Modelling one-dimensional crystal by using harmonic oscillator potentialAbdurrouf, ; Nurhuda, M.; Wiyono,
doi: 10.1088/1757-899X/546/5/052001pmid: N/A
Our recent developed filter method (Phys. Rev E 96(3), 033302, 2017) is applied here to investigate the energy spectrum and their corresponding wave function of one dimensional crystal. The periodic one dimensional potential is modelled by using one dimensional periodic harmonic oscillator, with variation on oscillator potential depth, quasi-potential depth, and crystal width. For energy less than the potential depth of the oscillator, the computational results reveal that the periodic harmonic oscillator produces a discrete spectrum, as the energy spectrum of a single harmonic potential. However, for energy almost equal to or greater than the depth of the potential oscillator, the periodic harmonic oscillator demonstrates the existence of pattern similar to energy band in crystal.
The rule of radius averaging in hydrogen atomAbdurrouf,
doi: 10.1088/1757-899X/546/5/052002pmid: N/A
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.
Modelling of Hypertension Risk Factors Using Penalized Spline to Prevent Hypertension in IndonesiaAdiwati, Tati; Chamidah, Nur
doi: 10.1088/1757-899X/546/5/052003pmid: N/A
Hypertension is an increase in blood pressure that increases to a target organ, such as stroke, coronary heart disease, right ventricular hypertrophy. Hypertension occurs if the blood pressure reaches 140 mmHg or more and diastole reaches 90 mmHg or more. According to WHO, from 50% of hypertensive patients recovering, only 25% received treatment, and only 12.5% could be treated well. Nationally, 25.8% of Indonesia’s population suffers from hypertension. In this study, we modeled the risk of hypertension by considering age, heart rate, family hypertension, stress levels, and the body’s future index as factors that influence the risk of hypertension. The cross-sectional survey was conducted in August 2018 at the Surabaya Hajj Hospital. Based on previous research the method used is logit and gompit logistic regression method, but the results obtained are not maximal. Therefore, in this study the researchers proposed a method for constructing hypertension risk factor modeling using a nonparametric application using a penalized spline estimator. The result of classification accuracy by using non-parametrical is 96%. Based on the result, we conclude that non-parametrical approach has better than outcome so that it can be used to modelling the risk of hypertension.
Modeling of Parity Status of The Mother and Basic Immunization Giving to Infants with Semiparametric Bivariate Probit (Case Study: North Kalimantan Province in 2017)Amelia, Rahmi; Mashuri, Muhammad; Vita Ratnasari, M.Si
doi: 10.1088/1757-899X/546/5/052004pmid: N/A
The bivariate probit regression model is a probit regression model consisting of two response variables with errors between the two variables correlate each other. The correlation between the two response variables can occur as a result of the presence of endogeneity, a condition in which a response variable becomes an exogenous variable in another response variable. Besides, the important issue that cannot be underestimated is undetectable nonlinear relationships between response variables and predictors, especially discrete or continuous predictor variables. The bivariate probit regression that does not ignore endogeneity cannot detect the nonlinear relationships between response variables and predictors, so one of the regression models that can overcome the problem is bivariate probit regression model with a semiparametric approach. The first step in semiparametric bivariate probit modeling is testing the hypothesis of exogeneity to determine whether there is a case of endogeneity or not. The exogenous test used in this study is the Lagrange Multiplier (LM) and Likelihood Ratio (LR) test. The data used in this study consisted of two binary categorical response variables, they are parity status of the mother and basic immunization giving to infants in North Kalimantan Province in 2017. The results of the exogenous test using the LM test and LR test stated that there was a significant correlation between response variables. The AIC value of the semiparametric bivariate probit model is 1301.602, while the bivariate probit model produces AIC of 1316.789, so it can be concluded that the semiparametric bivariate probit model provides better modeling results than the bivariate probit model.
Grey Wolf Optimizer for Parameter Estimation of Enzymatic Reaction in Biodiesel SynthesisAnam, Syaiful; Kumaralalita, Indira
doi: 10.1088/1757-899X/546/5/052005pmid: N/A
Computational models are used to help us to understand the mechanisms of a complex process in nature. Before building the model, we need to know the characteristic of samples which are described in the form of measure named parameters. Usually, the value of parameters is unknown and we need to investigate those value to know the compatibility between the artificial model and real circumstances. Many optimization methods have been introduced to estimate those parameters, but some of them meet the difficulties caused by the nonlinear type of function model. Many objective functions of the estimation parameter are multimodal, high dimensional, and have many local optima, so the estimation process using traditional optimization method is not suggested. In this article, we use Grey Wolf Optimizer (GWO), as one of the metaheuristic artificial intelligence algorithm, which is inspired by leadership hierarchy and hunting behavior of a pack of wolves. GWO is applied to estimate the parameters in a model of enzymatic reaction in biodiesel synthesis. Biodiesel is renewable fuel that can solve the energy crisis and pollution. While the process of biodiesel synthesis occurs, some enzyme in the biodiesel substances react to each other and it can be modeled into ODEs (Ordinary Differential Equations) system. The kinetic parameters inside them are needed to be estimated. After the parameter are estimated, the fourth-order Runge-Kutta method is used to solve the system. The result is evaluated by analyzing the objective function which minimizes the Sum of Squared Errors (SSE). The small value of SSE and the narrow range of both parameter of model and estimation shows that GWO is effective to be the proposed method for parameter estimation and model selection problems.
Parameters Estimation of Enzymatic Reaction Model for Biodiesel Synthesis by Using Real Coded Genetic Algorithm with Some Crossover OperationsAnam, Syaiful
doi: 10.1088/1757-899X/546/5/052006pmid: N/A
Along with the increase in population and industry in many countries, the fuel oil demand also increases. Petroleum exploration on a large scale will accelerate the depletion of petroleum reserves. One alternative to meet fuel needs is the discovery of biodiesel which is renewable alternative energy. Synthesis biodiesel is carried out through an enzymatic reaction. In the enzymatic reaction model making biodiesel, there are parameters that must be estimated. The estimated parameters of the enzymatic reaction model will determine the success of the reaction. The parameter estimation of the enzymatic reaction model can be done using local optimization or global optimization algorithms, but the local optimization algorithm has a major disadvantage, the optimal value obtained is the local optimal value. Genetic algorithms are global optimization algorithms that are capable of working on high-dimensional problems. The success of genetic algorithms is determined by chromosome models, crossover operations, and mutation operations. The use of improper crossover operations often produces local optimum solutions. There are various types of crossover operation, each of which has weaknesses and advantages. This paper studies the parameters estimation of the enzymatic reaction model for biodiesel synthesis by using genetic algorithms with some crossover operation.
Quantitative risk modelling of occupational safety in green-portAndriani, Debrina Puspita; Novianti, Vina Dwi; Adnandy, Rheza; A’yunin, Qurrota
doi: 10.1088/1757-899X/546/5/052007pmid: N/A
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.
Hybrid radial basis function with firefly algorithm and simulated annealing for detection of high cholesterol through iris imagesAnjarsari, A; Damayanti, A; Pratiwi, A B; Winarko, E
doi: 10.1088/1757-899X/546/5/052008pmid: N/A
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 testAntari, Ika S W; Zain, Ismaini; Suhartono,
doi: 10.1088/1757-899X/546/5/052009pmid: N/A
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 ApproachAnwar, A. E.; Chamidah, N.
doi: 10.1088/1757-899X/546/5/052010pmid: N/A
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.