiah Kahar, Muhammad Syahrul Waliulu, Raditya Faisal Amalia, Ainna Kurniasih, Nuning Arifin, Muhammad Muzid, Syafiul Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Akademi Teknologi Industri Immanuel Medan Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Department of Management STIE Sakti Alam Kerinci Jambi Indonesia Department of Mathematics Education State Islamic University of Malang Malang Indonesia Department of Economic and Business Universitas Pembangunan Nasional Veteran Surabaya Indonesia Department of Management STIE Labuhanbatu Sumatera Utara Indonesia Mathematics Education Department Universitas Muhammadiyah Sorong Sorong Indonesia Informatics Engineering Department Universitas Muhammadiyah Sorong Sorong Indonesia Islamic Institut of Miftahul Ula Kertosono Nganjuk East Java Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Department of Information System Faculty of Engineering Universitas Muria Kudus Indonesia Information System Department Universitas Muria Kudus Indonesia

Data Envelopment Analysis (DEA) is one method that is often used in the benchmarking process. The benchmarking process with DEA is often faced with problems where there is a Decision Making Unit (DMU) both Input and Output which is not in the form of qualitative data but in the form of qualitative data. Qualitative data requires an assessment from the appraiser so that it can become quantitative data. This study will use the Fuzzy Simple Additive Weighting method to convert qualitative data into quantitative. The results of this study will produce a model that integrates DEA with Fuzzy Simple Additive Weighting. © Published under licence by IOP Publishing Ltd.

关键词： Data envelopment analysis

Generalized Spatio Temporal Autoregressive Rainfall-Enso Pattern in East Java Indonesia 1

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Generalized Spatio Temporal Autoregressive Rainfall-Enso Pat...

1st Indonesian Association for Pattern Recognition International Conference, INAPR 2018

作者： Caraka, Rezzy Eko Ulhusna, Mishbah Darmawan Supatmanto, Budi Goldameir, Noor Ell Hutapea, Bonar Darmawan, Gumgum Novitasari, Dian Candra Rini Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia School of Mathematical Sciences National University of Malaysia Malaysia Department of Statistics Padjadjaran University Indonesia Modifikasi Cuaca - BPPT Balai Besar Teknologi Indonesia Faculty of Psychology Tarumanagara University Indonesia Department of Mathematics UIN Sunan Ampel Surabaya Indonesia Riau University Indonesia 'Yptk' Padang Universitas Putra Indonesia Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781538694220

Monthly rainfall analysis and forecasts are made to be able to provide a clear picture of the rain and climate conditions that have occurred and will occur in the territory of Indonesia. The information can be known in an area that has the most substantial maximum rainfall and in an area which has the lowest rainfall described and presented spatial pattern by using the ArcView-GIS. The purpose of making monthly rainfall forecasts is to be used and utilized by users, especially in the agriculture and irrigation sectors to plan and make decisions within the next 1 to 3 months related to climatic conditions, especially rainfall. In this paper, we use annual rainfall data in six location East Java. We analysis ENSO phenomena as well as rainfall forecasting in January - March 2018 by using generalized space-time autoregressive and get an accuracy MAPE out samp;e amount 2.95% dan RMSE out sample amount 4.77. © 2018 IEEE.

关键词： Rain

Geometry based data generation 18

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Geometry based data generation

Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： Ofir Lindenbaum Jay S. Stanley, III Guy Wolf Smita Krishnaswamy Applied Mathematics Program Yale University New Haven CT Computational Biology & Bioinformatics Program Yale University New Haven CT Departments of Genetics & Computer Science Yale University New Haven CT

We propose a new type of generative model for high-dimensional data that learns a manifold geometry of the data, rather than density, and can generate points evenly along this manifold. This is in contrast to existing generative models that represent data density, and are strongly affected by noise and other artifacts of data collection. We demonstrate how this approach corrects sampling biases and artifacts, thus improves several downstream data analysis tasks, such as clustering and classification. Finally, we demonstrate that this approach is especially useful in biology where, despite the advent of single-cell technologies, rare subpopulations and gene-interaction relationships are affected by biased sampling. We show that SUGAR can generate hypothetical populations, and it is able to reveal intrinsic patterns and mutual-information relationships between genes on a single-cell RNA sequencing dataset of hematopoiesis.

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Clustering of transcriptomic data for the identification of cancer subtypes

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arXiv 2018年

作者： Chen, Xiaochun Wang, Honggang Yan, Donghui Department of Electrical and Computer Engineering University of Massachusetts Dartmouth MA United States Department of Mathematics and Program in Data Science University of Massachusetts Dartmouth MA United States

Cancer is a number of related yet highly heterogeneous diseases. Correct identification of cancer subtypes is critical for clinical decisions. The advance in sequencing technologies has made it possible to study cancer based on abundant genomics and transcriptomic (-omics) data. Such a data-driven approach is expected to address limitations and issues with traditional methods in identifying cancer subtypes. We evaluate the suitability of clustering–a data mining tool to study heterogenous data when there is a lack of sufficient understanding of the subject matters–in the identification of cancer subtypes. A number of popular clustering algorithms and their consensus are explored, and we find cancer subtypes identified by consensus clustering agree well with clinical studies. Copyright © 2018, The Authors. All rights reserved.

