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All Journal Media Statistika Jurnal Mahasiswa Statistik Journal of Degraded and Mining Lands Management Natural B Mutiara Medika: Jurnal Kedokteran dan Kesehatan
Adji Achmad Rinaldo Fernandes
Department Of Statistics, Faculty Of Mathematics And Natural Science, University Of Brawijaya, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Health Professions Medicine & Pharmacology

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PENDUGAAN KOMPONEN RAGAM MODEL PETAK BERJALUR (STRIP PLOT) DENGAN METODE RESTRICTED MAXIMUM LIKELIHOOD (REML) Faisyal, Faisyal; bernadetha mitakda, maria theresia; rienaldo fernandes, adjie achmad
Jurnal Mahasiswa Statistik Vol 4, No 1 (2016)
Publisher : Jurnal Mahasiswa Statistik

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Abstract

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Determination of Stunting Risk Factors Using Spatial Interpolation Geographically Weighted Regression Kriging in Malang Henny Pramoedyo; Mudjiono Mudjiono; Adji Achmad Fernandes; Deby Ardianti; Kurniawati Septiani
Mutiara Medika: Jurnal Kedokteran dan Kesehatan Vol 20, No 2 (2020): July
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/mm.200250

Abstract

Stunting is the condition toddlers have Stunting is the condition toddlers have less length or height if compared to age. The high percentage of stunting is influenced by several factors, namely access to healthy latrines, quality drinking water, hand washing behavior with soap, coverage of posyandu access and coverage of breast milk 1-6 months, and there are indications that if an area has a high stunting percentage, then there is a possibility that the nearest area has the same condition. So, the statistic method for this research use the spatial interpolation Geographically Weighted Regression Kriging. Geographically Weighted Regression (GWR) is a weighted regression in which the weighting function is used to describe the closeness of relations between regions. The weight used is distance based weight dan weighting by area (contiguity). Ordinary kriging method calculated with semivariogram which is one function to describe and model the spatial autocorrelation between data of a variable and function as a measure of variance. The results showed that based on value GWR model with weight Fixed Gaussian Kernel better to use then the weighted GWR model Rook Contiguity. The Predicted of prevelensi stunting in the form of map based on interpolation GWR Kriging. Keywords: Stunting, GWR, and Kriging.
General Linear Mixed Model (GLMM) Bi-response Applications on Diabetes Mellitus Patients Response Adji Achmad Rinaldo; Sulis Harmamik
Natural B, Journal of Health and Environmental Sciences Vol 1, No 2 (2011)
Publisher : Natural B, Journal of Health and Environmental Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (9.381 KB) | DOI: 10.21776/ub.natural-b.2011.001.02.1

Abstract

General Linear Mixed Model  (GLMM) Bi-respon was  an  alternative  solution for  longitudinal  data  with  bi-responses  which  joining  fixed  effects, random effects and vector of  realization of bi-responses process  into single  statistical model. GLMM can  overcome  the  correlation between  observations  in  longitudinal  data  for  the  response  in  the form of continous data.  In each formation GLMM model beginning with  the  determination  of  a  tentative model  through  exploration  of data.  Exploration  data  covering  several  aspects  of  the  individual profile,  average  structure,  variance  structure,  and  correlation structure. Building GLMM  was done by  selecting  fixed  effects under using  Maximum  Likelihood  (ML)  method,  and  the  selection  of variance  components  (the  number  of  random  effects)  using Restricted  Maximum  Likelihood  (REML)  method.  Based  on  the comparison of AIC value, Diabetes Mellitus Type 2 disease data  was better  to  be  modeled  using  GLMM  with  one  response.  Cross correlations  matrix  elements  were  about  0.3  to  0.6  and  produced unstructured  covariance.  Correlation  coefficient  between  two responses was 0.5526 and  produced unstructured  covariance.
Screening potential local seed species for hydroseeding of post-coal mining land multilayering revegetation Muhammad Fadhil Anshari; Adji Achmad Rinaldo Fernandes; Amin Setyo Leksono; Endang Arisoesilaningsih
Journal of Degraded and Mining Lands Management Vol. 11 No. 1 (2023)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2023.111.4969

Abstract

This study aimed to screen some potential local seed grains for hydroseeding and describe their characteristics based on the literature review and a year of hydroseeding application. This study used six species/variants of Poaceae (Coix lacryma-jobi, Eleusine indica, Setaria italica (brown, black, and red), Sorghum timorense, S. bicolor, Themeda arundinaceae), five species of Leguminosae (Adenanthera pavonina, Cajanus cajan, Sesbania grandiflora, S. sesban, Indigofera sp.), a species of Cyperaceae (Cyperus javanicus), Sapindaceae (Sapindus rarak), Rhamnaceae (Ziziphus jujuba), and Moringaceae (Moringa oleifera). A seed germination test was held using soil media placed in 5 pots per species until 15 days after sowing (DAS). Characters were scored, and data were statistically analyzed. A field record of one-year hydroseeding applied on 6 m x 6 m post-coal mining land plot was presented. Some data such as pH H2O, pH KCl, conductivity, and soil organic carbon among hydroseeding areas, unrevegetated areas, and reference sites were observed. Results showed that there were 13 of 17 species could variably germinate. The fastest germination time was recorded for S. timorense, S. bicolor, red S. italica, C. cajan, and S. grandiflora, while the highest germination rate (≥50%) was black S. italica (80%), brown S. italica (58%) and S. bicolor (50%). The annual black and brown S. italica, S. bicolor, and S. timorense were highly recommended to be used in hydroseeding. The perennial C. cajan, Indigofera sp., S. sesban, and T. arundinaceae were also potential to be added into a hydroseeding slurry to improve pioneer vegetation multilayering structure and diversity.
GEOGRAPHICALLY WEIGHTED PANEL LOGISTIC REGRESSION SEMIPARAMETRIC MODELING ON POVERTY PROBLEM Aliyah Husnun Azizah; Nurjannah Nurjannah; Adji Achmad Rinaldo Fernandes; Rosita Hamdan
MEDIA STATISTIKA Vol 16, No 1 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.1.47-58

Abstract

Regression analysis is a statistical method used to investigate and model the relationship between variables. Furthermore, a regression analysis was developed that involved spatial aspects, namely Geographically Weighted Regression (GWR). GWR modeling consists of various types, one of which is Geographically Weighted Logistic Regression Semiparametric (GWLRS), an extension of the Logistic GWR model that produces local and global parameter estimators. In this study, it is proposed to combine the GWLRS model using panel data or Geographically Weighted Panel Logistic Regression Semiparametric (GWPLRS). The case study used in this research is the problem of poverty in 38 regions/cities in East Java, Indonesia, in 2018 – 2022 as seen from the Poverty Gap Index. The weights used in this research are the adaptive gaussian kernel weighting functions. The results of the parameter significance test show that the Human Development Index as global variable has a significant effect on each region/city.
Co-Authors Aliyah Husnun Azizah Amin Setyo Leksono Deby Ardianti Endang Arisoesilaningsih Faisyal, Faisyal Henny Pramoedyo Kurniawati Septiani Mudjiono Mudjiono Muhammad Fadhil Anshari Nurjannah Nurjannah Rosita Hamdan Sulis Harmamik Theresia Mitakda, Maria Bernadetha