bluuebunny/crossref_metadata_2025_split huggingface dataset:
Trapped field evaluation of ring-shaped GdBCO bulk magnet excited by pulsed field magnetization
Authors: Yokoyama K , Oka T 
Published: April 2021

Abstract
Recently, physical and mechanical properties of REBCO bulk are improved, and, accordingly, various practical applications are considered. As one of them, a portable bulk NMR system has been developed, and, then, the improvement of the system for practical use are progressing. In that system, 6 stacked bulks were magnetized by a superconducting magnet for NMR, resulting that the magnetic field of 4.74 T and inhomogeneity of 6.9 ppm were achieved. In this paper, we fabricated the bulk magnet with the room-temperature bore specialized in pulsed-field magnetization. The ring-shaped bulk was activated by pulsed-field magnetization, and, magnetic fields on the bulk surface and in the room-temperature bore of bulk magnet were evaluated. In the bore, the trapped field was approximately 0.79 T and homogeneity was over 150000 ppm.
The Impact of Relapse Rate on Deterministic Epidemiological Models with Pseudo-recovery
Authors: Qunia Anisa Fatta , Kusnanto Ali , Sianturi Paian 
Published: April 2021

Abstract
The deterministic epidemiological model with pseudo-recovery called the SEIRI model is a model that describes disease transmission in a population. Pseudo-recovery is a term for individuals who have recovered of infection, but some of them might be re-infected. This research aims to reconstruct the model, to analyse stability of fixed point and sensitivity of parameters. Also, to carry out numerical simulations upon combination of parameter values. The stability itself was determined using the Lyapunov functions. In this work, the sensitivity analysis was focussed on the effects of the effective contact rate and relapse rate on basic reproduction numbers. Both the effective contact rate and relapse rate increase with basic reproduction number. This may suggest that controlling the spread of the diseases can be done by decreasing both the effective contact rate or the relapse rate.
Disease Mapping of Leprosy in Maluku Province, 2019 using Hierarchical Bayes
Authors: Mahdy I F , Fitriani R , Revildy W D 
Published: April 2021

Abstract
Leprosy is a chronic disease caused by Mycobacterium.lepreae that affects the nerves of the extremities, skin, nasal mucosa, and upper respiratory tract. As of today, some countries include Indonesia facing difficulties to fight leprosy. The health ministry of Indonesia noted that the majority of leprosy cases occurred in the eastern region, like Maluku Province. Therefore disease mapping is needed to develop a map of leprosy so that the handling of this disease could operate optimally, especially in Maluku. One of the disease mapping techniques is Hierarchical Bayes (HB), which involves a measure of uncertainty compared to other methods such as Standardized Mortality Ratio (SMR). The results showed that the relative risk of leprosy in Maluku was almost the same in each region. It indicates that the disease is prevalent in Maluku, with the highest value in Ambon City and the lowest value in the Aru Islands.
Literacy Description of Probability for the Senior Secondary School Students in Makassar City
Authors: Tiro Muhammad Arif , Ruliana  , Aswi Aswi 
Published: April 2021

Abstract
This development research has obtained an assessment model of probability literacy in senior secondary school students. Such literacy is crucial because the statistical assessment as part of mathematics today in senior secondary schools tend to emphasize technical procedures with many mathematical formulae. Literacy of probability consists of five basic competencies, namely: (1) understanding the concept of probability, (2) insight into the application of the probability concepts, (3) skills in calculating the value of probability, (4) careful interpretation of the probability value, and (5) skills in visualization and communication of probabilities and conclusion that may be drawn from them. This study maps ability levels in these five competencies across Makassar City, identifying weaknesses in several questions about probability literacy that need to be revised. The emphasis on the concept of literacy is a major strategy to improve learning in secondary schools. These five competencies are mapped according to the type of questions that refer to the guidelines for implementing the skills of the 21st-century Curriculum 2013 in senior secondary school. The level of probability literacy achieved by high school students in Makassar is categorized as medium (47%-72%), highlighting an overall need for improved learning strategies that emphasize the five basic competencies. Additionally, morning classes are seen to correspond to a higher level of achievement than afternoon classes, for all five competencies.
Designing Rainfall Index Insurance for Rubber Plantation in Balikpapan
Authors: Azka Muhammad , Fauziyyah  , Hasanah Primadina , Wira Dinata Syalam Ali 
Published: April 2021

