bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development Carbonated Hydroxyapatite Powders from Oyster Shells (Crassostrea gigas) by Sintering Time Controlling
Authors: Almukarrama  , Yusuf Yusril 
Published: June 2019

Abstract
Carbonated-hydroxyapatite (CHAp) is a hydroxyapatite substituted with carbonate ions and is known to resemble the hard bone structure of humans. CHAp was synthesized from biogenic material. Oyster shells (Crassostrea gigas) was biogenic materials that used as a calcium source. In addition, sodium bicarbonate and ammonium hydrogen phosphate were also used as sources of carbonate and phosphate. In this research, the oyster shells used were milled, calcined, and characterized to determine the phase changes from CaCO3 to CaO by decomposition process. CHAp powders which were synthesized by precipitation method, were sintered at 650 °C for 0.25, 0.5, 1, 2 and 4 hours. To find the quality of CHAp powders with different sintering time, characterization process using Fourier Transform Infrared Spectroscopy (FTIR), X-Ray Diffractometer (XRD), and Scanning Electron Microscope-Energy Dispersive X-Ray (SEM-EDX) has been successfully done. FTIR, XRD, and SEM-EDX was used to determine functional groups, to confirm the presence of carbonate groups, to analyse the crystallography, and to calculate the Ca/P molar ratio and carbonate content of CHAp powders. Based on the results of characterization process showed that the oyster shells weres successfully synthesized into CHAp and the length of sintering time effects of the crystallite size and crystallinities. CHAp powders sintered for 4 h have 41.385 % and 31.499 nm degree of crystallinity and crystallite size, respectively.
Fiber Content and Relative Feed Value Estimation of Gamma Irradiated Rice Straw
Authors: Firsoni  , Wahyu Hardani Shintia Nugrahini , Wahyono Teguh 
Published: June 2019

Abstract
Ionizing irradiation is an alternative treatment to reduce the cell wall constituents and improve the nutritional value of substrates. The purpose of this research was to evaluate the fiber content and relative feed value of rice straw after gamma irradiation treatment. Rice straw was obtained from Sidenuk rice variety. Two polyethylene packages of samples were irradiated in Iradiator Karet Alam (IRKA), Indonesia at doses of 5 and 10 kGy in the presence of air. Treatments includes untreated/unirradiated, 5 kGy and 10 kGy irradiated rice straw. The observed parameters were neutral detergent fiber (NDF), acid detergent fiber (ADF), dry matter digestibility (DMD), dry matter intake (DMI) and relative feed value (RFV) estimation. The change of fiber content was also qualitatively observed by Fourier Transform Mid-Infrared (FTIR). This research was arranged into a completely randomized block design with five replications. The results showed that there was no difference in NDF and ADF content between all treatments. There was also no difference in RFV due to the results of NDF and ADF measurements. Interestengly, there was a strong absorption with a wide and strong band centred at 3331 cm−1 (O-H stretch) only observed in the untreated sample. Meanwhile, a peak signal was observed at 1722 cm−1 only in 5 kGy and 10 kGy irradiated rice straw spectra. This could affect in the increased of hemicellulose compounds after irradiated treatments. It can be concluded that gamma irradiation treatment at doses of 5 and 10 kGy has not been able to influence the fiber content and relative feed value of rice straw. However, there was a tendency for hemicellulose compounds increasingly due to irradiation treatment.
The Role of DC Biased Plasma Treatment of Polystyrene on the Formation of the C≡N Functional Group Controlling Its Hydrophobicity
Authors: Hanif Mahardika A. , Masruroh  , Santjojo Dionysius J. D. H. 
Published: June 2019

