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

作者： Mahesworo, Bharuno Budiarto, Arif Parung, Deby Erina Amirulloh, Dian Simon, Simon Pardamean, Bens Bina Nusantara University School of Computer Science Statistics Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Genetics Indonesia Product Department Jakarta Indonesia Genetics Indonesia Laboratory Department Jakarta Indonesia Board of Director Genetics Indonesia Jakarta Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Smoking habits in Indonesia has become a national health burden despite their huge contribution to national economics. With easy access to inexpensive cigarettes in Indonesia, the percentage of teenage smokers in Indonesia is very high. More than 70% of these smokers have tried to quit, but only approximately 5% have succeeded. These facts show the importance of developing precision stop smoking treatments. We proposed an information system design of smoking measurement questionnaire that is able to describe the characteristic differences between smokers and former smokers. This information system design was implemented in genetic service company's web-based application and is expected to help us extract the necessary variables to help us finding biomarkers associated with smoking cessation in our future study. The information system design has been developed by combining and modifying questionnaires from previous studies. The focus is on strategic variables and the compactness of the questionnaire, and it can be used by other researchers to describe the smoking dependency of their samples. The information system design has been tested on pilot respondents/customers and shows results that meet our expectations. © 2021 IEEE.

关键词： Tobacco

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Tinnitus: probable association with the elderly's cervical alterations

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Arquivos Internacionais de Otorrinolaringologia 2014年第3期15卷 333-337页

作者： Michelle Damasceno Moreira Luciana Lozza de Moraes Marchiori Viviane de Souza Pinho Costa Erick Costa Damasceno Paula Carolina Dias Gibrin Specialist Taking Master Degreee in Rehabilitation Science at UNOPAR. Physiotherapist admitted after a public examination by the Family Health's Program. Doctor in Medicine and Health Science. Master Degree's Professor of Rehabilitation Science at UNOPAR. Doctor in Nursery USP's Nursery School of Ribeirão Preto. Professor North of Parana University PR. Bachelor in Statistics – University of Brasilia 2008. Odds&Actions's Managing Partner and Counsellor at the Regional Council of Statistics – 1st Region. Specialist in Audiology taking Master Degree in Rehabilitation Science at UNOPAR. Cismepar's Phonoaudiologist.

Introduction: Tinnitus is a prevailing symptom that highly jeopardizes the elderly patient's quality of life. Neck pain and cervical column alterations are frequent in patients complaining about tinnitus. Objective: Evaluate the prevalence of both tinnitus and neck pain on an elderly group, and verify the likely association between tinnitus, neck pain and the constraint to make wide cervical movements. Methods: Retrospective transversal study, evaluating both the width of cervical movement by way of a goniometry and the tinnitus and neck pain by requesting a standard questionnaire to be filled out. Results: Sample was comprised of 147 individuals aged between 69.22, 61.90% of whom were female. Among these individuals, 42.85% showed a buzz complaint and 51% of these individuals claimed to have a neck pain. Neither was association found between tinnitus and neck pain nor it was between tinnitus and the width constraint of cervical movements. Conclusion: Despite not finding an association between tinnitus and neck pain or between tinnitus and the width constraint of cervical movements, there was a significant prevalence of tinnitus, neck pain and a reduction in the width of cervical movements on the elderly people. The results hereof, by finding this important prevalence of tinnitus in all the individuals of this study, will be the basis to integrate health professionals engaged in such alterations.

关键词： tinnitus neck pain vertebral column elderly zumbido cervicalgia coluna vertebral idoso

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Inventory control systems with safety stock and reorder point approach 1

Inventory control systems with safety stock and reorder poin...

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1st International Conference on Information and Communications Technology, ICOIACT 2018

作者： Efrilianda, Devi Ajeng Mustafid Isnanto, R. Rizal Master Program of Information Systems School of Post Graduate Studies Diponegoro University Semarang Indonesia Department of Statistics Faculty of Science and Mathematics Diponegoro University Semarang Central Java Indonesia Department of Computer Systems Faculty of Engineering Diponegoro University Semarang Indonesia

ISBN: (纸本)9781538609545

The inventory control systems has an important role to monitor the condition of the goods at the supply chain stage of goods distribution from the central warehouse to retail in accordance with consumer demand. The research aims to develop inventory control systems on the supply chain distribution of goods from suppliers to retail through the central warehouse with minimal supply chain costs. Minimization of supply chain cost using martingale model of forecast evaluation, and the result as the basis for calculation of safety stock and reorder point. The results of research are the system that can be used to control inventory and renewal of goods in the central warehouse by calculating the minimization of supply chain cost based on consumer demand. Case studies were conducted on the supply chain distribution of goods from the central warehouse to the retail in Semarang. The observation data derived from the goods distribution from central warehouse to retail for certain products over 4 time periods. Through such inventory control system, can minimize supply chain cost, and product inventory level can be maintained in time and amount of inventory that must be fulfilled. © 2018 IEEE.

