bluuebunny/crossref_metadata_2025_split huggingface dataset:
Copper Nanoparticle Catalysed Aerobic Oxidation of α-pinene
Authors: Masruri  , Pamungkas K K 
Published: June 2019

Abstract
A green aerobic-oxidation of alpha-pinene using air and catalyzed by copper nanoparticle (CuNP) was studied. The study involved preparation and characterization of copper nanoparticle with three different calcination temperatures (300, 500 and 800°C). Nanoparticle characterization was conducted using FTIR spectrophotometry and SEM-EDX analysis. Evaluation of the catalytic activity was done using direct air-bubbled to the alpha-pinene with and without catalyst. It was found that oxidized alpha-pinene was formed as the major product.
Comparison of nutritive value between intact and defatted black soldier fly larvae for animal feed
Authors: Mulianda Randi , Ridla Muhammad , Laconi E B , Ridwan Roni , Jayanegara Anuraga 
Published: June 2019

Abstract
In this study, the possibility black soldier fly (BSF) larvae as a novel feedstuff for animal was investigated. The objective of this study was to compare the nutritive value between intact and defatted BSF larvae for animal feed by means of chemical composition determination and in vitro rumen fermentation technique. The following treatments were tested: intact BSF (T1), BSF low in lipid by mechanical extraction (T2), BSF low in lipid by chemical extraction (T3), and soybean meal (T4). The samples were subjected to proximate analysis, Van Soest’s fiber fraction and cell wall nitrogen determination. The samples were further incubated in vitro with buffered-rumen fluid in four replicates, represented by two incubation units per replicate, and conducted for 48 h at 39°C. Parameters measured in the in vitro evaluation were total gas production and methane emission at regular time point intervals. Other parameters measured after the incubation were dry matter degradation, organic matter degradation, total volatile fatty acid (VFA), ammonia concentration and pH. Data were tested using analysis of variance (ANOVA) and if there was a significant different at P<0.05 then continued with Duncan’s multiple range test. Results revealed that removal of lipid from BSF larvae either by mechanical or chemical extraction increased dry matter degradability (DMD), organic matter degradability (OMD) and total gas production values as compared to intact BSF (P<0.05). However, these values were still lower than those of soybean meal (P<0.05). Concentrations of ammonia and total VFA were similar across all dietary treatments. It can be concluded that removal of lipid from BSF larvae by either mechanical or chemical extraction improves its utilization in the in vitro rumen fermentation system.
The Chemical Properties of Seaweed Caulerpa lentifera from Takalar, South Sulawesi
Authors: Sinurat Ellya , Fadjriah Sofa 
Published: June 2019

Abstract
Indonesia is known as a maritime country having many types of seaweed. The type of seaweed that is still little studied is the type of green seaweed. One of the most common types of green seaweed found in Indonesia is Caulerpa lentifera. Green seaweed is informed to contain beta-glucan. Beta glucan has widely studied because it contains bioactive compounds. The bioactive content of green seaweed, in this case, is obtained from Caulerpa type. In this study, the type of Caulerpa studied derived from cultivation (Takalar, South Sulawesi). Test of chemical properties of seaweeds parameters observed were protein quality, fat content, carbohydrate, water content, and dietary fibre. This study is a preliminary treatment to determine the primary metabolite content of Caulerpa seaweed cultivation. The methods used to include rinsing, washing with water, drying and grinding. The result of analysis for moisture content (5.4%), protein (14.4%), fat (0.85%), total ash (41.85%) and carbohydrate (32.95%). Protein and carbohydrate were the most sources in this species. This study suggests that C. lentifera could potentially be used as a nutritious and functional food item for the human diet.
Carbonated Hydroxyapatite Derived from Cerastoderma edule, Paphia undulata, and Meretrix meretrix Shells
Authors: Wati Rosita , Yusuf Yusril 
Published: June 2019