关键词： Clustering algorithms

Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models

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arXiv 2022年

作者： Aubry, Jean-Francois Bates, Oscar Boehm, Christian Pauly, Kim Butts Christensen, Douglas Cueto, Carlos Gélat, Pierre Guasch, Lluis Jaros, Jiri Jing, Yun Jones, Rebecca Li, Ningrui Marty, Patrick Montanaro, Hazael Neufeld, Esra Pichardo, Samuel Pinton, Gianmarco Pulkkinen, Aki Stanziola, Antonio Thielscher, Axel Treeby, Bradley Van't Wout, Elwin Physics for Medicine Paris INSERM U1273 ESPCI Paris PSL University CNRS UMR 8063 France Department of Bioengineering Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Institute of Geophysics ETH Zürich Sonneggstrasse 5 Zürich8092 Switzerland Department of Radiology Stanford University StanfordCA United States Department of Biomedical Engineering Department of Electrical and Computer Engineering University of Utah United States Department of Surgical Biotechnology Division of Surgery and Interventional Science University College London LondonNW3 2PF United Kingdom Earth Science and Engineering Department Imperial College London London United Kingdom Centre of Excellence IT4Innovations Faculty of Information Technology Brno University of Technology Bozetechova 2 Brno612 00 Czech Republic Graduate Program in Acoustics The Pennsylvania State University University Park PA16802 United States Joint Department of Biomedical Engineering University of North Carolina at Chapel Hill North Carolina State University NC United States Department of Electrical Engineering Stanford University StanfordCA United States Zurich Switzerland Laboratory for Acoustics / Noise control Empa Swiss Federal Laboratories for Materials Science and Technology Dubendorf Switzerland Zurich Switzerland Radiology and Clinical Neurosciences Departments Cumming School of Medicine University of Calgary Canada Department of Applied Physics University of Eastern Finland Kuopio70211 Finland Department of Medical Physics and Biomedical Engineering University College London Gower Street LondonWC1E 6BT United Kingdom Technical University of Denmark Denmark Danish Research Center for Magnetic Resonance Copenhagen University Hospital Hvidovre Denmark Institute for Mathematical and Computational Engineering School of Engineering and Faculty of Mathematics Pontificia Universidad Catolica de Chile Santiago Chile

Computational models of acoustic wave propagation are frequently used in transcranial ultrasound therapy, for example, to calculate the intracranial pressure field or to calculate phase delays to correct for skull distortions. To allow intercomparison between the different modeling tools and techniques used by the community, an international working group was convened to formulate a set of numerical benchmarks. Here, these benchmarks are presented, along with intercomparison results. Nine different benchmarks of increasing geometric complexity are defined. These include a single-layer planar bone immersed in water, a multi-layer bone, and a whole skull. Two transducer configurations are considered (a focused bowl and a plane piston), giving a total of 18 permutations of the benchmarks. Eleven different modeling tools are used to compute the benchmark results. The models span a wide range of numerical techniques, including the finite-difference time-domain method, angular-spectrum method, pseudospectral method, boundary-element method, and spectral-element method. Good agreement is found between the models, particularly for the position, size, and magnitude of the acoustic focus within the skull. When comparing results for each model with every other model in a cross comparison, the median values for each benchmark for the difference in focal pressure and position are less than 10% and 1 mm, respectively. The benchmark definitions, model results, and intercomparison codes are freely available to facilitate further comparisons. Copyright © 2022, The Authors. All rights reserved.

关键词： Bone

Smart sensor based distillation for fractionating citronella oil: current research challenges

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IOP Conference Series: Earth and Environmental science 2020年第1期472卷

作者： D Wahyudi E Noor D Setyaningsih T Djatna Irmansyah Post Graduate Program of Agroindustrial Technology Bogor Agricultural University Bogor Department of Agroindustrial Technology Bogor Agricultural University Bogor Indonesia. Department of Physics Bogor Agricultural University Bogor Indonesia. of Computer Science Faculty of Mathematics and Natural Science IPB University (Bogor Agricultural University) Medan Agricultural Development Polytechnic Medan Indonesia.

Citronella oil is one of the essential oils derived from the leaves and stem of lemongrass (Cymbopogon nardus). At present, most of the lemongrass oil industry in Indonesia is still in the form of upstream industries which only provide crude purified oils with low purity for export with a fairly low sales value. Technology in improving the purity of citronella oil needs innovation in following the development of the industrial revolution 4.0. This article presents an overview of the research opportunities in the development of distillation for fractionating citronella oil referring to automation of optimization processes in the vacuum fractional distillation accompanied by inspection of the quality of essential oil isolation run in the line process based on smart sensors integrated in a computer program. The combination technique of quality control of essential oils in line process system combined with the optimization technique of the vacuum fractional distillation process has not been studied by advanced researchers. Therefore, this study has a state of the art process engineering and smart sensor-based quality control systems that can be developed for strengthening the Indonesian citronella oil industry.

关键词： Citronella oil fractional distillation smart sensor