bluuebunny/crossref_metadata_2025_split huggingface dataset:
Electrodeposition of Platinum (Pt) Particles as a Catalyst of Hydrogenation of Acetone
Authors: Rahayu Yusnita , Budi Setia , Fauzia Vivi 
Published: June 2019

AbstractHeterogeneous catalysts play an important role in accelerating chemical reactions in order to save energy and cost during the reaction process. Platinum (Pt) has received a great deal of attention for its application in many catalytic processes, but very few studies have evaluated it in the hydrogenation process of acetone. In this study, Pt particles were successfully grown on an indium tin oxide (ITO) coated glass substrate by means of a facile, one-step, and template-free electrodeposition method using a solution containing 2.0 mM H2PtCl6and 0.5 M H2SO4at room temperature. Potentiostatic and galvanostatic methods were employed to conduct the electrodeposition process under different potentials, current densities, and deposition times to observe their influence on the morphology of the electrodeposited Pt particles. It was found that the morphology of the particles could be facilely manipulated by adjusting the deposition current and potential by using the galvanostatic and potentiostatic methods, respectively. The potentiostatic method produced a spherical or bayberry-like shape whereas the galvanostatic method produced a flower-like shape. The sharp tips on the surface of the bayberry-like shape are electron-rich sites that can break the C = O bond in acetone, thereby forming C-OH bonds that can generate isopropanol molecules.
Synthesis of Organonitrogen Compounds from Eugenol through The Ritter Reaction and The Toxicity Test on Artemia salina Leach.
Authors: Rahman Mohammad Farid , Pratama Ardhya Ayu 
Published: June 2019

Abstract
Eugenol or 4-allyl-2-methoxyphenol is the main component in clove oil which can be used as a starting material in organic synthesis. One of the example is synthesis of organonitrogen compound through the Ritter reaction. Eugenol as a carbocation precursor is reacted with acetonitrile, in the presence of a strong acid catalyst H2SO4, to form organonitrogen compounds. The number of moles in the reaction of eugenol:acetonitrile:H2SO4 is 0.1:0.5:0.1 moles. The reaction was carried out at several conditions namely at ice temperature (0°C) for 4-hours reaction time, at room temperature (27°C) for 4 hours reaction time, and at reflux temperature (90°C) for 4, 8 and 16 hours reaction times. The products were characterized using UV-Vis, IR, and GC-MS. The 4-hours (0°C) and 4-hours (27°C) reactions did not produce any organonitrogen compounds. Meanwhile the 4-hours (90°C) reaction produces organonitrogen compounds, eugenyl acetamide. The 8 - and 16-hours reflux temperature (90°C) reactions are also gives organonitrogen compounds, namely eugenyl acetamide (8% yield in 8-hours reaction time and 13% yield in 16-hours reaction time) and isoquinoline (7% yield in 8-hours reaction time and 11% yield in 16-hours reaction time), in which the yield was increased as the reaction time increased. The toxicity level of the crude obtained from the 16-hours reflux temperature (90°C) reaction time was tested using the Brain Shrimp Lethality Test method to Artemia salina. The result shows that LC50 value is 16 ppm (higher than that of eugenol) and is classified as having a strong toxicity activity.
Optimisation of Methyl Orange Photodegradation Using TiO2-Zeolite Photocatalyst and H2O2 in Acid Condition
Authors: Aziztyana Aprilya Putri , Wardhani Sri , Prananto Yuniar Ponco , Purwonugroho Danar , Darjito  
Published: June 2019

