Budi Nurani Ruchjana

Least squares estimation of Generalized Space Time AutoRegressive (GSTAR) model and its properties

In this paper we studied a least squares estimation parameters of the Generalized Space Time AutoRegressive (GSTAR) model and its properties, especially in consistency and asymptotic normality. We use R software to estimate the GSTAR parameter and apply the model toward real phenomena of data, such as an oil production data at volcanic layer.

Modeling of Infectious Diseases: A Core Research Topic for the Next Hundred Years

Incidence of infectious diseases is an under-researched topic in regional science. This situation is unfortunate because the occurrence of these types of diseases frequently has far-reaching welfare impacts at household, regional, national, and even international levels. Given its welfare impacts and soaring incidence, inter alia, because of climate change, increasing population density, higher mobility, and increasing immunity to several common medicines, the occurrence and spread of infectious diseases should become a regular research topic in regional science. There are also methodological reasons why regional scientists should pay (more) attention to the incidence of infectious diseases. Although both regional science and epidemiology deal with the spatial distributions of their research topics and apply spatial analytical techniques, important methodological differences between them open possibilities for cross-fertilization. This study presents an overview of the main models and estimators of infectious disease incidence. We first discuss maximum likelihood (ML), which is the most common estimator. It is unbiased but imprecise and unreliable for small regions. Next we discuss several methods that have been proposed to improve ML estimation by smoothing (i.e., Bayesian smoothing techniques and nonparametric estimators). From the review, we conclude that none of the models used so far adequately considers the most basic characteristic of infectious diseases, namely, spatial spillover. We argue that the development and application of infectious disease models that allow for spatial spillover is a core research topic for the years to come. We conclude the chapter with suggestions for future regional science research themes in the area of infectious diseases.

Image indexing using color histogram and k-means clustering for optimization CBIR in image database

Retrieving visually similar images from image database needs high speed and accuracy. Various text and content based image retrieval techniques are being investigated by the researchers in order to exactly match the image features. In this paper, a content-based image retrieval system (CBIR), which computes color similarity among images, is presented. CBIR is a set of techniques for retrieving semantically relevant images from an image database based on automatically derived image features. Color is one important visual features of an image. This document gives a brief description of a system developed for retrieving images similar to a query image from a large set of distinct images with histogram color feature based on image index. Result from the histogram color feature extraction, then using K-Means clustering to produce the image index. Image index used to compare to the histogram color feature of query image and thus, the image database is sorted in decreasing order of similarity. The results obtained by the proposed system obviously confirm that partitioning of image objects helps in optimization retrieving of similar images from the database. The proposed CBIR method is compared with our previously existed methodologies and found better in the retrieval accuracy. The retrieval accuracy are comparatively good than previous works proposed in CBIR system.

Generalized space time autoregressive with exogenous variable model and its application

In this paper we proposed the Generalized Space Time Autoregressive with variable Exogenous, abbreviated GSTARX as GSTAR development with the addition of exogenous variables. GSTARX not only involves the element of time and location, but also the influence of exogenous variables in the model. GSTARX equation can be written as a linear model, so we can estimate parameters of GSTARX model using Ordinary Least Squares (OLS) method. For our case study, we use GSTARX model with uniform and inverse distance weights to predict an export volume of Crude Palm Oil (CPO) in several locations on the island of Sumatera, where X is the international CPO prices.

Implementation of Generalized Space Time Autoregressive (GSTAR)-Kriging Model for Predicting Rainfall Data at Unobserved Locations in West Java

A Generalized Space Time Autoregressive or GSTAR is a specia l model of Vector Autoregressive (VAR) model which is a combination of time series and spatial models which has t e assumption of autoregressive parameter and space time pa rameter having different value for each location of observation. In addition, it assumes stationary time series data at the mean and variance levels and applies to locations with heterogeneous charact eristics. One disadvantage of the GSTAR model is that it can n ot be used to predict at unobserved locations. In this paper we combine the GSTAR model with the Ordinary Kriging (OK) technique, na med GSTAR-Kriging model so that the GSTAR model can be used to pre dict in unobserved locations. GSTAR parameters are estimat ed using the Ordinary Least Squares (OLS) method and these are u sed as inputs for the Kriging technique. Furthermore, by usi ng linear semivariogram we can obtain simulations to predict the GSTA R parameters. For the case study we applied the model to annua l rainf ll data in wet season (Desember, January and February) from sev eral locations in West Java, Indonesia, such as Majalengka, Kuningan and Ciamis Regencies. The GSTAR (1;1) model in observed loca tion have Mean Average Percentage Error (MAPE) value overal l l ss than 15 percent and residual of model have identically indep endent distributed normal. The results of GSTAR-Kriging mo del show that the GSTAR-Kriging parameter at unobserved locations are al most similar to GSTAR parameter at observed locations.

Prediction of cadmium pollutant with ordinary point kriging method using Gstat-R

Kriging is a method of estimation that provides an unbiased linear prediction of the values of a point or block. Ordinary point kriging is one of the most simple kriging method when the average population is not known which normally applied to the spatial data, for example Meuse river floodplain. On the Meuse river floodplain are contaminating metals such as cadmium, zinc, copper and lead, it is necessary to predict the location that contains cadmium. Calculation of cadmium pollutant can be implemented using the software Gstat-R in order to obtain accurate results. In the calculation of prediction with ordinary point kriging method required gstat library, sp library and some algorithms in GStat-R to be applied to the data of Meuse river floodplain to obtain an index prediction of pollutant in unobserved locations. Calculation of the index prediction of pollutant using GStat-R is easy, fast and accurate because the average kriging variance minimum resultant. GStat-R can also display contours showing the lo...