Abstract
Nitrogen plasma treatment was carried out to modify the surface of polystyrene film deposited on a quartz crystal microbalance (QCM) sensor. The modification was intended to enhance the wettability of the film by inducing the formation of CN functional group on its surface. This work investigated the effect of DC bias controlling N* in the plasma on the effectiveness of the nitrogen plasma treatment. The polystyrene was deposited by a spin coating method on the surface of a quartz crystal normally used in an electronic resonator. The nitrogen plasma was generated in an aluminium vacuum chamber using a 2 MHz radio frequency (RF) generator. The vacuum pressure and the gas flow rate were kept constant during the experiments. The plasma character in the chamber was controlled by applying a DC bias between the RF electrodes and the sample stage. The DC voltage was varied from 100 to 500 volts at 100 volts’ step, while maintaining the RF voltage constant. During the process, the species in the plasma was characterized using an optical emission spectroscopy (OES). The wettability of the treated samples was tested using a contact angle measurement system. FTIR measurement was utilized to study the polar functional groups on the surface of the polystyrene before and after the treatment. The OES identified the existence of N radical (N*). The reduced intensity of emission line of the N* increased with the increase of the DC voltage. However, a small decrease was also evidence at DC voltage higher than 400 volts. The decrease of the line intensity was directly related to the decrease of the contact angle (CA < 40°). The relation of the N* and the resulted wettability is related to the polar functional group of the C≡N bond found in the treated polystyrene’s surface.
Synthesis temperature and NaOH concentration dependences of microstructure and magnetic properties of MnNi-Fe2O4 nanoparticles
Authors: Saputra A I , Herdiman  , Suharyadi E , Kato T , Iwata S 
Published: June 2019

Abstract
MnNi-Fe2O4 nanoparticles have been successfully synthesized by coprecipitation method in different synthesis temperatures and NaOH concentrations. Microstructure and magnetic properties of nanoparticles were characterized by X-ray diffractometer (XRD), transmission electron microscope (TEM), fourier transform infra-red (FTIR) spectroscope, and vibrating sample magnetometer (VSM). X-ray diffraction patterns confirmed the formation of spinel structure with crystallite size in the range of 7.5 nm to 14.0 nm. The spectral analysis revealed two characteristic absorption bands related to metal-oxygen vibration in tetrahedral and octahedral site that appeared at 586 cm−1 and 432 cm−1, respectively. The magnetization saturation within the range of 3.0-8.1 emu g−1. The tendency to increase in coercivity from 6.5 to 17.5 Oe was due to the increase in synthesis temperature. The increase in NaOH concentration also affected coercivity to increase from 12.3 to 84.9 Oe.
Experimental Analysis on The Tensile Strength of Polyester Resin based Calcium Carbonate Powder and E-Glass Fibre Reinforced Composite
Authors: Wicaksono S T , Ardhyananta Hosta , Waluyo M B 
Published: June 2019

AbstractCalcium carbonate powder is contained in the waste produced from processing limestone. Limestone is used as an alternative substitute for organic fillers in polymer composites. The use of this waste can be a valuable resource as a replacement filler in polymer composites. The main purpose of this study is to reuse the waste materials calcium carbonate for composite materials. E-glass type woven roving is used as reinforcement and polyester resin was is as a matrix. In the present research, the addition of calcium carbonate waste powder in composites E-glass/polyester was investigated for tensile strength and density. In the hybrid, composite specimen of this study was prepared using hand lay-up technique. Tensile testing was carried out according to ASTM D368 standard. At first, calcium carbonate powder, glass fibre, and polyester were prepared in a specific volume and the specimens were prepared. Comparison filler plays an important role in producing the tensile strength of the composite. Thus, the fill ratio was considered as an experimental variable and were varied (2.5, 5.0, 7.5, and 10% by weight for calcium carbonate; 10, 12.5, 15, and 17.5% by weight for E-Glass. After testing was carried out, it is found that the strength of the material was increased as the volume of E-glass fibre increased.
The rule of radius averaging in hydrogen atom
Authors: Abdurrouf  
Published: June 2019

Abstract
According to quantum mechanics, electrons do not have a fixed position in an atom, and therefore orbitals have no definite radii. However, electrons have characteristic wave functions from which the radius of their orbits can be calculated or averaged. Depending on the average method, there are three most popular expressions for orbital radius, namely the average radius, the root mean square (rms) radius, and the most probable radius. Unfortunately, for hydrogen atom, none of those three radii is equal to the classical radius, even for large principal quantum numbers called the classical regime. Here, by using energy analysis, we propose a harmonic radius and show that the results well agree with the classical radius for each principal and orbital quantum numbers.
Forecasting Foreign Tourist Using Intervention Analysis On Count Time Series
Authors: Atmanegara Eviyana , Suhartono  , Atok RM 
Published: June 2019