Abstract
Rubber commodity is one of the essential agricultural sectors in the City of Balikpapan. Rubber plantation has become a leading product to boost the economic growth in Balikpapan from the agricultural sector. However, this commodity is vulnerable through the changes of rainfall index since less rainfall can decrease the amount of latex in a rubber tree. This unexpected event will make a financial loss to farmers because of delaying the cultivation time. Therefore, the crop insurance for rubber farmers should be discovered. This study applied the Burn Analysis method to determine some classes of rainfall index and calculating the crop insurance premiums based on Black Scholes methods. The lowest class of rainfall index is 1264.36 mm with the crop insurance premiums at this class is Rp. 521,482.73. Otherwise, the highest rainfall index is 1761,36 mm whereas the premium level is Rp.568,592.93.
Comparison of GLM, GLMM and HGLM in Identifying Factors that Influence the District or City Poverty Level in Aceh Province
Authors: Rusyana A , Kurnia A , Sadik K , Wigena A H , Sumertajaya I M , Sartono B 
Published: April 2021

Abstract
The purpose of research is to evaluate the GLM, GLMM and HGLM models to poverty data in Aceh Province and then identify the best model. The response variable is the percentage of district or city poverty while the fixed effect is population density, sex ratio, the number of populations, the number of industries, area types, percentage of PLN user, poverty line and percentage of productive age group. The random effect for the GLMM and HGLM models is the average monthly expenditure. The data in 2019 were taken from website of the Aceh Central Statistics Agency (BPS) on April 13, 2020. For aggregate, the response variable and the random effect met the normal and gamma distribution. the results showed that the population density has an influence on the percentage of poverty in the GLM and HGLM, while in the GLMM, there are no factors that affect it. The scores of determination coefficient (R2) for GLM, GLMM and HGLM were 75.795%, 68.441% and 75.881%, respectively whereas scores of RMSE of them were 0.121, 1.917 and 0.120, respectively. Because the HGLM model has the largest R2 and the smallest RMSE, the HGLM was said to be the best model for the case.
Numerical Prediction of paddy weight of Crop Cutting Survey using Generalized Geoadditive Linear Mixed Model
Authors: Ardiansyah M , Kurnia A , Sadik K , Djuraidah A , Wijayanto H 
Published: April 2021

Abstract
Rice production data is needed to support the information about achieving the second SDGs. Rice production data requires rice productivity data obtained from Crop Cutting Survey by BPS-Statistics Indonesia. The problem is that the measurement of unhulled rice weight in this survey is not always successful. This problem causes the unhulled rice weight data to be missing values. We proposed Geo-GLMM with covariate interaction to estimate the missing values. The proposed methods was compared by GLM, GLMM, and Geo-GLMM. The results showed that seed varieties, TSP/SP36 fertilizer, NPK / compound fertilizer, urea, organic fertilizer, the number of clumps per plot, pest attack, and climate impacts significantly affected rice productivity. Then, we selected the variables and got the best explanatory variables, namely seed varieties, fertilizer, interaction between urea and KCL fertilizer. Geo-GLMM with fertilizer interaction has better prediction performance than GLM, GLMM, and Geo-GLMM without interaction. Based on the results of the simulations, the Geo-GLMM with covariate interaction produces a smaller bias and RMSE. Therefore, it is recommended that the surveyors of Crop Cutting Survey continue to interview farmers when they fail to take sample plots, so we get covariate data can be used to estimate the unhulled rice weight.
A Bayesian approach for Generalized Linear Model Using Non-local Prior (Case Study: Poverty Status in East Java)
Authors: Dwinata A , Kurnia A , Sadik K 
Published: April 2021