关键词： Supply chains

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Static and dynamic panel models: Which is better? (Case study: Poverty data in Indonesia 2012-2019)

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IOP Conference Series: Earth and Environmental Science 2021年第1期739卷

作者： P R Sihombing A M Arsani Departemen of Statistics IPB University Bogor Indonesia Statistics Indonesia Applied Economics Master Program Padjadjaran University Bandung Indonesia

Indonesia's poverty rate continues to decline from year to year; however, the decline in the percentage of poor people is slowing down continuously, following the law of diminishing returns. Many factors influence poverty, including HDI, Gini ratio, and the open unemployment rate. This study model static and dynamic panel data on Indonesia's poverty cases in 2012-2019 to explain the relationship between HDI, Gini ratio, and the unemployment rate to poverty. It also compares poverty modeling with static and dynamic panel data regression. The results show that the Gini ratio has a significant and positive relationship to poverty. HDI has a significant and negative relationship to poverty. However, there is insufficient evidence to suggest that unemployment affects poverty. Using static panel data regression, the selected model is a fixed effect model but still experiences violations of the classic autocorrelation assumption. The dynamic panel data model gives better results than the static panel data regression model when viewed from the r-square value and the number of variables that significantly affect. From the results of this study, it can be implemented that an increase in economic growth without an increase in equity has no impact on poverty. So, it needs extra efforts by the government in economic equality for all regions of Indonesia. Also, there is a need for additional actions by the government and the community to improve education, health, purchasing power, and community competitiveness.

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

Generalized Spatio Temporal Autoregressive Rainfall-Enso Pat...

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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

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Ramadhan short-term electric load: A hybrid model of cycle spinning wavelet and group method data handling (CSW-GMDH)

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2019年第4期46卷

作者： Caraka, Rezzy Eko Chen, Rung Ching Toharudin, Toni Pardamean, Bens Bakar, Sakhinah Abu Yasin, Hasbi College of Informatics Chaoyang University of Technology Taichung City41349 Taiwan Department of Statistics Padjadjaran University Bandung Indonesia College of Informatics Chaoyang University of Technology Taichung City41349 Indonesia Bioinformatics Data Science Research Center Bina Nusantara University Indonesia BINUS Graduate Program-Master of Computer Science Bina Nusantara University. Indonesia School of Mathematical Sciences FST The National University of Malaysia Malaysia Department of Statistics Diponegoro University Semarang Indonesia

In general, performing a nonlinearity time series analysis in the modeling of data can reach a robust and increase the quality of the results. Wavelet methods have successfully been applied in a great variety of applications for modeling also forecasting. Wavelet Transform divided into two categories. There is continuous wavelet (CWT) and a discrete wavelet transform (DWT). Cycle spinning unlike the discrete wavelet transform (DWT), is highly redundant, non-orthogonal, also defined naturally for all sample sizes. There is a Group Method of Data Handling (GMDH) algorithm, which is a multivariate analysis method can be used in modeling and identifying uncertainty on linear also nonlinearity systems. In this paper, we aim to explain the combination of A-Trous wavelet transforms applied on cycle spinning and group method of data handling (GMDH) in data of short-term electric load holy month of Ramadhan from 2014 to 2015. © 2019, International Association of Engineers.

