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All Journal LOGIK@ Pythagoras: Jurnal Matematika dan Pendidikan Matematika CAUCHY: Jurnal Matematika Murni dan Aplikasi Global Medical and Health Communication Jurnal Matematika: MANTIK Jurnal Penelitian Pendidikan IPA (JPPIPA) BAREKENG: Jurnal Ilmu Matematika dan Terapan Berdikari : Jurnal Ekonomi dan Statistik Indonesia Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Indonesian Journal of Advanced Research (IJAR) Euclid
I Gede Nyoman Mindra Jaya
Jurusan Statistika, FMIPA Universitas Padjadjaran
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Spatial Autoregressive Model of Tuberculosis Cases in Central Java Province 2019 Zebua, Hasrat Ifolala; Jaya, I Gede Nyoman Mindra
CAUCHY Vol 7, No 2 (2022): CAUCHY: Jurnal Matematika Murni dan Aplikasi (May 2022) (Issue in Progress)
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v7i2.13451

Abstract

Tuberculosis is an infectious disease caused by infection with the bacterium Mycobacterium tuberculosis. Central Java is one of the three provinces with the highest tuberculosis cases in Indonesia. Some of the risk factors used in this research are the spatial lag of the number of tuberculosis cases representing the agent component, the morbidity rate representing the host component, population density, proper sanitation, and proper drinking water which represent environmental components. This study uses the Spatial Autoregressive (SAR) model. The SAR model is a regression model where the response variable has a spatial correlation. The estimation method usually used in SAR model is maximum likelihood. The value of Moran's I on the number of tuberculosis cases in Central Java is 0.499 and is significant, which means that there is a positive spatial autocorrelation. The model was chosen based on the LM test and AIC. The best model is the SAR model. The results of the analysis obtained show that the greater the number of tuberculosis cases is influenced by the number of tuberculosis cases in the surrounding area. Proper sanitation has a negative effect, on the contrary, the dense population has a positive effect on the number of tuberculosis cases in the province of Central Java.
APLIKASI LATENT DIRICHLET ALLOCATION (LDA) PADA CLUSTERING DATA TEKS . Zulhanif; . Sudartianto; Bertho Tantular; I Gede Nyoman Mindra Jaya
LOGIK@ Vol 7, No 1 (2017): Vol.7 No.1 Tahun 2017
Publisher : Universitas Islam Negeri Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (473.878 KB)

Abstract

Latent Dirichlet Allocation (LDA), model probabilistik generatif pada sekumpulan data teks (corpus) . LDA adalah model Bayesian Hirarki , di mana sekumpulan teks dimodelkan sebagai model campuran dari berbagai topik. Dalam kontek pemodelan teks, probabilitas topik memberikan representasi eksplisit dari sebuah dokumen. Pada penelitian ini menyajikan teknik inferensi berdasarkan algoritma Gibbs Sampling untuk mengestimasi parameter Bayes dalam pemodelan dokumen dan klasifikasi teks.
Pemodelan Kriminal di Jawa Timur dengan Metode Geographically Weighted Regression (GWR) Imanudin Nurhuda; I Gede Nyoman Mindra Jaya
Jurnal Matematika MANTIK Vol. 4 No. 2 (2018): Mathematics and Applied Mathematics
Publisher : Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (332.218 KB) | DOI: 10.15642/mantik.2018.4.2.150-158

Abstract

Criminality constitutes all kinds of actions that are economically and psychologically harmful in violation of the law applicable in the state of Indonesia as well as social and religious norms, while the criminal data is the number of cases reported to the police institution. The higher the number of complainants the higher the number of criminals in the region. The greater the risk the community represents the more insecure a region is. This study aims to obtain the best model affecting crime or crime in East Java. The number of crimes in this study is limited to the number of theft cases (whether ordinary theft, theft by force, theft with theft, and the theft of motor vehicles). In this study, we use the Geographically Weighted Regression (GWR) model because this method is quite effective in estimating data that has spatial heterogeneity (uniformity in location / spatial). In essence, the model parameters in GWR can be calculated at the observation location with the dependent variable and one or more independent variables that have been measured at the sites where the location is known, where criminal acts in the research conducted in East Java involves the effects of spatial heterogeneity, with fixed kernel weighting function. The results showed that the variables affecting criminality in East Java Province are population density, economic growth, Gini Ratio, and poverty.
Modeling Rice Production in West Java by Means Geographically Weighted Regression Muhamad Sobari; I Gede Nyoman Mindra Jaya
Jurnal Ekonomi Dan Statistik Indonesia Vol 2 No 3 (2022): Berdikari: Jurnal Ekonomi dan Statistik Indonesia (JESI)
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/jesi.02.03.08