Abstract
Generalized Linear Model (GLM) is an extension of the linear regression model. GLM is useful for determining the effect of predictor variables to the response variable that is a member of the exponential distribution family. This study uses a binary response variable having a Binomial distribution. This modelling uses the logit link function. This study aims to evaluate the performance of the Bayesian GLM modelling using local prior distribution and non-local prior distribution. The non-local prior density in this study was pi-MOM. The results of the research on modelling poverty status in East Java using the local prior distribution produced a predictor variable that had a significant effect on poverty status in East Java. Meanwhile, modelling using non-local prior distribution produced three predictor variables that significantly affected the status of poverty in East Java. The goodness criteria of the model showed that the Bayesian GLM modelling using the prior non-local distribution was the best model with the smallest misclassification and the lowest AIC value.
The Application of Firth Bias Correction in Variance Components Estimation of Clustered Random Intercept Binary Model
Authors: Arisanti R , Sumertajaya I M , Notodiputro K A , Indahwati  
Published: April 2021

AbstractFirth Bias Correction has been discussed in literatures as an alternative method for reducing the bias of variance components in generalized linear mixed model. This paper discusses the application of Firth’s correction in a clustered random intercept binary model in which the penalized quasi likelihood method is utilized. The clustered random intercept binary model is usually applied in clustered data in which the data can be naturally clustered. In this paper the Firth’s correction has been applied to low birth weight data in Indonesia to investigate factors affecting the incidence of the low birth weight as well as its variation within and between clusters.
Smartphone hedonic price study based on online retail price in Indonesia
Authors: Listianingrum T , Jayanti D , Afendi F M 
Published: April 2021

Abstract
The price of a smartphone is determined by its series of specifications, which makes it suitable to be modelled with a hedonic approach. We collected smartphone prices from various e-commerce along with the specifications using web scraping method. This study demonstrates statistical analysis of smartphone prices in Indonesia by constructing a hedonic model and investigate key variables determining smartphone price. An OLS regression model is employed to total of 1120 handsets data to construct a hedonic model. In addition, random forest regression and XGBoost regression were used to determine the variable importance. The results indicated that only 2 of 5 brands are significantly influence the price of smartphones; Xiaomi and Realme. Meanwhile, the other variables such as RAM, Internal Storage, NFC, Screen Size and Weight have significant positive effect on smartphone prices. In contrast to the use of brand as a weighing basis for calculating CPI, we discovered that RAM is the ultimate key variable determining smartphone prices.
Spatial autoregressive stochastic frontier model with application to Indonesia’s aquaculture
Authors: Zulkarnain R , Djuraidah A , Sumertajaya I M 
Published: April 2021

AbstractStochastic frontier analysis (SFA) is one of the popular methods for measuring efficiency and productivity. SFA generally assumes that decision making units are independent. However, this assumption may be inappropriate since decision making units are probable interdependent depending on their spatial proximities. This paper aims to incorporate spatial lag into standard SFA model. The performance of spatial autoregressive stochastic frontier model (SAR-SFA) is compared to standard SFA using simulation and real data. Simulation study is undertaken using various degrees of spatial autocorrelation. Furthermore, SAR-SFA is applied to estimate technical efficiency of aquaculture in Indonesia. Simulation results showed that the SAR-SFA generally produces better performance than the standard SFA. If spatial dependencies are significant, then the estimation of production frontier from standard SFA could be misleading. It affects the distribution of technical efficiency and its ranking. Moran’s I test indicated that Indonesia’s aquaculture has significant positive spatial autocorrelation. If standard SFA is applied to estimate technical efficiency of Indonesia’s aquaculture, then the resulted technical efficiency is implausible. This issue is well handled by the SAR-SFA model. Moreover, SAR-SFA model serves procedure to estimate efficiency spillovers.
Dynamic Spatial Durbin Modeling (Case: Percentage of Poor Population in Indonesia 2010-2019)
Authors: Sunandi E , Sumertajaya I M , Djuraidah A , Wigena A H , Fitrianto A 
Published: April 2021

