bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modeling The Amount of Insurance Claim using Gamma Linear Mixed Model with AR (1) random effect
Authors: Adam F A , Kurnia A , Purnaba I G P , Mangku I W , Soleh A M 
Published: April 2021

AbstractThe amount of insurance claims is continuous data and is positive so it is usually assumed to have gamma distribution. Usually, Generalized Linear Model (GLM)’s approach is used since the gamma distribution is a member of the exponential family. In case there is a random effect in modeling, then GLM can be extended to Generalized Linear Mixed Model (GLMM). This study models the amount of insurance claims with the most GLMM’s approach using two random effects, namely the region and time of the occurrence which is assumed to follow a first-order autoregressive process. The h-likelihood method is used to estimate the regression parameters and the variance parameters. A simulation study is carried out with an evaluation using the average relative bias and the average MSE. An application study which is conducted to model the amount of insurance claims in a certain region and time based on the 2014 profile of risk and loss of motor vehicle insurance in Indonesia is also carried out.
Robust stochastic frontier using Cauchy distribution for noise component to measure efficiency of rice farming in East Java
Authors: Zulkarnain R , Indahwati  
Published: April 2021

Abstract
Several studies have been conducted to estimate efficiency of rice farming in Indonesia. Those studies generally apply standard stochastic frontier (SF) model which assume that noise component has normal distribution. However, the normality assumption of noise is inadequate if the outliers are present. This paper employs Cauchy distribution for noise component to produce a more robust SF model. There are two variants of models that are employed: Cauchy-Half Normal and Cauchy-Exponential models. These models are applied to measure technical efficiency of rice farming in East Java. The findings presented in this study are based primarily on data from Cost Structure of Paddy Cultivation Household Survey 2017 (SOUT2017-SPD) that is compiled by Statistics Indonesia. Output and input variables are linked using transcendental logarithmic production function, while the parameters are estimated using maximum simulated likelihood. The results showed that Cauchy-Half Normal and Cauchy-Exponential models effectively reduce the effect of outliers. The performance of model is improved after the outliers are handled. Cauchy-Half Normal and Cauchy-Exponential models revise the technical efficiency scores of several rice farming units.
Robust multi-stage method (MM) and least median square (LMS) evaluation on handling outlier for multiple regression
Authors: Khotimah Khusnul , Sadik Kusman , Kurnia Anang 
Published: April 2021

Abstract
Outliers are observation with extreme values that are very different from most other data. The least squares method (OLS) is a method that is very sensitive to outliers because it can cause the modeling accuracy decrease. Throwing out outliers with the aim of improving the suitability of the regression equation cannot be done carelessly because it will provide imprecise estimation precision. This study examines the performance of robust method of the least median squares (LMS) and the multi-stage method (MM) compared to OLS in a regression analysis of data which contains outliers. Data analysis was performed on simulation data and oil palm production data. Based on the average parameter estimate bias value, MM method has the best performance in each scenario condition of data size and outlier percentage, while based on the average root mean squares error (RMSE), LMS has better performance than MM when the data size is 25. Analysis of Indonesian oil palm production data in 2018 which data size 25 and contains 44% outliers resulted the conclusion that LMS method produced smallest RMSE and highest R2, namely 38.81 and 99.78%, respectively. MM method is in the second best position, while OLS produces largest RMSE and highest R2.
Quality Control of Labelstock Using Fuzzy Exponentially Weighted Moving Average (FEWMA) Control
Authors: Pratiwi R M , Wibawati  , Ahsan M , Mashuri  , Khusna H 
Published: April 2021

Abstract
Production development in the industrial era sues the company to continue creating the best product for consumer society. Product quality is the main consideration for the consumer in selecting products. This is certainly driving the company to maintain the quality of its products. Therefore, companies need to control the quality of production so their products can fulfill the standards set. One of the ways that can be used to control quality is using the control chart. The most frequently used control chart is the Shewhart type control chart. However, the Shewhart control chart can only be used for crisp data, while for data containing ambiguity, a fuzzy control chart can be applied. In this research, the thickness of the label stock glue quality on HVS P 60 is monitored using the fuzzy control chart. The type of fuzzy control chart used in this study is the FEWMA (Fuzzy Exponentially Weighted Moving Average) control chart. This control chart was chosen because it can monitor data that contains ambiguity and data with small process shifts. In this research, rigorous inspection (α-cuts) was used to detect shifting data. Based on the analysis, it was found that the weight (λ) 0.1 is the optimum weight value in detecting a process shift in the thickness of the label stock glue. Through the cause and effect diagram, four factors that cause the out of control data was found, namely machine, method, man, and material.
Spatial Regression Model with Optimum Spatial Weighting Matrix on GRDP Data of Sulawesi Island
Authors: Paramita N , Masjkur M , Indahwati  
Published: April 2021