Abstract
This paper discusses an optimisation of methyl orange photodegradation using TiO2-zeolite photocatalyst and H2O2 (with condition of pH 2, 10 ppm, UV light). A 15 mmol TiO2 in 1 gram activated zeolite was used. The influence of H2O2 concentration (4, 8, 12, 16%) and irradiation time (20, 30 40, 50, 60 minutes) was also investigated, as well as initial methyl orange concentration (10, 20, 30 ppm) toward reaction rate constant. As the H2O2 concentration increase, the photodegradation percentage is also increased until it reaches optimum condition (H2O212% for 10 ppm MO solution). Identical effect also showed by the irradiation time in which an optimum point reached at 60 minutes (for 10 ppm MO solution) and 50 minutes (for 20 and 30 ppm MO solutions). Moreover, the higher initial MO concentration used, the lower reaction rate constant obtained. The highest rate constant was shown by MO solution of 10 ppm. The reuse of TiO2-Zeolite photocatalyst remains effective and efficient in photodegradation of methyl orange in solution for up to 4 times.
Quantitative risk modelling of occupational safety in green-port
Authors: Andriani Debrina Puspita , Novianti Vina Dwi , Adnandy Rheza , A’yunin Qurrota 
Published: June 2019

Abstract
Three industrial revolutions are known to have been able to improve the welfare of the community. The fourth industrial revolution or industry 4.0 made many studies carried out on plans, implementations, and other actions that will affect the community. Some industries also began competing to apply industry 4.0 in their systems, including case in this study. This study was conducted in one of the port terminals that are known to be the one and only port that has implemented semi-automatic technology and environmentally friendly in the developing countries. The port operates semi-automatically, so the operating system uses computerization and minimal manpower. As a modern port, this port is equipped with advanced technology, such as automated stacking cranes, ship to shore, and grab ship un-loader, CNG trucks, combined terminal tractors, and others. Port working system is also different from the other ports where only equipment and vehicles that fuelled electricity and gas are allowed to operate in this port, so they also are known as green-ports. Based on preliminary research that had been done, even though the technology and work systems that are applied are sophisticated, but it was known that there were still some occupational accidents that should not have happened, especially in the ship to shore area. The aim of this research was conducted to identify the probability of hazards that occur based on historical data and minimize the risk of further occupational accidents using risk analysis and modelling. Statistical, mathematical, and computational approaches were carried out to obtain risk quantification and develop risk mitigation and response strategies. Thus, the results of the study are expected to help this port as a pilot green-port for other ports, so that it will have a massive positive impact on the change in a more environmentally friendly transportation system.
Kernel Spherical K-Means and Support Vector Machine for Acute Sinusitis Classification
Authors: Arfiani  , Rustam Zuherman , Pandelaki Jacub , Siahaan Arga 
Published: June 2019

Abstract
Acute sinusitis is an inflammation of the sinus which causes the cavity around the sinus to swells due to accumulated mucus. It makes the patient experience difficulty in breathing through the nose. Generally, it is caused by the common cold, and in most cases, the patient recovers within seven to ten days. However, persistent acute sinusitis can cause severe infections and other complications. Therefore, it requires timely detection and more accurate method of classification. Many techniques have been used to classify acute sinusitis but, in this study, the machine learning methods which includes Kernel Spherical K-Means (KSPKM) and Support Vector Machine (SVM) was applied. SPKM is the application of K-Means, in this research, it was modified by changing the inner product with kernel function to ensure linear data separation on higher dimensions for the maximization of SPKM performance. The SVM is a binary classification method that helps to create a model with good generalization ability. We used CT scan result data from RSCM, Central Jakarta. Simulations were performed with different percentage of training data. The results were compared in terms of Accuracy and Running Time. The score showed that the performance of KSPKM attained an accuracy rate of 97%, while SVM reached 90%.
Continuous Ranked Probability Score Validation Methods in Mixture Bayesian Model for Microarray Data in Indonesia
Authors: Astuti Ani Budi 
Published: June 2019

