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All Journal Journal of Innovation and Applied Technology Mutiara Medika: Jurnal Kedokteran dan Kesehatan ComTech: Computer, Mathematics and Engineering Applications
Henny Pramoedyo
Brawijaya University
Computer Science & IT Engineering Health Professions Mathematics Medicine & Pharmacology

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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.
Community Assistance For Quality Improvement And Testing Of Dairy Products As A Superior Product In Krisik Village, Gandusari District, Blitar Regency Henny Pramoedyo; Novi Nur Aini; Bestari Archita Safitri; Suci Astutik; Achmad Efendi; Loekito Adi Soehono
Journal of Innovation and Applied Technology Vol 8, No 1 (2022)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jiat.2022.008.01.5

Abstract

Krisik Village is one of the villages located in Gandusari District, Blitar Regency, East Java Province. Krisik Village has abundant natural resources. Krisik Village has livestock products in the form of milk and its processed products which are managed independently by the Bumdes Krisik. Krisik Village already has several types of dairy products, namely fermented milk, milk sticks, milk candy and milk ice cream. As a step to improve the typical product of Krisik village, it is necessary to have an activity that is able to increase public understanding in product processing and marketing. This service activity aims to improve the quality and marketing of dairy products in Krisik village. Activities that have been carried out are in the form of coordination with the village, making ice cream packaging designs and product marketing training by utilizing social media. This activity is expected to increase the independence of the crisis village community in marketing their products.
Spatial Modeling of Fixed Effect and Random Effect with Fast Double Bootstrap Approach Wigbertus Ngabu; Henny Pramoedyo; Rahma Fitriani; Ani Budi Astuti
ComTech: Computer, Mathematics and Engineering Applications Vol. 14 No. 1 (2023): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v14i1.8033

Abstract

The use of panel data on spatial regression has many advantages. However, testing the spatial dependency and parameter presumption generated in spatial regression of panel data becomes inaccurate when applied to regions with large numbers of small spatial units. One method of overcoming problems of small spatial unit sizes is the bootstrap method. The research aimed to combine cross-section and time-series panel data. The analysis was performed to extract information based on observations modified by the influences of space or location, known as spatial analysis of panels. The influence of location effects on spatial analysis was presented in the form of weighting. The research applied the Fast Double Bootstrap (FDB) method by modeling poverty rates on Flores Island. The results of the Hausman test show the right model, which is a random effect. Meanwhile, spatial dependency testing concludes spatial dependence and poverty modeling in Flores Island, which is more likely to be the Spatial Autoregressive Random (SAR) model. SAR random effect in modeling value has R2 of 77,38% and does not meet the normality assumption. SAR effect in modeling the FDB approach can explain the diversity of poverty rate in the Flores Island with 88,64% and meets residual normality assumptions. The analysis with the FDB approach on spatial panels shows better results than the common spatial panels.
Co-Authors Achmad Efendi Adji Achmad Fernandes Ani Budi Astuti Bestari Archita Safitri Deby Ardianti Kurniawati Septiani Loekito Adi Soehono Mudjiono Mudjiono Novi Nur Aini Rahma Fitriani Suci Astutik Wigbertus Ngabu