Abstract
The two main spatial regression models are the spatial autoregressive model (SAR) and spatial error model (SEM). The extension of the SAR model is a spatial Durbin model (SDM), which considers the spatial dependence of response and explanatory variables. However, the determination of the spatial weight matrix is critical for the best estimation results. We consider two distance-based spatial weight matrices, i.e., the k-Nearest Neighbour (k-NN) and Inverse Distance Weighting (IDW). The objective of this study was to compare the performance of the Ordinary Least Squares (OLS) regression, SAR, SEM, and SDM models with k-NN and IDW on the estimation of Growth Regional Domestic Product (GRDP) and identify the critical factors that influence the value of GRDP of Sulawesi island. The study used the GRDP data of 81 districts/cities in Sulawesi island in 2018 with six explanatory variables. The results show that the 4-NN weighted SAR model outperforms the OLS, the 4-NN SEM, SDM models, IDW SAR, SEM, and SDM models. The factors that influence the value of GRDP in Sulawesi island are HDI (Human Development Index), population size, open unemployment, small/micro and medium industries, and the spatial lag autoregressive coefficient.
Mixed Geographically and Temporally Weighted Regression Analysis of Percentage of Poverty in Java Island
Authors: Djuraidah A , Indahwati  , Asianingrum A H 
Published: April 2021

Abstract
Regression analysis is a method that can be used to analyze the linear relationship between explanatory variables with a response variable to analyze problems. The problem of poverty is one of the fundamental problems and is the center of government attention in every country especially developing countries like Indonesia, precisely in Java Island that have the largest number of poor people. In poverty data, there is spatial and temporal diversity. Besides, potential differences between regions in Indonesia allow for the diversity of data and can be locally and globally addressed so that the MGTWR method can be used. In this study using data on the percentage of poor people in regency/municipality in Java in 2012-2018. MGTWR modeling results show that the GDRP, literacy numbers, and EYS variables are global variables. Whereas the last variable of education was completed by elementary school, per capita expenditure, Raskin recipient households, residents of the 15-64 age group, and MYS were local variables. Based on the Bisquare kernel function, the MGTWR model is the best model to modeling the percentage of poor people in regency/municipality in Java with the smallest AIC value of 198.75 and PseudoR2 the biggest is 72.92%. In MGTWR modeling, 5 regional groups are formed based on factors that influence simultaneously.
Study of Forecasting Method for Agricultural Products Using Hybrid ANN-GARCH Approach
Authors: Jannah Miftahul , Sadik Kusman , Afendi Farit Mochamad 
Published: April 2021

Abstract
The capital city of Jakarta as the center of socio-economic activities has to maintain the stability of its economy, including the stability of staple food prices such as rice, onion, and chili prices. To make a strategic plan for anticipatory action, one of the ways is by doing forecasting. The problem in forecasting staple food prices is the fact that those staple foods have high volatility prices. It makes the conditional variance of residual become inconstant. This research applied the Hybrid ANN-GARCH model that combined Artificial Neural Network (ANN) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. This method has used to predict rice, onion, red chili, and cayenne pepper prices in Jakarta. The data divided into two categories, training and testing data. The result shows that the Hybrid ANN-GARCH model produced smaller RMSE and MAPE values than ARIMA model. Based on the accuracy value of the model, it can be concluded that Hybrid ANN-GARCH better than ARIMA in forecasting the price of the four commodities.
Impacts of Earthquakes on Consumer Price Index and Inflation: A Case Study in West Nusa Tenggara Province, Indonesia
Authors: Wibowo Wahyu , Purwa Taly , Abdul Bahri Elya Nabila , Suprih Ulama Brodjol Sutijo , Wilantari Regina Niken 
Published: April 2021

AbstractSince Indonesia is located in the Ring of Fire, it is well understood that it has been highly vulnerable to earthquakes. Earthquakes can have many effects, including infrastructure damage and socioeconomic disruption. During 2009 to 2019, West Nusa Tenggara Province had the most earthquake frequencies. This paper aims to investigate the impacts of earthquakes on the regional economy of West Nusa Tenggara Province on the consumer price index (CPI) and inflation using the autoregressive integrated moving average with exogenous variables (ARIMAX) method. The data used in this paper are monthly CPI and the inflation in West Nusa Tenggara Province from January 2008 to December 2018. Based on the modeling process, two models for CPI and inflation are obtained. The forecast values of CPI are converted to inflation values to produce an indirect inflation forecasting, and the RMSE of four models are compared. The overall best model, with the smallest RMSE, for inflation is ARIMAX with stochastic trend and seasonal variable, which indicates that direct forecasting using inflation data is better than indirect inflation forecasting using CPI. From the best model, the earthquake effect, i.e., the real and estimated effects, has positive and negative effects on CPI and inflation with the magnitude of the real effect that is larger than the estimated effect. These two conditions indicate that the model cannot forecast well the earthquake effect. Therefore, much greater anticipation is necessary from local governments regarding the impact of the earthquake on the prices of essential commodities that is likely to occur in the future.
New normal policy on the Rupiah exchange rate using Long Short-Term Memory
Authors: Pontoh R S , Zahroh S , Sunengsih N 
Published: April 2021

