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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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Articles

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.

Spatially Filtered Ridge Regression Modeling to Find Out the Rice Production Factors in East Java, Indonesia Vita Dewi Islami; Rahma Fitriani; Henny Pramoedyo
CommIT (Communication and Information Technology) Journal Vol. 14 No. 2 (2020): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v14i2.6665

The research aims to model rice production in East Java using the Spatially Filtered Ridge Regression (SFRR) method and ensure that all violations of assumptions are resolved by knowing the direct and indirect effect of predictor variables. The data are secondary data sourced from the publication of Badan Pusat Statistik containing provincial food crop agriculture statistics in East Java and the 2018 publication of Dinas Pertanian Jawa Timur (literally translated as Agriculture Department of East Java). The data analysis process is done by RStudio and ArcMap 10.3 software. In the research, the observation unit is 38 regencies or cities in East Java. The analysis results show that SFRR with queen contiguity weighting can overcome spatial autocorrelation and multicollinearity in rice production data in East Java. As for the established model, the variables of rice field area, urea fertilizer, Phonska fertilizer, SP-36 fertilizer, and tractor have a significant effect on rice production. However, ZA fertilizer has no significant effect on rice production. Then, a large comparison of direct and indirect impacts for each predictor variable is also generated. Generally, direct impacts are greater than indirect impacts.

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.

ACCELERATED FAILURE TIME MODEL CURE RATE Liduina Asih Primandari; Henny Pramoedyo; Rahma Fitriani
Industri Inovatif : Jurnal Teknik Industri Vol 3 No 2 (2013): inovatif Vol. 3 No. 2
Publisher : Prodi Teknik Industri S1 Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Accelerated Failure Time (AFT) adalah metode yang digunakan untuk mengetahui hubungan antar peubah yangmempengaruhi waktu survival. Metode ini diperluas dengan menggunakan model cure rate. Model cure ratedigunakan apabila data survival terbagi menjadi dua kelompok pasien yaitu susceptible dan immune. Pasiendikatakan susceptible apabila pasien mengalami kejadian yang diamati (kematian) dan dikatakan immune apabilapasien tersebut masih hidup pada akhir penelitian. Model AFT dengan penambahan model cure rate diterapkandalam 3 sebaran yakni sebaran Eksponensial, Weibull dan Log – Logistik kemudian diaplikasikan untukmengetahui hubungan antara usia pasien (Y1) dan waktu menunggu hingga memperoleh donor (Y2) terhadapwaktu survival pasien penerima sumsum tulang belakang (X). Berdasarkan hasil penelitian, diperolehkesimpulan bahwa model AFT parametrik dapat digabungkan dengan model cure rate dengan terlebih dahulumembentuk fungsi survival dari model AFT parametrik. Model AFT parametrik dengan penambahan model curerate hanya dapat digunakan apabila waktu survival terbagi menjadi dua kelompok pasien, yakni susceptible danimmune. Penambahan model cure rate memberikan tambahan informasi, yakni dapat diketahui pula proporsiindividu yang masih hidup (tersensor) dalam kasus ini. Informasi ini dapat berguna untuk mengetahuikeefektifan dari pengobatan yang telah dilakukan.

SPATIAL CLUSTER ING DENGAN METODE SKATER (K’LUSTER ANALYSIS BY TREE EDGE REMOVAL) UNTUK PENGELOMPOKAN SEBARAN COVID-19 DI KABUPATEN TULUNGAGUNG Danang Ariyanto; Henny Pramoedyo; Novi Nur Aini
Pattimura Proceeding 2021: Prosiding KNM XX
Publisher : Pattimura University

