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All Journal Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI CommIT (Communication & Information Technology) Natural B Industri Inovatif : Jurnal Teknik Industri Pattimura Proceeding : Conference of Science and Technology

Henny Pramoedyo

Jurusan Matematika Fakultas MIPA Universitas Brawijaya Jalan Veteran Malang 65145

Author-ID : 2844205

Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Physics

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Modeling Carrying Capability of Agricultural Land with Spatial Autoregressive Model (SAR) in Batu City Meilinda Trisilia; Henny Pramoedyo; Suci Astutik
Natural B, Journal of Health and Environmental Sciences Vol 2, No 4 (2014)
Publisher : Natural B, Journal of Health and Environmental Sciences

Increasing population growth can lead to the availability of agricultural land becomes smaller, it causes an imbalance of farmers population in a region with an area of agricultural land there, so the population pressure on agricultural land will be greater so that the region no longer can meet the needs of food population. If this happends continue then it is not impossible that the production has not proportional to the needs of existing population, and resulted in the carrying capacity of agricultural land will be smaller. So the analysis of the carrying capacity of agricultural land needs to be done to determine the ability of the land to provide food for the population needs in a given area. Carrying capacity of agricultural land is a function of several spatial variables may give effect in spatial linkages. The model can explain the relationship between variables that have a spatial relationship is called spatial regression models. One of the effective spatial regression models to estimate the effects of data that has a spatial dependency in the response variable is Spatial Autoregressive (SAR) model. Agricultural land supporting food is a phenomenon of spatial autocorrelation. Based on observations made at the carrying capacity of agricultural land for food in every village in Batu City, information obtained that there is significant effects of the percentage of farmers (X1), the land area for a decent life (X2), and the amount of food crops (X3) and the coefficient dependencies on lag (ρ) to the carrying capacity of farmland food (Y).

General Spatial Models (GSM) Approach on Baby Infant Mortality Data Henny Pramoedyo; Meilinda Trisilia
Natural B, Journal of Health and Environmental Sciences Vol 1, No 3 (2012)
Publisher : Natural B, Journal of Health and Environmental Sciences

Proximity and linkages between sites led to the emergence of the phenomenon of spatial linkages. Weighting matrix can be used to determine the proximity and linkages between spatial data or spatial relationships and can be used to calculate the coefficient of spatial dependencies. This study uses spatial panel data, namely the infant mortality rate (IMR) regional data taken from a unit area of development (SWP) Gerbangkertasusila and the Malang-Pasuruan SWP in the period 2005-2009. Those data use rook contiguity to make spatial weighting matrix. The aim of this study is to determine the model of what can be formed from the general spatial model (GSM) using panel data. Estimation of panel models with common effects approaches, fixed effects and random effects, will be followed by estimating the coefficient parameters of the general spatial model on panel data using maximum likelihood estimation method. From the prediction model by using software EViews7 note that all spatial panel data in this study followed the random effect model. To estimate the coefficient parameters of the general spatial model with Matlab-R2010 software is used to obtain a spatial lag / autoregressive models (SAR) of random effects and spatial error models (SEM) random effect. Model selection using the criteria as well as the largest R2 and corr2, and the smallest AIC values, the MSE values and the SC values. The best model for regional infant mortality data is the spatial error models (SEM) random effect.

Spatial Modeling Weibull-3 Survival Parameters with Frailty Distributed Conditionally Autoregressive (CAR) Nur Mahmudah; Henny Pramoedyo
Natural B, Journal of Health and Environmental Sciences Vol 3, No 1 (2015)
Publisher : Natural B, Journal of Health and Environmental Sciences

Survival analysis is a collection of statistical procedures for data analyzing, where respon variables caused by time until an event occurs. One of application of survival regression’s purpose is to know dengue hemorragic fever. Since the spread of dengue hemorragic fever caused by the spread of mosquito, there is probability that event in one location affects other event in another locations thus, it is better to model with Bayessian method of spatial survival. Model includes random spatial effect CAR to overcome the spatial effect in survival model using queen contiguity type weight. This study aimed to obtain spatial survival model one survival data year of 2013 which was the event of dengue hemorragic fever in city of Malang. Based on the data, moran value I was -0.5930 with Z-test value equal to -2,002, which means there is a spatial autocorelation on the event of dengue hemorragic fever in city of Malang. Spatial survival model with Weibull-3 Parameter (Weibull-3P) distribution obtained the factors significantly affecting dengue hemorragic fever, which were sex, hematrocit rate, thrombocyte volume had equal rate of healing in each subdistrict.

Spatial Analysis and Multiple Regression Approach for Determining Soil Organic Material in Sampang Regency Henny Pramoedyo; Ni Wayan Surya Wardhani; Eka Saraswati; Ria Rosilawati
Natural B, Journal of Health and Environmental Sciences Vol 1, No 1 (2011)
Publisher : Natural B, Journal of Health and Environmental Sciences

An organic matter is one of the main components of soil. It is very potential to influence condition or type of soil and further it helps the growth of plants. One of methods which can be used to measure the levels of organic matters in an area is remote sensing technology and Geographic Information Systems (GIS) by using satellites. Analysis could be done in two steps. First, in statistically analysis by using regression models. The equation models of C-Organics level in -0,849 + 0,017X1 - 0.008X3 + 0.011X4. Second, in spatial analysis, it is to know the C-Organic distribution, and also using interpolation with spatial analysis technique which is Inverse Distance Weighted (IDW) methods. Next, testing a model estimation which have been obtained in Sampang. Through the validation analysis using t-paired test, resulting estimation model which have been obtained is able to estimate the C-Organic levels in Sampang which could be an alternative way to estimate the C-Organic levels in same area.

Co-Authors Alfi Fadliana Arif Ashari Danang Ariyanto Eka Saraswati Elok Pratiwi Fahimah Fauwziyah Liduina Asih Primandari Meilinda Trisilia Meilinda Trisilia Ni Wayan Surya Wardhani Novi Nur Aini Nur Mahmudah Rahma Fitriani Ria Rosilawati Suci Astutik Vita Dewi Islami

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