Abstract
Carbonated hydroxyapatite (CHAp) derived from three different shells, Cerastoderma edule, Paphia undulata, and Meretrix meretrix shells, have been synthesized by precipitation method. Calcium oxide from extracting shells, diammonium hydrogen phosphate, and ammonium bicarbonate was used to synthesis as the starting precursors. The CHAp materials were analyzed by Energy Dispersive Spectroscopy (EDS), Fourier Transform Infrared (FT-IR), and X-Ray Diffractometer (XRD). EDS analysis showed that the Ca/P ratio of CHAp powders were around 1.75-1.77 (bigger than the Ca/P ratio of HAp) and carbonate content of the powder CHAp was 3.10-4.05wt%. XRD data has produced diffraction patterns from the three different shells similar to the apatite structure. The position of the main peak (2θ) diffraction patterns detected at 31.99°, 32.23°, and 31.95°. The lattice parameter of a-axis and c-axis were obtained around 9.37 Å and 6.89 Å. Crystallite size of CHAp samples was 30-40 nm. The FTIR spectra showed that there are functional groups of OH-, PO4
3-, and CO3
2- at wave number around 3447, 571, and 873 cm−1. Based on the results of the analysis, it shows that the CHAp characteristics that had successfully produced from Cerastoderma edule, Paphia undulata, and Meretrix meretrix shells were B-type CHAp.
Samarium Complexes from 2,6-Naphtalenedicarboxylate: Synthesis, Photocatalytic Properties and Degradation of Methylene Blue
Authors: Wulandari Dwi Aprillia , Zulys Agustino , Kusrini Eny 
Published: June 2019

Abstract
Coordination polymer have been attracting considerable attention and present a great potensial in photocatalytic water treatment, paving the way for buliding efficient and enviromentally friendly photocatalysts. In this work, samarium complexes constructed from 2,6-naphtalenedicarboxylate ligand was successfully synthesized using solvothermal methode. Samarium complexes are characterized by Powder X-ray Diffraction (PXRD), Fourier Transform Infrared Spectroscopy (FTIR), Uv-vis Diffuse Reflectance and Cyclic Voltametric. FTIR characterization results indicate peak absence of absorption band arround 1700 cm−1 further confirms that the carboxylate groups in dicarboxylate ligand are completely deprotonated. The value of the band gap obtained from the corresponding transition is 3,1 eV. The LUMO Level reduction potential O2/O2
-* is obtained -2.1 volt, this value is more negative than the potential standard O2/O2
-* reduction is -0,33 volt, so the ability to form O2
-* is greater. Based on the characteristics obtained, this material potential as a semiconductor for photocatalysts methylene blue degradation, where samarium complexes in 240 minutes can degradation methylene blue by 68,6 percent.
Hybrid radial basis function with firefly algorithm and simulated annealing for detection of high cholesterol through iris images
Authors: Anjarsari A , Damayanti A , Pratiwi A B , Winarko E 
Published: June 2019

Abstract
Cholesterol is a lipid (fat) produced by the liver and is required to build and maintain cell membranes. Cholesterol is also important for the metabolism of fat soluble vitamins. This important lipid is found in human blood. Excess cholesterol (high cholesterol) can cause health problems such as being a factor of coronary heart disease that responsible for the heart attacks, liver or kidney disease. Observation of iris pattern can detect several types of diseases, one of which is high cholesterol. The purpose of this research is to detect whether someone is exposed to high cholesterol or not, through iris images based on firefly algorithm, simulated annealing, and radial basis function. Firefly algorithm and simulated annealing are used in the unsupervised learning process in radial basis function neural networks. The stages of high cholesterol detection process are images processing namely grayscale process, thresholding, histogram equalization, segmentation, and detection process is using radial basis function neural network. The percentage success rate of the recognition pattern of iris images for detecting high cholesterol is 89%.
Classification method at acceptance of new student at public university on the national written test
Authors: Antari Ika S W , Zain Ismaini , Suhartono  
Published: June 2019