Abstract

In Indonesia, rice production varies from province to province, resulting in both large and small disparities between provinces. In Indonesia, East Java, Central Java and West Java Provinces have the highest rice production. In contrast to East Java and Central Java, however, the total rice consumption per year in West Java is the highest. In linear regression, the coefficients are the same for all regions, while each region sometimes has different influencing factors, resulting in spatial diversity. Consequently, the Geographically Weighted Regression (GWR) method was used to model the rice production of West Java Provincial regencies/municipalities by accounting for spatial heterogeneity. The GWR model employs the fixed bi-square kernel function as its weighting function. This model includes five explanatory variables, such as number of agricultural labor, number of used rice seed, number of two-wheel tractor, number of water pump, and number of farmer groups, with rice production as the response variable. GWR model has greater coefficient determination (96.8 percent) and smaller AIC values (920.76) than global regression. During the period of 2018-2020, the number of two-wheel tractors and the number of water pumps had the greatest impact on rice production in West Java and the number of two-wheeled tractors and the number of farmer groups variables has an effect on rice production in most regencies/municipalities in West Java. There are 11 groups of areas which has the similarity of significant predictor variables.
Calculation of the Risk Index for Diarrhea, ISPA, and Pneumonia in Toddlers in the City of Bandung Using Geographically Weighted Principal Component Analysis Azka Larissa Rahayu; Gumgum Darmawan; I Gede Nyoman Mindra Jaya
Indonesian Journal of Advanced Research Vol. 2 No. 4 (2023): April 2023
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (874.996 KB) | DOI: 10.55927/ijar.v2i4.3868

Abstract

Diarrhea, ISPA, and pneumonia are infectious diseases that are prone to occur in toddlers. The management of these three diseases is included in the Republic of Indonesia Ministry of Health's National Priorities for 2020-2024. One area that has a high risk of diarrhea, ARI, and pneumonia in toddlers is the city of Bandung. Effective and efficient disease control is needed, namely by controlling the three diseases simultaneously which can be emphasized on improving environmental quality, especially in areas with high disease risk. The analysis found that there is a spatial dependence on each variable and each variable is correlated with one another. Therefore, in this study used Geographically Weighted Principal Component Analysis (GWPCA). Calculation of the risk index and mapping with GWPCA produces a combined risk index of the three observed diseases by considering the spatial dependence of the data.
METODE BAYESIAN DALAM PENAKSIRAN MODEL SPATIAL AUTOREGRESSIVE (SAR) (STUDI KASUS PEMODELAN PENYAKIT TB PARU DI KOTA BANDUNG) I Gede Nyoman Mindra Jaya; Zulhanif Zulhanif; Bertho Tantular; Neneng Sunengsih
Euclid VOL 4, NO 2 (2017): EDISI JULI
Publisher : Universitas Swadaya Gunung Jati.

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (867.539 KB) | DOI: 10.33603/e.v4i2.419

Abstract

Aplikasi pemodelan spatial ekonometrik dalam berbagai bidang ilmu semakin banyak khususnya dalam ruang lingkup spatial regional dan spatial epidemiologi. Metode ini berkembang karena kemampuan metode ini mengakomodasi adanya ketergantungan spatial dalam data. Analisis ekonometrik biasa tidak mampu memberikan hasil yang baik pada saat data tidak berdistribusi independen. Metode Maksimum likelihood adalah metode yang umumnya digunakan untuk menaksir parameter model spatial econometrics. Namun metode ini tidak cukup baik dalam mengestimasi parameter model pada saat unit spatialnya sangat banyak. Metode alternative Bayesian diperkenalkan untuk mengatasi masalah tersebut. Penelitian ini mengkaji pendekatan metode Bayesian pada model Spatial Autoregresive (SAR). Model SAR merupakan satu dari delapan model spatial ecokometrik yang paling banyak digunakan. Pendekatan Bayesian akan diaplikasikan pada pemodelan kasus TB Paru di Kota BandungKata kunci: Bayesian, Model SAR, Maximum Likelihood, Spatial
ANALISIS INTERAKSI GENOTIP x LINGKUNGAN MENGGUNAKAN STRUCTURAL EQUATION MODELING Sumertajaya, I Made; Matjjik, Ahmad Ansori; Mindra Jaya, I Gede Nyoman
PYTHAGORAS Jurnal Matematika dan Pendidikan Matematika Vol. 4 No. 1: Juni 2008
Publisher : Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (688.75 KB) | DOI: 10.21831/pg.v4i1.684

