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Option is one of derivative instruments that can help investors improve their expected return and minimize the risks. However, the Black-Scholes formula is generally used in determining the price of the option does not involve skewness factor and it is difficult to apply in computing process because it produces oscillation for the skewness values close to zero. In this paper, we construct option pricing formula that involve skewness by modified Black-Scholes formula using Shifted Poisson model and transformed it into the form of a Linear Approximation in the complete market to reduce the oscillation. The results are Linear Approximation formula can predict the price of an option with very accurate and successfully reduce the oscillations in the calculation processes.

Rainfall is one of the climate components which is very important for an agrarian country with a tropical climate like Indonesia. There are so many variables that can affect the intensity of rainfall, including humidity, air temperature, speed and wind direction. However, not all of these variables can have a significant effect, therefore, we need a modeling technique that can select and shrink the predictor variables so as to make the model simpler. One of the techniques used is the Lasso (least absolute shrinkage and selection operator). The purpose of this study is to model rainfall intensity using lasso regression and determine which variable have the greatest influence on rainfall intensity. This paper also designs an application system that can be used to implement Lasso regression. The response variables in the form of daily rainfall at the Citeko observation station, Bogor Indonesia is assumed to have normal distribution. Rainfall intensity modeling involves 16 predictor variables, namely the maximum temperature, minimum temperature, average temperature, average humidity, sun exposure, maximum wind speed, wind direction when maximum wind speed and 8 other categorical variables are the most frequent wind direction. The resulting Lasso regression model successfully select and shrink the variables used to 9 variables only. In addition, based on the AIC and coefficient of determination indices for the model evaluation, the Lasso regression shows satisfying results rather than classical multiple linear regression. In Lasso regression, AIC value is 5901.20 where in classical model it is 5911.43. Also, Lasso regression coefficient of determination value is greater that is 22.73% while in classical model is 21.41%. From lasso regression model that has been obtained, it is known that the most frequent wind direction to the north is the variable that has the greatest influence on rainfall intensity. The Lasso regression application has been successfully created so that it can facilitate the user in uploading data, exploration of data and variables, Lasso regression analysis and report result. [ABSTRACT FROM AUTHOR]

Malnutrition is a condition of serious nutritional disorders that occurs when food intake does not match the amount of nutrients needed. This nutritional disorder is fatal to a toddler's health if not treated immediately. For this reason, the purposes of this study are to model and map malnutrition cases by taking into account regional aspects using the Bayes spatial analysis whose inference uses INLA (integrated nested Laplace approximation). The spatial Bayes model used is a generalized linear mixed model, by including random effects in the form of conditional autoregressive spatial structured components. The response variable is the number of cases of malnutrition in 22 city districts in Indonesia's East Nusa Tenggara province, which is assumed to have a Poisson distribution. In spatial modeling, the fixed effects as the explanatory variables are included, i.e. the number of children under five given complete immunization, the poverty depth index, the number of maternal and child health services, population density and the average duration of breastfeeding. The results of spatial modeling show that the poverty depth index is the main variable that has a significant effect on the number of malnutrition cases. From the results of spatial mapping, it can be seen that there are regional links that affect the number of malnutrition cases, including in Sumba Barat Daya, Sumba Barat and Sumba Utara which have a high probability of malnutrition risk rather than in Sumba Timur. [ABSTRACT FROM AUTHOR]

Based on geographical location, Indonesia is crossed by the equator which has a tropical climate with a high diversity of rainfall. This can lead to an increase or decrease in extreme rainfall that has the potential to cause hydrometeorological disasters such as floods, winds and landslides. Estimation of rainfall is not only influenced by the local topography in each region, but there are also spatial and temporal influences. Therefore, this study aims to estimate the pattern of extreme rainfall and map the rain observer station based on the status of extreme rainfall on spatio-temporal data in the West Java province, Indonesia. Estimator of extreme rainfall patterns uses the generalized linear mixed model (GLMM) which consists of spatial and temporal random components with INLA (integrated nested Laplace approximation) inference. Response data in the form of daily rainfall in 73 observer stations, within 2 years period of observation. Average rainfall data is assumed to have gamma distribution, rainfall above the average is identified using the Bernoulli distribution then identified (extreme) rainfall above average is assumed to have generalized pareto distribution. From the rainfall patterns produced there are a number of conclusions, including that the average rainfall in West Java experiences a consistent downward trend in 13-th week to 32-nd week, which indicates that there is a decrease in rainfall intensity at the beginning of dry season in early April until the end of dry season at the end of August. While in the case of extreme rainfall, the decrease in rainfall intensity consistently occurs from the first week in January to 32-nd week at the end of August. The resulting extreme rainfall mapping states that more extreme rainfall occurs in the north to southeast region of West Java. [ABSTRACT FROM AUTHOR]

Abstract: We propose a method that recognizes and tracks face using stereo vision and can be used for vision-based service robots. Face recognition and tracking are important features for vision-based service robot in order to obtain detailed information about people around the robot and do specific action based on the task given before. We have developed and introduced successfully three main modules to train faces online, to recognize and track faces. The system consists of stereo camera and able to recognize and track face using 2 servos as actuators. In our scenario, online training for faces conducted using PCA to store information of the people. We propose a probabilistic model using Bayesian Filtering for this system and combined with controller using ANFIS (Adaptive Neuro Fuzzy Inference System) controller with 2 inputs (and coordinates) and 2 output to tracks the people s face smoothly. The experimental results show that our proposed method working well and can recognize and track face with different speed smoothly. [Copyright &y& Elsevier]

Abstract: In this paper, we construct the estimation for periodic component of the intensity function of a periodic Poisson process in the presence of power function trend with uniform kernel function. It is considered the worst case where there is only available a single realization of Poisson process having intensity which consist of a periodic component and a power function trend, observed in interval [0,n]. It is assumed that the period of periodic component is known and the slope of the power function trend is unknown. It has been formulated the estimator and the proof of convergence of the bias, variance and mean square error of the estimator. The proofs and the simulations produce the estimator that we construct is asymptotically unbiased estimator and mean square error of the estimator converge to zero while n is large. [Copyright &y& Elsevier]