Abstract
Acceptance of new students at public universities through the national written test is based on the total score and the capacity of the study program. This causes the study program accepts several students who have low scores on the main subject of the study program. The purpose of this study is to find the best method in predicting the probability of being accepted on the national written test and find the minimum score for each subject that must be achieved by participants to be accepted at a public university. There are two classification methods in statistics that are studied to overcome this problem, i.e. logistic regression and random forest. The results showed that the best logistic regression model had an accuracy of 97.11 percent, whereas the random forest method had an accuracy of 96.59 percent. Furthermore, the minimum score for each subject was developed based on the univariate logistic regression model.
Glaucoma Identification on Fundus Retinal Images Using Statistical Modelling Approach
Authors: Anwar A. E. , Chamidah N. 
Published: June 2019

Abstract
Glaucoma is an eye disease characterized by progressive deterioration of the optic nerve head and a broad view that can cause blindness. The Population Based Survey in 2010 indicates that glaucoma was the second leading cause of blindness after cataracts, which was about 8% of 36 million sufferers of blindness worldwide. Symptoms of glaucoma that arise usually cannot be felt directly. So it is necessary to do an eye examination to find out glaucoma, one of which is to look at the size of the optic disk in the digital fundus photo. The previous studies about glaucoma identification were done by using mathematical computation approach that have still not satisfied. Therefore, in this study we propose a new method, i.e., statistical modelling approach to identify glaucoma. In statistical modelling, there are two approaches, i.e., parametrical approach, and non-parametrical approach based on penalized spline estimator. The result of classification accuracy by using parametrical and non-parametrical approaches are 73.3% and 93.33%, respectively. Based on the result, we conclude that non-parametrical approach has better outcome so that it can be used to identify glaucoma on fundus retinal image.
Parameter Estimation of Locally Compensated Ridge-Geographically Weighted Regression Model
Authors: Fadliana Alfi , Pramoedyo Henny , Fitriani Rahma 
Published: June 2019

Abstract
Geographically weighted regression (GWR) is a spatial data analysis method where spatially varying relationships are explored between explanatory variables and a response variable. One unresolved problem with spatially varying coefficient regression models is local collinearity in weighted explanatory variables. The consequence of local collinearity is: estimation of GWR coefficients is possible but their standard errors tend to be large. As a result, the population values of the coefficients cannot be estimated with great precision or accuracy. In this paper, we propose a recently developed method to remediate the collinearity effects in GWR models using the Locally Compensated Ridge Geographically Weighted Regression (LCR-GWR). Our focus in this study was on reviewing the estimation parameters of LCR-GWR model. And also discussed an appropriate statistic for testing significance of parameters in the model. The result showed that Parameter estimation of LCR-GWR model using weighted least square method is

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Statistical downscaling to predict drought events using high resolution satelite based geopotential data
Authors: Kuswanto H , Yuliatin I L , Khoiri H A 
Published: June 2019

Abstract
Drought prediction is a very challenging work due to high degree of uncertainty in the climate system. Geopotential height has been investigated as one of the dominant variables that can be used to predict drought events. This paper discussed the use of high resolution satelite based (reanalysis) data as the predictor of drought events, resulting on a high dimensional dataset. To deal with this, dimension reduction has been carried out by using Principle Component Analysis (PCA), prior to the development of the downscaling models which incorporate the past SPI (Standardized Precipitation Index) combined with the geopotential height at some specific atmosperic levels i.e. 500hPa, 850hPa, 900hPa, 975 hPa and 1000hPa. The SPI, as the drought risk measure is derived from the reduced dimension of precipitation data observed from the corresponding meteorological stations, while the geopotential height is reduced from gridded high resolution data. The downscaling process found the best model to predict the drought risk with various degree of R-squares. The outsample validation showed that predicting drought using SPI3 (three month period SPI) with geopotential at the 900hPa level as the predictor outperforms the others with R-square reaching 77%.
Optimal Control of an HIV Model with Changing Behavior through an Education Campaign, Screening and Treatment
Authors: Marsudi  , Trisilowati  , Suryanto Agus , Darti Isnani 
Published: June 2019

