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Moh Yamin Darsyah
UIN Walisongo
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Industrial & Manufacturing Engineering

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Journal : Jurnal Litbang Edusaintech

 1 
Pemodelan Generalized Additive Model For Location, Scale, and Shape (Gamlss) Dengan Pemulusan Locally Estimated Scatterplot Smoothing (Loess) pada Kasus Hiv/Aids Di Jawa Timur : Pemodelan Generalized Additive Model For Location, Scale, and Shape (Gamlss) Dengan Pemulusan Locally Estimated Scatterplot Smoothing (Loess) pada Kasus Hiv/Aids Di Jawa Timur Silvia Tri Wahyuni; Tiani Wahyu Utami; Moh Yamin Darsyah
Jurnal Litbang Edusaintech Vol. 2 No. 1 (2021): Volume 2 No 1 2021
Publisher : Litbang PWM Jawa Tengah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51402/jle.v2i1.7

Abstract

HIV / AIDS is a contagious disease that can attack all age groups of the population and is a health challenge in almost all over the world including Indonesia. Therefore, it is necessary to model HIV / AIDS cases for the factors that are suspected to influence them. One suitable method for estimating factors that influence HIV / AIDS is the Generalized Additive Model for Location, Scale, and Shape (GAMLSS). The GAMLSS method is flexible because it includes expansion of a good exponential family distribution to handle overdispersion data, continuous data, and discrete data. This research will apply GAMLSS semiparametric modeling with LOESS smoothing to find out the characteristics and models of HIV / AIDS cases in East Java in 2017. Based on the analysis, it was found that the variables that significantly affected were the number of homeless people, number of victims of drug abuse, population poor, and the number of fertile age couples using condom contraception with AIC value of 437,404, degree = 1 and span = 0.3, and the distribution used is Negative Binomial I.
RESIDUAL BOOTSTRAP RESAMPLING METHOD FOR MULTIPLE LINEAR REGRESSION MODEL PARAMETER ESTIMATION : RESIDUAL BOOTSTRAP RESAMPLING METHOD FOR MULTIPLE LINEAR REGRESSION MODEL PARAMETER ESTIMATION Fajar Prihatmono; Moh Yamin Darsyah; Abdul Karim
Jurnal Litbang Edusaintech Vol. 1 No. 1 (2020): Volume 1 No 1 2020
Publisher : Litbang PWM Jawa Tengah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51402/jle.v1i1.8

Abstract

Fuzzy Geographically Weighted Clustering dengan Gravitational Search Algorithm pada Kasus Penyandang Masalah Kesejahteraan Sosial di Provinsi Jawa Tengah : Fuzzy Geographically Weighted Clustering dengan Gravitational Search Algorithm pada Kasus Penyandang Masalah Kesejahteraan Sosial di Provinsi Jawa Tengah Syayidati Mashfufah; Indah Manfaati Nur; Moh Yamin Darsyah
Jurnal Litbang Edusaintech Vol. 2 No. 1 (2021): Volume 2 No 1 2021
Publisher : Litbang PWM Jawa Tengah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51402/jle.v2i1.10

Abstract

One of the indicators of the success of social welfare development in Central Java was decreasing the population of people with social welfare problems (PMKS). One exertion that can be done was grouping or clustering the areas in Central Java-based on 26 indicators of PMKS. Fuzzy Geographically Weighted Clustering (FGWC) algorithm is a clustering analysis that observing the effect of the area. However, FGWC has a limitation in the initialization centroid phase that makes it trapped to local optimal. The limitation can be addressed with the Gravitational Search Algorithm (GSA) approach. The purpose of GSA was to optimize the value objective function. This research applied FGWC-GSA on PMKS in Central Java Province contained 26 indicators. Some validity indexes were applied to determine the best cluster. This research clustering the areas of Central Java into two clusters. The first cluster contained 24 districts and cities, and the second cluster contained 11 districts
PEMODELAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION (GWNBR) PADA KASUS MALARIA DI INDONESIA Moh Yamin Darsyah
Jurnal Litbang Edusaintech Vol. 2 No. 2 (2021): Volume 2 No 2 2021
Publisher : Litbang PWM Jawa Tengah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51402/jle.v2i2.100

Abstract

Geographically Weighted Negative Binomial Regression Modeling by Comparing Adaptive Gaussian Weighting and Adaptive Tricube in Cases of Malaria in Indonesia. Malaria was an infectious disease caused by the bite of female malaria mosquitoes (Anopheles) caused by the Plasmodium parasite that breeds in human red blood cells. Malaria is one of the biggest causes of death in Indonesia, so it needs special handling in preventing the number of Indonesian malaria cases. The spread of malaria cases was caused by population density, the households with clean and healthy living behaviors (PHBS), the sufferers receiving ACT programs, and the households living in slums. Indonesia is a unitary state that has a large area and certainly has different environmental characteristics. So that spatial regression analysis is the right solution for the case of Malaria in Indonesia. The spatial regression analysis used is Geographically Weighted Negative Binomial Regression (GWNBR) is one of the models on spatial points. The purpose of this study is to determine the best modeling using GWNBR with malaria cases in Indonesia and the factors that influence it from a regional perspective by comparing the Adaptive Gaussian weighting matrix and Adaptive Tricube weighting matrix. The results showed that the best modeling with the smallest AIC value of 695,2341962 was Geographically Weighted Negative Binomial Regression (GWNBR) with Adaptive Tricube weighting. Significant variable are population density, provision of ACT treatment and slums by taking samples from Papua Province as the province with the highest number of malaria cases.
Co-Authors Fajar Prihatmono Indah Manfaati Nur Karim, Abdul Silvia Tri Wahyuni Syayidati Mashfufah Tiani Wahyu Utami