Abstract
The dynamic panel model assumes that each observation unit is independent of each other. But sometimes this assumption is violated, so there are spatial effects in the model. This study aimed to make percentage modeling of poverty in Indonesia using the Dynamic Spatial Durbin Model (DSDM). The data used in this study were secondary data obtained from the Statistics Indonesia in the period 2010-2019. The parameter estimation method used in this model was the Maximum Likelihood (ML). According to Moral-Benito, Allison and Williams the ML method has the best performance when used on panel data that has small cross-section data dimensions. The results of this study indicated that spatial dependence, time lag, variables of Poverty Gap Index, Poverty Severity Index and Logarithm of Expenditures per Capita affect poverty in Indonesia significantly. Other results showed that DSDM was able to explain the diversity in models at 95.6 %. This seemed higher when it’s compared to the Spatial Durbin Model (SDM). Thus, the result of the study proved that DSDM is the best model for modelling dynamic poverty data panel in Indonesia during 2010-2019.
Mixed Geographically and Temporally Weighted Autoregressive to Modeling the Levels of Poverty Population in Java in 2012-2018
Authors: Djuraidah Anik , Djalihu Hartinah , Soleh Agus Mohamad 
Published: April 2021

Abstract
Geographically and temporally weighted regression (GTWR) is a method applied when there is spatial and temporal diversity in the observation. In the GTWR model not all variables were local or insignificant (global in nature) so the GTWR model was developed into Mixed Geographically and Temporally Weighted Regression (MGTWR). The GTWR model can also add an autoregressive component of response variable, the resulting model is known as a geographically and temporally weighted autoregressive model (GTWAR). This study develops the MGTWR model and the GTWAR model which produces a Geographically and Temporally Weighted Autoregressive model (MGTWAR) which is used to model data on the percentage of poor people in regency/municipality on Java Island in 2012-2018. The results showed that MGTWAR produced Akaike Information Criterion (AIC) smaller than GTWR, and the coefficient of determination (R2) is higher than GTWR.
Development of the Panel ARDL by Adding Space-Time effect to Modeling Monthly Paddy Producer Price in Java
Authors: Ardiansyah M , Djuraidah A , Sumertajaya I M , Wigena A H , Fitrianto A 
Published: April 2021

Abstract
Classic panel data modelling has large cross section data (N) and small time series data (T). The aim of the study was to develop a panel data type T > N by adding space-time effect to a panel ARDL model. The basic idea was to combine between the AR (Autoregressive), DL (Distributed Lag), and ST (space-time) effect. The model was applied to paddy producer price at the farmer level in Java from January 2016 to December 2019 where the explanatory variable was the Farmers’ Terms of Trade. Both variables were stationary in the first-difference I (1). The results showed that the ST-ARDL model was good for T > N panel data types. The ST-ARDL model with reparameterization of explanatory variables was able to overcome the problem of multicollinearity. The ST-ARDL model was able to improve the performance of the classic panel data model which was able to reduce the RMSE and increased R2-adj. The linear combination of this model was cointegrated or had a long-term equilibrium relationship. Another result of the study was the ST-ARDL model provided better estimation performance than the AR (p), ARDL (p, q) and GSTAR (p, λ) models with the smaller MAPE values. For further research, the ST-ARDL model can be developed by adding the effect of space-time interaction.
Study of X-13 ARIMA SEATS Modeling for Rice Price Index Data in Indonesia
Authors: Putri Agnes Maludfi , Sadik Kusman , Afendi Farit Mochamad 
Published: April 2021

Abstract
Outlier on a data can cause several problems, including a model that is formed to produce a large residual, and the variance of data will increase. A robust method for outlier problems is needed in order to produce an unbiased model. X-13 ARIMA SEATS is one of robust method in time series. X-13 ARIMA SEATS is a seasonal adjustment method capable of detecting, resolving the presence of outliers, and overcoming seasonal and calendar effect. The X-13 ARIMA SEATS consists of two stages that is using regARIMA model, and using X-12 seasonal adjustment. In this paper, X-13 ARIMA SEATS method is compared with ARIMA method to prove its ability to overcome outliers based on the smallest error value. In this paper, the X-13 ARIMA SEATS method is applied to model and forecast the rice price index in Indonesia in January 2010-December 2018. The results showed that the X-13 ARIMA SEATS method produces a model with smaller MAPE and RMSE values than the ARIMA model.
Classification modeling of support vector machine (SVM) and random forest in predicting pharmacodynamics interactions
Authors: Farhana N A , Afendi F M , Fitrianto A , Wijaya S H 
Published: April 2021