Abstract

Additive Main Effect and Multiplicative Model (AMMI Model) nowadays is used to asses in plant breeding, especially to asses the Genotype í— Environment Interaction (GEI) on multi-environment trial. The presence of genotype í— environment interaction (GEI) creates difficulties in modeling complex trait that involve sequence biological process. Coupling Structural equation modeling with AMMI was developed to analyzed genotype í— environment interaction (GEI). Structural equation modeling allows us to account for underlying sequential process in plant development by incorporating intermediate variables associated with those processes in the model. With this method we can incorporating genotypic and environmental covariate in the model and explain how those covariates influence grain yield. SEM-AMMI useful when both environments and genotype are fixed and the purpose of the multi-environment trials (MET) is to assess the combined effect genotypic and environmental covariate on yield and yield components  Keywords : AMMI Model, Structural equation modeling 
Spatial Analysis of Dengue Disease in Jakarta Province Sobari, Muhamad; Jaya, I Gede Nyoman Mindra; Ruchjana, Budi Nurani
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 7, No 4 (2023): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v7i4.17423

Abstract

Dengue disease is a virus-borne illness spread by the bite of the female Aedes aegypti mosquito. Jakarta Province has a vulnerability to dengue disease due to high population density and percentage of urban slum households. This study applied a spatial autoregressive (SAR) model to identify the risk factors that affect the number of dengue disease cases in Jakarta Province. The spatial dependency was accounted for using the queen contiguity spatial weight matrix. The number of flood-prone points, the number of slum neighborhood associations, the population density, the number of hospitals and the number of public health centers per 1,000 population and spatial lag significantly impact the number of dengue disease cases in Jakarta Province. When dengue disease cases increase in one sub-district, the number of dengue disease cases in the sub-districts around it will increase as well because of the positive and significant spatial lag coefficient. Based on the direct impact, each addition of one percent of flood-prone points in one sub-district will increase the number of dengue disease cases in that sub-district by 3.86 cases
Bayesian Spasial Varying Coeffcient Model dalam Menaksir Resiko Relatif Penyakit Diare di Kota Bandung Jaya, I Gede Nyoman Mindra; Tantular, Bertho; Zulhanif, Z
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2017: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (760.679 KB)

Abstract

Penyakit diare masih merupakan masalah kesehatan di Indonesia khususnya di Kota Bandung. karena morbiditas dan mortalitas-nya yang masih tinggi. Pemodelan regresi Poisson Global dinilai kurang tepat digunakan dalam memodelkan data diare yang memiliki karakteristik spasial yang meliputi ketergantungan spasial dan heterogenitas spatial. Model yang diuslkan adalah model Bayesian Spatial Varying Coefficient Model (SVCM) sebagi pendekatan untuk mensolusikan adanya pelanggaran asumsi karena adanya karakteristik spatial. Hasil analisis menemukan bahwa pemodelan regresi poisson kurang tepat digunakan untuk memodelkan angka kasus diare di Kota Bandung dikarenakan adanya pelanggaran asumsi homoskedastisitas. Pemodelan SVCM menyimpulkan menginformasikan adanya efek spatial yang berbeda untuk setiap kecamatan di Kota Bandung sehingga memberikan informasi yang lebih lengkap bagaimana kontribusi dari masing-masing variabel Kepadatan Penduduk, PHBS, Rumah Sehat, Gizi Buruk dan Air Bersih berpengaruh terhadap angka kasus diare di masing-masing kecamatan di Kota Bandung
Klasifikasi Curah Hujan Berdasarkan Data Satelit Mtstat dengan Metode Bayesian Zulhanif, Z; Jaya, I Gede Nyoman Mindra
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2017: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (238.124 KB)

Abstract

Metode Bayesian Klasifikasi merupakan metode pengklasifikasian probabilistik berdasarkan berdasarkan teorema Bayes. Metode ini mengasumsikan bahwa keberadaan (atau ketidaberadaan) dari atribut tertentu dari suatu kelas adalah tidak terkait dengan keberadaan (atau ketidaberadaan) dari setiap atribut lain baik pada kelas yang sama maupun yang berbeda. Klasifikasi Bayes menganggap semua atribut berkontribusi secara independent untuk mengklasisfikasikan suatu pengamatan kedalam suatu kelas tertentu. Pada makalah ini akan diterapkan motode bayes dalam pengklasifikasikan curah hujan berdasarkan intesitas curah hujan adapun hasil penelitian ini adalah model klasifikasi curah hujan yang akan dipergunakan dalam memprediksi klasifikasi curah hujan.
Co-Authors . Sudartianto . Zulhanif Ahmad Ansori Matjjik Al Madani, Aulia R. Angga, Anggisti Nurdinda Chaerany Putri Arisanti, Restu Auliyazhafira, Shabira A. Azka Larissa Rahayu Bertho Tantular Bertho Tantular Budi Nurani Ruchjana Falah, Annisa N. Fauziah, Nisa Gumgum Darmawan Hasrat Ifolala Zebua I Made Sumertajaya Ijonu, Ukhti Nurfajriah Sasmita Imanudin Nurhuda Lia Faridah Muhamad Sobari Nauli, Theresia S. Neneng Sunengsih Neneng Sunengsih Putri, Fariza A. Sobari, Muhamad Zulhanif Zulhanif Zulhanif, Z Zulhanif, Zulhanif