Covid-19 telah melanda banyak negara di dunia, termasuk di Indonesia. Penyebaran Covid-19 di Indonesia begitu cepat dengan kasus pertama di daerah Jawa Barat yang kemudian menyebar dengan cepat ke seluruh pulau Jawa, termasuk di Jawa Timur yaitu Kabupaten Tulungagung. Meskipun telah diterapkannya aturan PSBB namun penyebaran SARS-CoV-2 masih terus terjadi. Analisis statistika yang dapat digunakan untuk mengetahui sebaran Covid19 di suatu wilayah adalah analisis kluster spasial. Analisis ini menggunakan pengamatan yang dilakukan berdasarkan letak geografis dan merupakan bagian dari analisis kluster yang tidak terbatas hanya pada eksplorasi saja. Penelitian ini bertujuan untuk mengetahui pengelompokan sebaran covid-19 Kabupaten Tulungagung dengan metode SKATER (K’luster Analysis by Tree Edge Removal). Pengetahuan tentang pengklasteran spasial sangat penting dalam pengendalian penyebaran covid-19, khususnya untuk menurunkan kejadian Covid-19 karena dapat memberikan informasi tentang lokasi populasi yang berisiko. Selain itu, hasil dari pengklusteran spatial juga dapat digunakan sebagai rujukan pengambilan keputusan untuk menyiapkan sarana-prasarana kesehatan yang diperlukan. Berdasarkan metode SKATER, telah terbentuk 3 cluster dan 5 cluster untuk dibandingkan untuk mendapatkan metode mana yang paling baik berdasarkan hasil analisis MANOVA. Berdasarkan nilai Pillai’s Trace diperoleh hasil bahwa metode SKATER dengan 3 cluster lebih baik daripada 5 cluster

INTERPOLASI KRIGING DALAM PEMODELAN GSTAR-SUR DAN GSTARX-SUR PADA SERANGAN HAMA PENGGEREK BUAH KOPI Henny Pramoedyo; Arif Ashari; Alfi Fadliana
MEDIA STATISTIKA Vol 13, No 1 (2020): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The GSTAR and GSTARX models with the SUR approach normally can only be used in forecasting an event in the future in locations where the data is indeed used in forming the model. The problem that sometimes occurs in some cases is that not all locations that want to be modeled do not have data, or if there is data, the data is not as complete as other locations. This study uses GSTAR and GSTARX modeling with the SUR approach and combines them with the kriging interpolation technique in forecasting in an unobserved location. The case study used in this research is PBKo attack forecasting in Probolinggo Regency, where it is simulated that Watupanjang Village is an unobserved location because the location of coffee plantations in the area is difficult to reach due to difficult terrain / access roads. The results showed that PBKo pest attacks in the Probolinggo Regency could be predicted using the GSTAR model (1, [1,12]) and the GSTARX model (1, [1.12]) (10,0,0). Both models, both GSTAR Kriging and GSTARX Kriging, can be relied upon as an alternative to predicting PBKo pests in unobserved locations or where insufficient data are available.

Bahasa Indonesia Bahasa Inggris: Bahasa Indonesia Elok Pratiwi; Henny Pramoedyo; Suci Astutik; Fahimah Fauwziyah
Jurnal Matematika, Statistika dan Komputasi Vol. 19 No. 1 (2022): SEPTEMBER, 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v19i1.21757

Discrete data on the response variable can be analyzed using poisson regression. The assumption of equidispersion in poisson regression must be fulfilled, but in practice there are many problems of overdispersion. The negative binomial regression model is used to overcome the problem of overdispersion, but this model is global while in some cases each location has different characteristics. Therefore, a method that considers the effects of spatial heterogeneity is needed. If the response variable is discrete data that is overdispersed and includes spatial effects, a model called Geographically Weighted Negative Binomial Regression (GWNBR) is developed. The GWNBR method can be applied in the health sector, such as in stunting. The prevalence of stunting in Malang Regency is still quite high, there is 25.7%. By conducting the GWNBR test, 385 models were obtained, one of them is Tulungrejo Village with factors influencing the incidence of stunting, namely access to permanent healthy latrines, access to posyandu, exclusive breastfeeding, population density and community empowerment. From three weights used, namely the Adaptive Gaussian Kernel, Adaptive Bisquare Kernel and Adaptive Tricube Kernel, the best model was obtained from the Adaptive Bisquare Kernel weighting with the smallest AIC is -211.3763.

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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