AbstractDrug-drug interaction (DDI) is a drug’s effectiveness that can affect the body’s response to the treatment process. DDI occurs when food, drinks, chemicals, and other drugs change the effectiveness of a drug that is given simultaneously. One type of DDI is pharmacodynamics interactions. This interaction is difficult to detect and is very dangerous to humans. Therefore it is necessary to do classification modeling to identify pharmacodynamics interactions based on the value of Side Effect Similarity (SES), Chemical Similarity (CS), and Target Protein Connectedness (TPC). The Support Vector Machine (SVM) and random forest classification method that can be used to predict pharmacodynamics interactions. This study aims to find the best classifications technique by first applying the scaling process, variables interaction, resampling technique, and binarization technique. Best on the analysis result obtained by the random forest is the best model with the highest accuracy and AUC value to other models. The accuracy and AUC values for the best models are 89.93% and 79.96%.
Affecting mechanical on some growth properties for corn, MAHA cultivar
Authors: Alwan Alsharifi Salih K. , Shtewy Naim , Imran Alaamer Shathar A. 
Published: April 2021

Abstract
The effect of chisel plow on corn / Maha cultivar was observed based on some technical indicators, under three plowing depth (14, 16 and 18 cm). The experiments were conducted in a factorial experiment under randomized complete block design with three replications. The 11-13% soil humidity was meaningfully best than 13-15% soil humidity in all studied parameters. For 11-13% soil humidity and 14cm plowing depth, the fuel consumption, machine efficiency, germination percentage, plant height, 1000 – weight grain, number of grains per ear and the corn productivity were 12.336 and 11.913l.ha−1 79.425 and 79.972 %;89.527 and 92.346 %; 148.85 and 153.70 cm; 218.85 and 226.58 g; 474.308 and 499.354 grain; 4.163 and 4.286t.ha−1.
Effects of different broiler flooring systems on surface temperature, air quality and carcass characters of broilers
Authors: Al-Nasseri Ahmad N.I. , Taha Ahmed T. , Khalaf Hasan Aljumaily Tareq 
Published: April 2021

Abstract
The present study investigated the use of perforated plastic floors with different heights in the broiler performance, surface temperature, air quality, and carcass characters of broilers. Three hundred sixty-seven day-old unsexed chicks randomly divided into 4 groups: control reared on sawdust litter in others used, plastic floors with different heights (5, 10 and 15) cm, for 2nd, 3rd and 4th treatment respectively. The first treatment (sawdust) showed significantly (P < 0.05) increase in body surface temperature, as compared to the plastic floors system treatments, and the three plastic floor treatments affected air quality, with less concentrations of ammonia and carbon dioxide compared to the sawdust. The results showed significant differences (P < 0.05) in the live relative weight, where the treatments T2, T3 and T4 were increase at the age of 35 days compared to the sawdust treatment. We suggested the perforated plastic floors could be a good alternative to promote a better quality environment and superior production rates with improved surface temperature.
Effect of variety and humidity on the loss of maize crop after shelling in shelling and drying factories of maize (Zea mays) in Iraq
Authors: AL-Aaty Y H S , Al-Jomaily A R A 
Published: April 2021

Abstract
In order to know effect of the variety and the optimum humidity to reduce the percentage of loss in the yield of maize during the process of shelling the maize factories, this research was conducted during the autumn season of 2011 in Hawija district, west of Kirkuk governorate, using six entered varieties (CADIZ, DKC1120, DKC1188, DRACHMA, RONALDINIO and RANT) were cultivated according to a randomized complete block design (RCBD) with three replications. The shelling was carried out at three levels of humidity (23-26%, 27-30% and 31-33%). The following characteristics were studied (number of rows in ear, number of grains per row, total yield kg/du, outturn ratio of grain yield at the three moisture levels, and the percentage of loss of grain yield at the three moisture levels). The results indicated that there are significant differences between the varieties at a probability level of 1% for all the studied traits, and the results of the means test showed the superiority of the DRACHMA variety at the best rates for all the traits studied, as it gave a yield amounted to 3708.2 kg/du, and it also gave the lowest percentage, it was 3.95% Low humidity 23-26%. The low humidity percentage also achieved the lowest rates of loss percentage during the shelling process for all varieties.