Abstract
The foreign tourist’s data are count time series data that contains the discrete value. Poisson autoregressive (AR) and Negative Binomial AR are time series models used for forecasting count data. The number of foreign tourist arrivals is influenced by the series of inputs called interventions, such as the existence of bomb terror and tourism promotion. This research aims to forecast the number of foreign tourists visiting Indonesia by nationality, 2019. The number of tourist arrivals from Bahrain and Singapore represents low count data and high count data, respectively. This work employs intervention analysis on count time series model and intervention analysis on ARIMA. Intervention on Poisson AR is the best model for forecasting the number of tourist arrivals from Bahrain and Singapore to Indonesia, 2019.
Convert Probability Network to Artificial Neural Network based on Position, Time and Speed of Events
Authors: Cholissodin Imam , Marji  
Published: June 2019

Abstract
Building a probability function to get the best answer between events in many review problems is a very complex one. In this study, several events were given as a case study to find a solution, after which an analysis will be carried out when and where the event contained position and time features. So that the distance factor, travel time factor and speed factor for each event can be calculated. Then a concept comparison between Probability Network from the Bayes Theorem versus Artificial Neural Network (ANN) was made, accompanied by a review of the Propositional Logic (PL) or First Order Logic (FOL) concept compared to using the Uncertainty concept in calculating the probability of an eventin a pseudo event that has often been ignored because it is considered to have no influence and the probability is small whereas it can be the opposite. In fact, sometimes, the pseudo event is the main factor determining the real event and the probability is much greater than expected. In addition, we also give an Engineering Events Theorem that can intentionally set event-by-event syntheses to increase the probability of an event which is initially smaller than the probability of another event. After being given an injection/dummy/synthesis event, however, then the initially small probability of the event becomes very high and the time of occurrence can be accelerated or decelerated, or even minimized so that it does not occur.
Geographically Weighted Bivariate Gamma Regression in The Analysis of Maternal Mortality Rate and Infant Mortality Rate in North Sumatra Province 2017
Authors: Dewi Diah Kusuma , Purhadi  , Sutikno  
Published: June 2019

Abstract
In this study, Geographically Weighted Bivariate Gamma Regression (GWBGR) model is proposed. This GWBGR model is a developer of the Bivariate Gamma Regression (BGR) model which all of the regression parameters depend on the geographical location, i.e latitude, and longitude. In these models, the response variables are correlated and follow the gamma distribution. We applied the GWBGR model to analyze Maternal Mortality Rate (MMR) and Infant Mortality Rate (IMR) in North Sumatra Province 2017. The result shows that the test of heterogeneity spatial is significant, it means MMR and IMR in North Sumatra Province depend on the geographical location. Modelling with BGR produced 6 groups based on significant variable similarities to MMR and 3 groups based on significant similarity of variables towards IMR. Based on AICc, GWBGR model is smallest than BGR model. Finally, we conclude that the GWBGR model was better than the BGR Model (global model).
Generalized Linier Autoregressive Moving Average (GLARMA) Negative Binomial Regression Models with Metropolis Hasting Algorithm
Authors: Febritasari Popy , Surya Wardhani Ni Wayan , Sa’adah Ummu 
Published: June 2019

AbstractThis paper discusses regression models when the variance in count data is not equal to the mean. It happens in mortality cause of traffic accident data in jurisdiction’s territory of Dharmasraya’s Police Resort, where the variance is larger than the mean, which is called overdispersion. In this case we used negative binomial regression in time series with generalized linier autoregressive moving average (GLARMA) models. The parameters were estimated using maximum likelihood estimation (MLE) method and metropolis hasting algorithm at 100thburn - in period and 150000 iteration. The prior distribution and the number of iteration in metropolis hasting algorithm had less Mean Square Error (MSE) than MLE method. Prediction for next period using model metropolis hasting algorithm.
Modeling of Exclusive Breastfeeding and Mother Working Status with Recursive Bivariate Probit Model (Case Study in Surabaya City 2017)
Authors: Isnaini Fadhila , Ratnasari Vita , Mashuri Muhammad 
Published: June 2019