关键词： Discrete wavelet transforms

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Randomized Spline Trees for Functional Data Classification: Theory and Application to Environmental Time Series

arXiv

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arXiv 2024年

作者： Riccio, Donato Maturo, Fabrizio Romano, Elvira Machine Learning Engineer and Student in the Data Science Master’s Degree Program The University of Campania Luigi Vanvitelli Caserta Italy Department of Economics Statistics and Business Faculty of Technological and Innovation Sciences Universitas Mercatorum Rome Italy Department of Mathematics and Physics University of Campania Luigi Vanvitelli Caserta Italy

Functional data analysis (FDA) and ensemble learning can be powerful tools for analyzing complex environmental time series. Recent literature has highlighted the key role of diversity in enhancing accuracy and reducing variance in ensemble *** paper introduces Randomized Spline Trees (RST), a novel algorithm that bridges these two approaches by incorporating randomized functional representations into the Random Forest framework. RST generates diverse functional representations of input data using randomized B-spline parameters, creating an ensemble of decision trees trained on these varied representations. We provide a theoretical analysis of how this functional diversity contributes to reducing generalization error and present empirical evaluations on six environmental time series classification tasks from the UCR Time Series Archive. Results show that RST variants outperform standard Random Forests and Gradient Boosting on most datasets, improving classification accuracy by up to 14%. The success of RST demonstrates the potential of adaptive functional representations in capturing complex temporal patterns in environmental data. This work contributes to the growing field of machine learning techniques focused on functional data and opens new avenues for research in environmental time series *** Codes 62M10, 68T05, 65D07, 68T10 © 2024, CC BY.

关键词： Spatio-temporal data

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Revisiting Fuel Subsidies in Indonesia using K-Means, PAM, and CLARA

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2023年第3期50卷

作者： Prasetyo, Fajar Agung Caraka, Rezzy Eko Kim, Yunho Goldameir, Noor Ell Sulistyowati Tyasti, Avia Enggar Gio, Prana Ugiana Anggoro, Faisal Ramadhani, Muthia Pardamean, Bens Faculty of Economics and Business Universitas Indonesia Indonesia Indonesia Department of Mathematics Ulsan National Institute of Science and Technology Korea Republic of Department of Mathematics Statistics Study Program Riau University Riau Pekanbaru Indonesia Institut Teknologi Adhi Tama Surabaya East Java Indonesia International Trade of ASEAN and RRT Region Polytechnic of APP DKI Jakarta Indonesia Department of Mathematics Universitas Sumatera Utara Medan20155 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

Indonesia is one of the countries in the world that still applies subsidies for fuel oil. By the law, the Indonesian government must ensure the supply and distribution of fuel for all Indonesian people. To implement this policy properly, understanding the pattern of fuel consumption is fundamental. In this study, clustering will be used to determine the categories of districts and cities based on subsidized fuel consumption patterns. Our research used several methods to compare which cluster method is the most optimal;the methods include k-means, Partitioning Around Medoids (PAM), and Clustering Large Applications (CLARA). The results show that k-means is the best clustering method with the highest Dunn index and Silhouette coefficient compared to others. The optimum cluster number we get is two and represents fuel consumption from several districts and cities. Some districts and cities had under-average consumption and needed to be monitored. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： K-means clustering

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Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

Clustering Analysis of a Spatiotemporal Dataset with a Novel...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jen-Chien Yu Chun-Chieh Yang John Reuben Gilbert Rou-Jun Liu Yen-Jen Oyang Meng-Han Yang Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung City Taiwan ROC Master Program in Statistics National Taiwan University Taipei Taiwan ROC Department of Computer Science & Information Engineering National Taiwan University Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset.

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Naphtha’s price forecasting using Neuro-fuzzy system

Naphtha’s price forecasting using Neuro-fuzzy system

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SICE Annual Conference

作者： Porntip Visetsripong Pitikhate Sooraksa Pramote Luenam Watchareeporn Chaimongkol Joint Master of Science Program in Information Technology Management National Institute of Development Administration Bangkok Thailand Faculty of Engineering ing Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Information Systems Education Center National Institute of Development Administration Bangkok Thailand School of Applied Statistics National Institute of Development Administration Bangkok Thailand

Naphthapsilas price forecasting using Neuro-fuzzy system is a forecasting technique that applied information technology with statistics. 1950 daily prices were collected as a time-series data with trend component. The research found that Neuro-Fuzzy system is more accurate and more reliable than a statistical method; it also works well with continuous data and performs better with more training data. Neuro-Fuzzy system might be used with different data type, but, it might come across with other factors, e.g. seasonal or irregular event. The research also illustrates the multidisciplinary nature in todaypsilas world of works in the era of merging among many disciplines.

关键词： Forecasting Predictive models Smoothing methods Neurons Market research Time series analysis Data models

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