Abstract
Since the world is facing health problems due to COVID-19, Indonesia was not success to surge the outbreak. In addition to the risk of health problems, this pandemic is also disrupting the global economy. The rupiah exchange rate also saw a devaluation during the COVID 19 outbreak. The Indonesian economy is expected to grow negatively in the third quarter of 2020 and predicted to continue until the end of the year. The measure from the government impacted the economic circumstances then along with the decline in new cases of the spread of COVID-19 the government was implementing a new normal, after the completion of large scale social restrictions weakened the economic development, with one of its goals to improve and try to save Indonesia’s economy from a possible worse recession. This study attempts to use the forecasting method to find out whether the application of the new normal will strengthen the Rupiah exchange rate in the coming period. The methods that will be used is Long Short Term Memory as the best model to overcome long-term dependencies to obtain a predictive model that most closely approximates existing data patterns. The most suitable model is Long Short Term Memory with 50 epochs using five hidden neurons. Based on the results, it seems that the Rupiah exchange rate tends to weaken in the next five-day.
Recognizing poverty pattern in Central Java using Biclustering Analysis
Authors: Putri C A , Irfani R , Sartono B 
Published: April 2021

Abstract
Poverty is a complex and multidimensional problem and becoming a development priority. In analyzing the pattern of poverty in a region, one of the statistical procedures that is usually used is the cluster analysis. However, it does not consider the different levels of performance by region in different characteristics at a particular time. In this study, an alternative approach, namely Cheng and Church’s biclustering algorithm, was used to simultaneously identify the poverty pattern in Central Java by region and poverty dimension variables. Using this algorithm, we found two biclusters with different characteristics. The first bicluster represents the general condition of poverty in Central Java, but they are better in labor and housing quality indicators. While the second bicluster is poorer in some indicators of labor, education, and housing quality than Central Java.
Classification of Paddy Growth Phase Based on Landsat-8 Image with Convolutional Neural Network Algorithm
Authors: Izah R N , Kurnia A , Sartono B 
Published: April 2021

Abstract
Food security is one of the major issues in the world. One of the related component of the issue is the provision of agricultural data. In Indonesia, there is a difference of rice production data between the Ministry of Agriculture and Statistics Indonesia (locally known as BPS). This is due to difference in data acquisition methods used. In order to predict rice production, we must know about paddy growing phase so that the prediction of rice production in a certain period can be accurately calculated. This research aims to propose a convolutional neural network method to develop paddy growth phase classification model using remote sensing data of landsat-8 images. The convolutional neural network algorithm is used to perform self-learning processes such as reading image, extracting and classifying by changing the structure of landsat-8 image into matrix or pixel. This research also conducted experiment against hyper parameter epoch with 10, 30 and 50 epochs to get good accuracy. The case research is the area of PT Sang Hyang Seri rice field in Subang Regency, Indonesia. The experiment results provided an accuracy of 80.37%. It means the convolutional neural network model is matched with the data. It is able to classify landsat-8 images.
Atomic-level evolutionary information improves protein–protein interface scoring
Authors: Quignot Chloé , Granger Pierre , Chacón Pablo , Guerois Raphael , Andreani Jessica 
Published: April 2021

Abstract

Motivation
The crucial role of protein interactions and the difficulty in characterizing them experimentally strongly motivates the development of computational approaches for structural prediction. Even when protein–protein docking samples correct models, current scoring functions struggle to discriminate them from incorrect decoys. The previous incorporation of conservation and coevolution information has shown promise for improving protein–protein scoring. Here, we present a novel strategy to integrate atomic-level evolutionary information into different types of scoring functions to improve their docking discrimination.

Results
We applied this general strategy to our residue-level statistical potential from InterEvScore and to two atomic-level scores, SOAP-PP and Rosetta interface score (ISC). Including evolutionary information from as few as 10 homologous sequences improves the top 10 success rates of individual atomic-level scores SOAP-PP and Rosetta ISC by 6 and 13.5 percentage points, respectively, on a large benchmark of 752 docking cases. The best individual homology-enriched score reaches a top 10 success rate of 34.4%. A consensus approach based on the complementarity between different homology-enriched scores further increases the top 10 success rate to 40%.

Availability and implementation
All data used for benchmarking and scoring results, as well as a Singularity container of the pipeline, are available at http://biodev.cea.fr/interevol/interevdata/.