Abstract
There are special cases in Bivariate Probit model where one dependent variable becomes the regressor endogenous variable for the other dependent variable. Therefore, recursive bivariate model is used to solve this case. Exclusive breastfeeding is measured to identify its characteristics so that policies can be formulated to increase the rate of exclusive breastfeeding. The variable of mother working status is endogenous. Therefore, a recursive model is used to model this case. Mother’s working status affects mother’s exclusive breastfeeding. Mothers who do not work increase the chances of a mother being able to give exclusive breastfeeding. The greater the age of the first pregnancy of the mother, the less the chances of mothers to not work. A mother with more child has more chance to not working. A medium number of family member affect a chance for a mother to working. A mother who has last education at the Elementary School has a high chance of exclusively breastfeeding their babies. This may be related to the ability of mothers to work with mother’s education. Early Initiation of Breastfeeding (EIB) affects the opportunity for mothers to exclusively breastfeed. It proves the importance of EIB shortly after the baby is born so that the baby does not confuse the nipple and the breastfeeding process becomes easy. A husband who has last education at Senior High School give a greater chance for the mother to exclusively breastfeed. Recursive Bivariate Probit models have smaller AIC values than Bivariate Probits, which are 522.226 and 526.414. This shows that the Recursive Bivariate Probit model is better at modeling cases of exclusive breastfeeding and the status of working mothers in Surabaya city than Bivariate Probit Model.
Possibilistics C-Means (PCM) Algorithm for the Hepatocellular Carcinoma (HCC) Classification
Authors: Khairi Rafiqatul , Rustam Zuherman , Utama Suarsih 
Published: June 2019

Abstract
Hepatocellular Carcinoma (HCC) is a malignant tumor that attacks the liver and can cause death. Although there have been advances in technology for the prevention, diagnosis, and treatment, the number of liver cancer patients is still increasing. The liver can still function normally even if some of its parts are not in good condition. Therefore, the symptoms of liver cancer at an early stage are difficult to detect. Early diagnosis of this disease will increase the chances of recovery. One method to diagnose Hepatocellular Carcinoma (HCC) is to check the level of alpha-fetoprotein (AFP) in the blood which is alpha-fetoprotein (AFP) is a cancer index. If the liver cancer cells continue to grow, the level of alpha-fetoprotein (AFP) will be very high. This paper presents a Possibilistic C-Means (PCM) algorithm, which used to classify the results of alpha-fetoprotein (AFP) blood tests to determine whether patients diagnosed with Hepatocellular Carcinoma (HCC) or normal patients. This method will help to get an accuracy of about 92%.
Summarizing Netizens’ Sentiments Towards the 1stIndonesian Presidential Debate using Lexicon Sentiment Analysis
Authors: Lestari Ariesta , Karolita Devi 
Published: June 2019

AbstractTwitter is one of the popular social media platforms in Indonesia. This platform has been used as a media communication and public engagement tool for many purposes, especially in political and governance domains. During the process of 2019 Indonesian Presidential Election, many people use Twitter to express their opinion/sentiment towards the election process. In this paper, we investigate the nature of people’s opinion towards the Indonesian Presidential Election after the 1st debate. The goal of this study is to perform exploratory sentiment based analysis of Twitter data, and that was gathered after the 1st debate. We used lexicon sentiment analysis to calculate the sentiment of political tweets collected after the 1st debate. The identification of positive and negative opinion was automatically conducted using the available dictionary. Our result shows that sentiment of the netizen towards the 1st Presidential debate was mostly negative. In addition to this result, a predictive model was generated using CART and logistic regression to predict the netizens’ sentiment. This experiment shows that the accuracy of the prediction model reaches 90%. Therefore, our study suggests that Twitter data can be used to analyse citizens’ sentiment toward the Indonesian Presidential Debate and can generate a model to predict citizens’ future sentiment toward the next debate.
Survival Analysis of Un-identical Recurrence Using Conditional I, Conditional II, and Marginal Method
Authors: Kusthika Putri Dita Ramadayanti , Sa’adah Umu , Efendi Achmad 
Published: June 2019