Abstract
Validation in statistical modeling becomes a very important part to get information on how well the model has been built. Algorithm of Continuous Ranked Probability Score (CRPS) is a validation method of goodness of fit model in statistical modeling. A model that has a small CRPS value and has a small statistical significance, then the model is declared fit for data. Conversely, if a model has a large CRPS value, then the model is declared not fit for data. Several applications of the CRPS Algorithm have been developed for unimodal distribution models. Bayesian mixture is a modeling with Bayesian approach where data has a multimodal distribution. Characteristics of multimodal distribution are owned by microarray data in Indonesia, namely data on gene expression differences for several gene IDs from Chickpea plants in Indonesia. The purpose of this study was to obtain a performance from the Continuous Ranked Probability Score (CRPS) Algorithm as a goodness of fit model method in Bayesian Mixture Model (BMM) modeling for microarray data in Indonesia in a series of activities to find new varieties of Chiekpea plants that are resistant to attack by pathogenic fungal diseases Ascochyta Rabiei. The results of this study have succeeded in establishing the Algorithm of Continuous Ranked Probability Score (CRPS) for the distribution of normal mixture for data on gene expression differences of Chickpea plants in Indonesia as a result of microarray experiments with Bayesian approaches. BMM modeling on microarray data is declared fit because it has a small average value of CRPS, which is 0.0412 to 0.385.
Comparison of Cubic SVM with Gaussian SVM: Classification of Infarction for detecting Ischemic Stroke
Authors: Bagasta Amanda Rizki , Rustam Zuherman , Pandelaki Jacub , Nugroho Widyo Ari 
Published: June 2019

Abstract
Ischemic Stroke is a condition whereby the blood supply to the brain is disrupted or reduced due to a blockage and if it is not treated immediately will cause the death of the brain. A decrease in blood flow resulting in dead brain tissue can be called an infarction. The classifications of infarction help the health sector in detecting ischemic stroke in patients. In medicine, CT scans can be used to identify Infarctions and for detecting Ischemic Stroke in patients. Therefore, studying the CT scans is crucial in helping doctors obtain functional information about the surrounding brain tissues which will be used for detecting infarction in the brain. Since it is important to pay more attention at the time of choosing the best method that gives the best results, therefore this study proposes to compare between two types of methods, Gaussian Support Vector Machine (Gaussian SVM) and Cubic Support Vector Machine (Cubic SVM). The Cubic SVM could be an efficient method for infarction classification with accurate performances as high as 80%.
Estimation of Truncated Spline Function in Non-parametric Path Analysis Based on Weighted Least Square (WLS)
Authors: Hidayat Muhamad Fariq , Adji Achmad Rinaldo F. , Solimun  
Published: June 2019

Abstract
The purpose of this research is to identify a particular relationship pattern among predictors and response variables where the response variables are multi-response and correlated. Path analysis is one of the appropriate statistical parametric method to estimate the parameter when the curves are known, but in fact the curves is unknown. Nonparametric approach is a suitable method to estimate the unknown curve in Path Analysis. Truncated spline provides a powerful tool for estimating curve in non-parametric path model with the optimal knots will be estimated by using Generalized Cross Validation. Truncated spline estimator is obtained from Weighted Least Square (WLS) technique that depends on polynomial degree and knot.
Spline Truncated Estimator in Multiresponse Semiparametric Regression Model for Computer based National Exam in West Nusa Tenggara
Authors: Hidayati Lilik , Chamidah Nur , Nyoman Budiantara I 
Published: June 2019

Abstract
Multiresponse semiparametric regression model is a combination of parametric regression model and nonparametric regression model with response variables more than one and correlate. The estimate used in estimate the parameters is spline truncated. Excess spline truncated is a model that has excellent statistical and visual interpretation and can model data with changing patterns on certain sub-intervals, because spline is a kind of polynomial pieces. The data used in this study is the value of Computer Based National Examination (CBNE) Vocational High School (VHS) in the province of West Nusa Tenggara (NTB) in 2017, each subject tested on CBNE serve as response variables. Based on the significant correlation test results obtained p-value <0.05 so it can be concluded that there is correlation between the responses. The result of the multiresponse semiparametric regression model estimation is obtained by the best model with the value of MSE of 49,608; R2 of 0.84 and minimum GCV value of 0.00000323 so it can be concluded that the value of CBNE VHS in NTB province satisfies goodness of fit criterions.
Consumer Satisfaction on Mocaf (Modified Cassava Flour) Based Food Products in Supporting Industrial Revolution 4.0: SEM Approach
Authors: Isaskar Riyanti , Darwanto Dwidjono Hadi , Waluyati Lestari Rahayu , Irham  
Published: June 2019