Abstract
Optimal control theory was used on the system of differential equations to achieve the goal of minimizing the infected population and slow down the epidemic outbreak. Necessary conditions of optimal control problem were rigorously analysed using Pontryagin’s maximum principle. Three control strategies were incorporated such as human education campaign, screening and treatment of infected human and its impact were graphically observed. Runge-Kutta forward-backward sweep numerical approximation method is used to solve the optimal control system. Numerical results with education campaign levels, screening and treatment rates as controls are illustrated.
Parameter Estimation and Statistical Test in Multivariate Adaptive Generalized Poisson Regression Splines
Authors: Hidayati Sri , Otok Bambang Widjanarko , Purhadi  
Published: June 2019

Abstract
Poisson regression is a standard model for data counts that can be used to determine these factors. Equidispersion is assumptions that must be met in poisson regression. Equidispersion is a condition that the average of response variable is equal with the variance of the response variable. In real cases, there are overdispersion or underdispersion cases. Generalized Poisson Regression (GPR) is one of method that can handle cases of overdispersion or underdispersion. Multivariate Adaptive Regression Splines (MARS) is a nonparametric regression that can handle data whose behavior changes in sub-intervals, so that there is a knot point that indicates the occurence of changes in data behavior patterns. Multivariate Adaptive Generalized Poisson Regression Splines (MAGPRS) model is used as the development of the MARS and Generalized Poisson Regression. This research use Weighted Least Squares (WLS) with Berndt Hall Hall Husman (BHHH) algorithm to obtain parameter model estimator. Afterwards, get the test statistic on the model Multivariate Adaptive Generalized Poisson Regression Splines using Maximum Likelihood Ratio Test (MLRT). Finally, the application of MAGPRS model was carried out in the case of the number of Acute Respiratory Tract Infection in babies.
Dynamical Analysis of Infected Predator-Prey Model with Saturated Incidence Rate
Authors: Pusparani Antika , Kusumawinahyu Wuryansari Muharini , Trisilowati  
Published: June 2019

Abstract
A predator-prey model with disease in both populations is proposed to illustrate the possibility of disease transmission between prey and predator through contact and predation. We used saturated incidence rate which takes behavioural changes of healthy population into consideration when disease spreads around them. The existence of eight non-negative equilibrium points is analysed and their local stability has been investigated. Numerical simulations are given to illustrate analytic results.
The Estimation Function Approach Smoothing Spline Regression Analysis for Longitudinal Data
Authors: Risnawati  , Rinaldo Fernandes Adji Achmad , Nurjannah  
Published: June 2019

Abstract
It is given the paired data following a nonparametric regression model for longitudinal data. The regression curve is approached by the smoothing spline function. The smoothing spline is a function that is able to map the data well and has a small error variance. This current study aims to obtained from completing PWLS (Penalized Weighted Least Square) optimaziton. Besides, the GCV method is used to select the smoothing parameter.
Random-Forest (RF) and Support Vector Machine (SVM) Implementation for Analysis of Gene Expression Data in Chronic Kidney Disease (CKD)
Authors: Rustam Zuherman , Sudarsono Ely , Sarwinda Devvi 
Published: June 2019

Abstract
The application of mathematics in the field of bioinformatics has been widely developed. For example Support Vector Machines (SVM) and Random Forest (RF) are state of the art for classification of cancer in many applications. One of them is Chronic Kidney Disease (CKD). CKD is one of the kidney diseases that sufferers are increasing and have symptoms that are difficult to detect at first. Later, microarrays in gene expression are important tools for this approach. Microarrays gene expression provides an overview of all transcription activities in biological samples. The purpose of this research is a hybrid model combining Random Forest (RF) and Support Vector Machine (SVM) can be used to classify gene expression data. RF can highly accurate, generelize better and are interpretable and SVM (called RF-SVM) to effectively predict gene expression data with very high dimensions. In addition, from the simulation results on data from the Gene Expression Omnibus (GEO) database, it is shown that the proposed RF-SVM is a more accurate algorithm on CKD data than RFE-SVM.
Modeling Inflation and Money Supply using Spatial Vector Autoregressive Model with Calendar Variation: Restricted vs Non-restricted Coefficient
Authors: Sumarminingsih Eni , Setiawan  , Suharsono Agus , Ruchjana Budi Nurani 
Published: June 2019