Supplementary information
Supplementary data are available at Bioinformatics online.
Scalar-on-function local linear regression and beyond
Authors: Ferraty F , NAGY S 
Published: April 2021

SummaryIt is common to want to regress a scalar response on a random function. This paper presents results that advocate local linear regression based on a projection as a nonparametric approach to this problem. Our asymptotic results demonstrate that functional local linear regression outperforms its functional local constant counterpart. Beyond the estimation of the regression operator itself, local linear regression is also a useful tool for predicting the functional derivative of the regression operator, a promising mathematical object in its own right. The local linear estimator of the functional derivative is shown to be consistent. For both the estimator of the regression functional and the estimator of its derivative, theoretical properties are detailed. On simulated datasets we illustrate good finite-sample properties of the proposed methods. On a real data example of a single-functional index model, we indicate how the functional derivative of the regression operator provides an original, fast and widely applicable estimation method.
Phenology, Development, and Parasitism of Allium Leafminer (Diptera: Agromyzidae), a Recent Invasive Species in the United States
Authors: Lingbeek Brandon , Roberts Dana , Elkner Timothy , Gates M , Fleischer Shelby J 
Published: April 2021

Abstract
Allium leafminer, Phytomyza gymnostoma (Diptera: Agromyzidae), is an invasive species first recorded in the Western Hemisphere in 2015 and has expanded its range into northeastern and MidAtlantic states. Its host range encompasses Allium species grown for food and ornamentals, weedy species, species used for pollinator provisioning, and species of conservation concern. Using field and laboratory studies, we advanced methods for rearing, developed a phenology model for spring emergence, describe pupal development, and report on parasitism. Spring emergence was best detected by scouting wild alliums as opposed to emergence cages, and modeled using 350 degree-days above a lower threshold of 1.0°C. Spring adult flight occurred for about 5 wk. Larval development required 22 and 20 d at 17.5 and 25°C, respectively. Pupal development progressed along a color gradient, and an initial presence of fat cell clusters and an air bubble, followed by an exarate pupa. Pupal developed at 3–5% per day at 3°C and reached 25% per day at 21.5°C, but development was not successful at 30°C. Although parasitism rates were low, we documented two Chalcidoidea parasitoids, Halticoptera circulus (Walker) (Hymenoptera: Pteromalidae) and Chrysocharis oscinidis Ashmead (Hymenoptera: Pteromalidae). Together, these data provide baseline information to advance IPM for this invasive species both in crops and noncrop areas.
Three hurdles towards commercialisation: integrating subsistence chickpea producers in the market economy
Authors: Tabe-Ojong Martin Paul Jr , Mausch Kai , Woldeyohanes Tesfaye B , Heckelei Thomas 
Published: April 2021

Abstract
Enhancing agricultural productivity through the adoption of improved technologies presents a credible pathway to economic development and poverty reduction especially through increased commercialisation of production. We used a triple hurdle (TH) model to estimate the production and commercialisation of smallholder farmers in Ethiopia. In doing so, we account for the adoption of improved Cicer arietinum (chickpea) varieties on commercialisation using a three-wave panel data set. We estimate a correlated random effect model with a control function and find the adoption of improved chickpea to have a significant positive effect on smallholder commercialisation. Our findings that are robust over different specifications and identification strategies also support the role of transaction cost in driving market participation (MP). Finally, we argue that the TH model is a better fit to the commonly used double hurdle model for MP, when not all households in a population produce a particular crop.
A new technique for tricuspid valve repair addressing the subvalvular apparatus in functional tricuspid regurgitation
Authors: Doenst Torsten , Faerber Gloria 
Published: April 2021

Abstract
Annular dilatation is the main mechanism for tricuspid regurgitation, but right ventricular dilatation often adds a restrictive mechanism, which may limit durability. We describe a subvalvular technique anchoring the chordal origins to the annuloplasty, with the aim to stabilize valve geometry and increase durability. A Goretex suture is attached to the anterior papillary muscle. One arm of the suture is stitched through the septal muscle and both arms are atrialized underneath the septal leaflet and tied to the annuloplasty band. In 12 patients (75 ± 6 years, EuroSCORE II 10 ± 9%), severe-torrential tricuspid regurgitation was successfully reduced to mild. Results were stable in all but one patient during follow-up (1–15 months). NYHA class and general health status was improved. This subvalvular technique is safe with the potential to generate a durable repair.
The notion of almost zeros and randomness
Authors: Leventides J , Karcanias N , Poulios N C 
Published: April 2021

Abstract
We investigate the problem of almost zeros of polynomial matrices as used in system theory. It is related to the controllability and observability notion of systems as well as the determination of Macmillan degree and complexity of systems. We also present some new results on this important invariant in the light of randomness and we prove an uncertainty type relation appearing in such ensembles of operators.