Abstract
Survival analysis can be used to examine events that may occur more than once in one individual or is called a recurring event. This event can be seen in cases of stroke recurrence. The occurred recurrence shows a more severe recurrence stage than before (recurring events are not identical). The Stratified Cox model with Conditional I, Conditional II, and Marginal methods can be used to analyse recurring events, especially in recurrent events that are not identical. The used data are secondary data from the medical record archives of stroke patients at Chasbullah Abdulmadjid Hospital Bekasi City from March 1, 2016 to April 30, 2016. Based on the Wald test and the backward elimination steps, we found two explanatory variables that influence the recurrence rate of stroke which were hypertension and hypercholesterolemia. The model from the three methods is a model with a combination of variables of hypertension and hypercholesterolemia. Patients suffering from hypertension or hypercholesterolemia have the potential to experience a recurrence of stroke compared to patients who do not have hypertension or hypercholesterolemia. Based on the AIC value of each method, the best method for recurring data on stroke patients is the Conditional I method.
Learning Vector Quantization for Diabetes Data Classification with Chi-Square Feature Selection
Authors: Putri Nadisa Karina , Rustam Zuherman , Sarwinda Devvi 
Published: June 2019

Abstract
Diabetes mellitus or commonly referred as diabetes is a metabolic disorder caused by high blood sugar level and the pancreas does not produce insulin effectively. Diabetes can lead to relentless disease such as blindness, kidney failure, and heart attacks. Early detection is needed in order for the patients to prevent the disease being more severe. According to the non-normality and huge dataset in medical data, some researchers use classification methods to predict symptoms or diagnose patients. In this study, Learning Vector Quantization (LVQ) is used to classify the diabetes dataset with Chi-Square for feature selection. The result of the experiment shows that the best accuracy is achieved at 80% and 90% of the data training and the performance measurement, which are precision, recall, and f1 score are the highest when the model contains all the features in the dataset.
Trivariate gamma regression
Authors: Rahayu Anita , Purhadi  , Sutikno  , Prastyo Dedy Dwi 
Published: June 2019

Abstract
Regression analysis is a method for determining a causal relationship between the response and predictor variables. The regression model has been developed in various ways, one of them is based on the distribution of the response variables. In this study, the response variables follow trivariate gamma distribution, such that the regression model developed is Trivariate Gamma Regression (TGR). The purposes of this study are to obtain the parameter estimators, test statistics, and hypothesis testing on parameters are significance (overall and partial) of the TGR model. The parameter estimators are obtained using the Maximum Likelihood Estimation (MLE). The overall test for the model’s significance is using Maximum Likelihood Ratio Test (MLRT), and the partial test is using the Z test. Based on the results of this study, it can be inferred that the parameter estimators obtained from the MLE are not closed form. Hence a numerical method is needed. In this study, the algorithm of numerical optimization used is BFGS quasi-Newton.
Classification of Breast Cancer using Fast Fuzzy Clustering based on Kernel
Authors: Rustam Zuherman , Hartini Sri 
Published: June 2019

Abstract
Breast cancer is the second leading cause of death in women in the world. The classification is the initial process of executing patient treatment, which is important as it increases life expectancy as well as quality. In this paper, a new method is proposed based on kernel, which is modified from KC-Means: it combines K-Means, Fuzzy C-Means algorithm, and kernel function. The C-Means algorithm is applied on the centers of a fixed number of groups founded by K-Means, and the kernel function is expected to improve the accuracy of classification with its ability to separate data which cannot be separated linearly. We applied the proposed method on a dataset of 201 breast cancer and 85 non-breast cancer samples from the UC Irvine Machine Learning Repository. Results concluded that fast fuzzy clustering has an accuracy of 85.26%, but fast fuzzy clustering based on kernel is 89.74%, with a better running time on average than 90.95% with the same method.