Abstract
One of the tasks of the government is to maintain price stability reflected in the stability of inflation through the regulation of the money supply. Therefore the government needs to know the forecast of the inflation rate and the money supply. In modeling inflation and the money supply simultaneously in Indonesia, three things need to be accommodated, namely the relationship between variables, the existence of space-time relationships and the effect of Eid al-Fitr. The spatial vector autoregressive model with calendars variation can accommodate these three things. The purpose of this study is to compare two types of spatial vector autoregressive models with calendar variations, namely restricted and non-restricted coefficient to model inflation and the money supply in Surabaya, Malang, Kediri, and Jember simultaneously. Results of this study indicate that the non-restricted spatial vector autoregressive model with calendar variation is better than the restricted one. This can be seen from the value of MSE of the non-restricted model that is smaller than the restricted model.
Spatio-Temporal Fay-Herriot Models in Small Area Estimation to Obtain Factors That Affecting Poverty in Polewali Mandar District
Authors: Suwarti  , Rumiati Agnes Tuti , Kuswanto Heri 
Published: June 2019

Abstract
Poverty is a classic problem in almost all regions in Indonesia both at the provincial and district levels. Polewali Mandar district is a district with the highest poverty rate in West Sulawesi Province. Handling the problem of poverty requires the availability of data to the smallest level so that the policies taken by the government can be right on target. One method for obtaining poverty estimates reaches the smallest area using the Small Area Estimation (SAE) method. Spatio-Temporal Fay-Herriot models is one of the SAE methods that has considered spatial and time effects. Auxiliary variables are needed in the SAE method to get good estimation results. Availability of auxiliary variables that affect poverty in Polewali Mandar district is very necessary. Therefore the purpose of this study was to obtain auxiliary variables that influence poverty in Polewali Mandar district. Bootstrap parametric procedures are used to obtain factors that influence poverty in Polewali Mandar district, while for spatial weighting matrices use customized contiguity based on the main business fields in the village. The response variable is the percentage of poverty in each village affected by the Susenas sample. The results obtained were the variables that affected poverty in Polewali Mandar district were the percentage of farm worker’s families, the percentage of the population receiving Jamkesmas / Jamkesda, ratio of population to number of micro and small industries, average number of household members, ratio of population to number of health facilities and percentage of families living on river banks.
Recursive Particle Swarm Optimization (RPSO) schemed Support Vector Machine (SVM) Implementation for Microarray Data Analysis on Chronic Kidney Disease (CKD)
Authors: Rustam Zuherman , Syarifah Mas Andam , Siswantining Titin 
Published: June 2019

Abstract
Chronic Kidney Disease is the second chronical and catastrophic disease after heart disease in terms of treatment cost. This is because CKD symptoms occurs on final stages, that is fourth and fifth, in which it is too late for treatment. Therefore, final stage patient must receive continuous medication, such as haemodialysis. So early detection on a patient CKD is necessary to prevent patient to be chronic. Studies of gene genes are used to classify microarray data with global CKD decisions or not. So to get accurate results in this study using SVM-RFE with the addition of the Particle Swarm Optimization algorithm as a gene selector to be more optimal and it consideration of the fixed gene in its condition which is important information of the CKD gene itself. This research is then expected to be able to classify globally with CKD output or not CKD. As a result, for the CKD microarray data accuracy using RPSO schemed SVM highest than only using SVM-RFE.
Modeling of HIV and AIDS in Indonesia Using Bivariate Negative Binomial Regression
Authors: Tohari Amin , Chamidah Nur , Fatmawati  
Published: June 2019

Abstract
The problem of HIV and AIDS in Indonesia is a frightening health problem with a number of cases that tend to increase each year. The aim of this research is to model the number of HIV and AIDS cases in Indonesia using bivariate negative binomial regression approach. Bivariate negative binomial regression is a regression method for modeling a pair of response variables in the form of count data with negative binomial distribution and correlating to each other. This research uses secondary data from the ministry of health in 2017 about the number of HIV and AIDS cases in Indonesia. From the results of this research, we obtained the deviance value of 38.9197 which was used to describe the goodness of fit test.