Development of the statistical ARIMA model: an application for predicting the upcoming of MJO index

This study is mainly concerned in development one of the most important equatorial atmospheric phenomena that we call as the Madden Julian Oscillation (MJO) which having strong impacts to the extreme rainfall anomalies over the Indonesian Maritime Continent (IMC). In this study, we focused to the big floods over Jakarta and surrounded area that suspecting caused by the impacts of MJO. We concentrated to develop the MJO index using the statistical model that we call as Box-Jenkis (ARIMA) ini 1996, 2002, and 2007, respectively. They are the RMM (Real Multivariate MJO) index as represented by RMM1 and RMM2, respectively. There are some steps to develop that model, starting from identification of data, estimated, determined model, before finally we applied that model for investigation some big floods that occurred at Jakarta in 1996, 2002, and 2007 respectively. We found the best of estimated model for the RMM1 and RMM2 prediction is ARIMA (2,1,2). Detailed steps how that model can be extracted and applying to predict the rainfall anomalies over Jakarta for 3 to 6 months later is discussed at this paper.

Rainfall prediction of Cimanuk watershed regions with canonical correlation analysis (CCA)

Rainfall prediction in Indonesia is very influential on various development sectors, such as agriculture, fisheries, water resources, industry, and other sectors. The inaccurate predictions can lead to negative effects. Cimanuk watershed is one of the main pillar of water resources in West Java. This watersheds divided into three parts, which is a headwater of Cimanuk sub-watershed, Middle of Cimanuk sub-watershed and downstream of Cimanuk sub- watershed. The flow of this watershed will flow through the Jatigede reservoir and will supply water to the north-coast area in the next few years. So, the reliable model of rainfall prediction is very needed in this watershed. Rainfall prediction conducted with Canonical Correlation Analysis (CCA) method using Climate Predictability Tool (CPT) software. The prediction is every 3months on 2016 (after January) based on Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data over West Java. Predictors used in CPT were the monthly data index of Nino3.4, Dipole Mode (DMI), and Monsoon Index (AUSMI-ISMI-WNPMI-WYMI) with initial condition January. The initial condition is chosen by the last data update. While, the predictant were monthly rainfall data CHIRPS region of West Java. The results of prediction rainfall showed by skill map from Pearson Correlation. High correlation of skill map are on MAM (Mar-Apr-May), AMJ (Apr-May-Jun), and JJA (Jun-Jul-Aug) which means the model is reliable to forecast rainfall distribution over Cimanuk watersheds region (over West Java) on those seasons. CCA score over those season prediction mostly over 0.7. The accuracy of the model CPT also indicated by the Relative Operating Characteristic (ROC) curve of the results of Pearson correlation 3 representative point of sub-watershed (Sumedang, Majalengka, and Cirebon), were mostly located in the top line of non-skill, and evidenced by the same of rainfall patterns between observation and forecast. So, the model of CPT with CCA method is reliable to use.

The role of ethnomathematics in West Java (a preliminary analysis of case study in Cipatujah)

Indonesia is a multicultural country of which people usually do anything influenced by their culture. The culture contain of many aspects, one of them is ethnomathematics. It is a study about the connection between culture and mathematics concepts. It also reveals mathematical practices of day life. These mathematical practices could be seen at Cipatujah, West Java, Indonesia. It has several ethnics which implemented of ethnomathematics in their life, for example they implement traditional mathematical concept in the way they determine the time to head seaward for fishing, and the way they construct their houses. The exploration will describe about how deep is the role of ethnomathematics in Cipatujah and state any problem found according to the exploration result. The objectives of this study is to show that ethnomathematics holds an important role in our day life, with the case study of primary school students and for the people of Cipatujah. The method which implemented in this study is exploratory. The result is the people of Cipatujah have been implemented ethnomathematics in their life for many years and believe that ethnomathematics is part of their life, but the teachers of primary school there have not implemented ethnomathematics approach yet in learning process of mathematics. Conclusion of this study is ethnomathematics as the root of culture life in West Java.

Application of universal kriging for prediction pollutant using GStat R

In the universe, the air and water is a natural resource that is a very big function for living beings. The air is a gas mixture contained in a layer that surrounds the earth and the components of the gas mixture is not always constant. Also in river there is always a pollutant of chemistry concentration more than concentration limit. During the time a lot of air or water pollution caused by industrial waste, coal ash or chemistry pollution is an example of pollution that can pollute the environment and damage the health of humans. To solve this problem we need a method that is able to predict pollutant content in locations that are not observed. In geostatistics, we can use the universal kriging for prediction in a location that unobserved locations. Universal kriging is an interpolation method that has a tendency trend (drift) or a particular valuation method used to deal with non-stationary sample data. GStat R is a program based on open source R software that can be used to predict pollutant in a location that is not observed by the method of universal kriging. In this research, we predicted river pollutant content using trend (drift) equation of first order. GStat R application program in the prediction of river pollutants provides faster computation, more accurate, convenient and can be used as a recommendation for policy makers in the field of environment.