Abstract
The increase in the snack food industry in Indonesia is one of the effects of improving the current lifestyle. Along with the progress of the digitalization era, technology and marketing strategies that take advantage of the industrial revolution 4.0 demand for snacks is also felt to grow rapidly. Likewise with the growth of culinary tourism provides an opportunity for small industries in the food sector to innovate in developing their products to be attractive to tourists. Mocaf (modified cassava flour) is local flour produced from cassava through a fermentation process, in the long-term it can be used as a substitute for wheat flour. This study aims to determine the variables that affect consumer satisfaction with mocaf-based food products. Purposive sampling technique was applied as a method for determining the sample, with a sample size of 145 people. The analytical method used is structural equation model with the help of warp-PLS software using two exogenous variables namely product quality variable (X1) and price variable (X2) also satisfaction endogenous variable (Y). The results of the analysis show that product quality variables have no significant effect on customer satisfaction, while the price variable has a significant positive effect on customer satisfaction. This shows that in the consumption of mocaf-based processed products, consumers consider product prices more than product quality to influence satisfaction.
Alternative Analytic and Friendly Solution of the Bose-Einstein Integral
Authors: Juwono Alamsyah M. , Istiroyah  , Nuriyah L. 
Published: June 2019

Abstract
A study to find an analytic, and fairly friendly, solution of Bose-Einstein Integral has been conducted, by employing various common mathematical relations, mainly the Fourier Series. The result obtained was without approximation, and did not involve any divergent series. This study was carried out with motivation to obtain analytic solutions from Integral Bose-Einstein, which is very important in the study of Planck Distribution of blackbody radiation, and in Bose-Einstein statistic; and to show that the Bose-Einstein Integral can, in fact, be solved without involving complicated special functions. The Bose-Einstein Integral, surely, had been solved by large number of authors, generally, by utilizing various special functions, such as, by using the Riemann-Zeta function [1][2][6][7]. This study produced the same results as previously obtained through various ways, that is π
4 / 15, which guarantees that the steps we conducted were correct mathematically.
Admission Test Modelling of State Islamic College in Indonesia Using Local Linear for Bivariate Longitudinal Data
Authors: Nidhomuddin  , Chamidah N , Kurniawan A 
Published: June 2019

Abstract
The main challenge in the development of education is improving the quality of education and equitable education. Improving the quality of education is needed in order to create high-quality human resources. Equitable education carried out also still has problems with the uneven quality of education in each region. The aim of this research is to determine the effect of public senior high school status on the admission test scores, i.e., scholastic aptitude test score and Islamic test score of state Islamic colleges in Indonesia. In this research we use a local linear estimator to estimate nonparametric regression model for longitudinal data, and then apply it to the data of admission test scores of state islamic colleges in Indonesia based on the percentage of public senior high school. The result shows that the pattern of the scholastic aptitude test and Islamic test based on the percentage of public senior high school of state Islamic colleges in western Indonesia is different from that in eastern Indonesia. The result of this research can be used by the government to make policies so that the quality of state Islamic colleges will be better by increassing the admission test scores.
Estimation Parameter of Generalized Poisson Regression Model Using Generalized Method of Moments and Its Application
Authors: Girik Allo Caecilia Bintang , Otok Bambang Widjanarko , Purhadi  
Published: June 2019

AbstractPoisson Regression is a standard model for data counts that can be used to determine the relationship between the response variable and predictors variables. Equidispersion is assumptions that must be met in Poisson Regression. Equidispersion is a condition that the variance of the response variable is equal with the average of the response variable. In real cases, these assumptions are often not meet. In real cases, there are overdispersion and underdispersion cases. Generalized Poisson Regression (GPR) is one method that can handle cases of overdispersion and underdispersion. The GPR model is used to estimate regression parameters. Many articles proposed to use only Maximum Likelihood Estimation (MLE) to estimate the parameters of GPR. This article will develop the parameter estimation method of the GPR model, which is using the Generalized Method of Moments (GMM). GPR model is applied in the case of diarrhea in infants in Pasuruan Regency, East Java. The best model is chosen by the value of AICc. The smaller the value of AICc, the better the model. The best model is a model that includes exclusive breastfeeding, complete basic immunization, and healthy living behavior in the model.
Network Analysis of The Brazil Nut Effect Phenomenon with a Single Intruder
Authors: Rahmadhan Putra Muhammad Iqbal , Rudiawan Aufa , Andariwulan Wahyuni , Berasategui Rubén García , Viridi Sparisoma 
Published: June 2019

Abstract
One phenomenon that can be observed in granular systems is the Brazil Nut Effect (BNE), that is, a phenomenon in which large-size particles lift up when vibrated vertically. In this experiment, structural changes in a pseudo-two-dimensional model of a granular system experiencing BNE were observed from the perspective of network analysis. The system consisted of 199 granular beds of 0.68 cm of diameter with a 2.5 cm diameter intruder placed in a 3mm wide double-window box that was slightly larger than the thickness of the bed and the intruder. The system was subjected to vibrations with a frequency of 13.33 Hz and an amplitude of 0.75 cm, so the BNE could be observed. For the purpose of the analysis, the granular beds were considered the nodes of a network and the relationships between adjacent beds (were contact force occurred) represented its edges. The analysis, consisting of image processing, network extraction, network parameters calculation and community detection, was performed using Wolfram Mathematica v. 11.3. The experiment was able to calculate the change in the network parameters including degrees, clustering coefficients, betweenness centrality, and modularity for the system with intruders and systems without intruders. The parameter values corresponding to each system were markedly different, clearly showing the influence of the intruder. The authors were also able to successfully map the evolution of the community structure in both types of granular systems one step at a time using a modularity optimization method.
Kernel Based Fuzzy C-Means Clustering for Chronic Sinusitis Classification
Authors: Putri Rezki Aulia , Rustam Zuherman , Pandelaki Jacub 
Published: June 2019

Abstract
Sinusitis is an inflammation of the sinus wall, a small cavity interconnected through the airways in the skull bones. It is located on the back of the forehead, inside the cheek bone structure, on both sides of the nose, and behind the eyes. Chronic sinusitis is caused by infection, growth of nasal polyps, or irregularities of the nasal septum. This condition can affect teenagers, adults, and even children. To classify sinusitis we use Kernel Based Fuzzy C-Means (FCM) Clustering Algorithm, which is the development of Fuzzy C-Means (FCM) Algorithm. FCM is one of the widely used clustering technique. FCM algorithm comprises of sample points used to make whole and sub vector spaces according to the size of the distance. However, when non-linear data is separated, the convergence is inaccurate and slow. To overcome this problem, a Kernel-Based Fuzzy C-Means algorithm that makes use of kernel functions as a substitute for Euclidean distance utilized. It maps out samples to high-dimensional space to increase the differences between cluster centres, so they can overcome FCM deficiencies and improve linear machine capabilities. Data was obtained from the laboratory of Radiology at Cipto Mangunkusumo National General Hospital, Indonesia, with a 100% accuracy.
Lung Tumor Classification on Human Chest X-Ray Using Statistical Modelling Approach
Authors: Rizka N. , Chamidah N. 
Published: June 2019

Abstract
Lung tumor is a group of abnormal cells that are formed from the process of excessive and uncoordinated cell division in the lung or known as a neoplasia. Neoplasia refers to the growth of new cells that are different from the growth of cells around it. The Tumor can formed to be benign tumors that not cause cancer and malignant tumors that can cause cancer. Chest X-ray is the most technique that used for detecting a lung tumor. Image processing is done by mean for distinguishing the classification lung tumor. Based on previous research the most used method is the mathematical method, but the result obtained are not maximal. Therefore, in this study we propose methods to classify lung tumor by using statistical modelling approach with logit link function based on parametric model, and nonparametric model using penalized spline estimator. Based on the proposed method, we get the classification accuracy of 80% for parametric model approach and 85% for nonparametric model approach, it means that the nonparametric model approach is better than the parametric model approach.