Spatial Modeling Weibull-3 Survival Parameters with Frailty Distributed Conditionally Autoregressive (CAR)

Mahmudah, Nur; Pramoedyo, Henny

doi:10.21776/ub.natural-b.2015.003.01.12

Cited by 4 publications

(3 citation statements)

References 1 publication

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“…Metode tersebut diharapkan mampu memodelkan jumlah kasus setiap wilayah di Kecamatan Bojonegoro (Mahmudah & Anggraeni, 2021). Pertimbangan menggunakan pengaruh spasial atau letak geografis suatu daerah diharapkan mampu menganalisis pola hubungan spasial yang dapat diamati (Mahmudah & Pramoedyo, 2015).…”

Section: Pendahuluanunclassified

Implementasi Geographically Weighted Logistic Regression Pada Laju Pertumbuhan Penduduk Di Bojonegoro

Mahmudah,

Khoiriyah

2023

SCI TECH ED MATH

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Population Growth Rate is the rate at which influencing factors increase and decrease population size. The development of large populations in regional governments causes uncontrolled population growth rates. The population growth rate in Bojonegoro Regency from 2019 to 2020 experienced a significant increase of 0.96%. The increase in population has an impact on the emergence of various problems in the economic and social fields. This problem requires effective and comprehensive spatial modeling, namely Geographically Weighted Logistic Regression (GWLR) with Fixed Gaussian and Adaptive Gaussian weighting. GWLR modeling aims to determine the implementation of knowledge and insight into the factors that influence the rate of population growth in each area of Bojonegoro District. based on the results of GWLR modeling with the Akaike Index Criteria (AIC) on the Fixed Gaussian kernel function of 33.91. This value identifies that the population growth rate modeling in each sub-district has different values. This difference can be seen from 6 sub-districts which are significantly influenced by the number of births and 5 sub-districts are significantly influenced by the number of couples of childbearing age who participate in family planning. The results of modeling predictions (GWLR) in Bojonegoro Regency show that 11 sub-districts have low growth rate categories while 17 sub-districts have high population growth rate values

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Section: Pendahuluanunclassified

Implementasi Geographically Weighted Logistic Regression Pada Laju Pertumbuhan Penduduk Di Bojonegoro

Mahmudah,

Khoiriyah

2023

SCI TECH ED MATH

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“…Bayesian is an alternative method to estimate model parameter [21]. The availability of program package for Bayesian analysis makes this method more flexible to create a modelling analysis that is stochastically complex [22].…”

Section: Bayesian Ordinal Logistic Regression Link Function Modelmentioning

confidence: 99%

On Computational Bayesian Ordinal Logistic Regression Link Function in Cases of Cervical Cancer in Tuban

Mahmudah

Anggraini

2022

BAREKENG: J. Math. & App.

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Cervical cancer is the most common cancer that causes death in women. This cancer is mainly caused by Human Papilloma Virus (HPV). It is estimated that 52 million of Indonesian women are at risk of having cancer, and 36% of female cancer patients suffer from cervical cancer. This type of cancer cannot be diagnosed immediately as there is several years of pre-malignancy phase; thus, early detection or screening is needed to prevent it from turning into malignant. Pap test as a screening program can detect cancer, precancer, and normal condition. To understand the predicting factors of the test results, a comprehensive mathematical modelling was created using the link function of Bayesian Ordinal Logistic Regression. This study observed several possible factors that may affect Pap test results in Tuban regency, namely Age (X1), Education (X2), Childbirth Experience (X3), Use of Contraceptives (X4), Menstrual Cycle (X5), Age of First Menstruation (X6), History of Miscarriage (X7), Anemia (X8) and Number of Sexual Partners (X9) . The outcomes indicated that the predicting factors of Pap cervical cancer results are Age (X1), Education (X2), Childbirth Experience (X3), Use of Contraceptives (X4), Menstrual Cycle (X5), and Anemia (X8). In this model, there is an inexplainable error dependency as indicated by the varied constance values of alpha.

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“…Pada penelitian ini distribusi variabel lama rawat inap pasien kanker serviks mengikuti distribusi normal (𝜇 dan 𝜎 2 ) [12]. Dengan demikian fungsi kepekatan peluang sebagai berikut [13]:…”

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Aplikasi Komputasi Bayesian Regresi Dummy Pada Kasus Kanker Serviks di Kabupaten Tuban

Mahmudah

Yuwita

2022

JaMES

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Kanker serviks adalah kanker yang paling banyak diderita oleh wanita yang menjadi penyebab kematian. Penyebab utama kanker serviks adalah infeksi Human Papilloma Virus (HPV). Kanker serviks merupakan penyakit yang disebabkan oleh pertumbuhan sel- sel jaringan tubuh yang tidak normal di dalam leher rahim/ serviks yang terdapat dalam organ bagian reproduksi pada tubuh wanita dan menyebabkan kematian. Untuk mencegah munculnya fase ganas dibutuhkan program screening pada lama rawat inap pasien kanker serviks. Penelitian ini bertujuan untuk mengetahui faktor-faktor yang mempengaruhi lama rawat inap pasien kanker serviks di Kabupaten Tuban dengan metode komputasi Bayesian Regresi Dummy. Metode Bayesian adalah salah satu teknik komputasi pada estimasi parameter yang menggabungkan fungsi likelihood dan distribusi prior menjadi distribusi posterior dalam menduga parameter model. Bayesian regresi dummy menghasilkan suatu variabel yang memiliki pengaruh signifikan terhadap lama rawat inap pasien kanker serviks, yaitu variabel Komplikasi (X1). Dengan nilai alpha 2.17, menunjukan bahwa terdapat dependensi/error yang tidak bisa dijelaskan dalam model regresi dummy pada kasus lama rawat inap kanker serviks di Kabupaten Tuban.

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Spatial Modeling Weibull-3 Survival Parameters with Frailty Distributed Conditionally Autoregressive (CAR)

Cited by 4 publications

References 1 publication

Implementasi Geographically Weighted Logistic Regression Pada Laju Pertumbuhan Penduduk Di Bojonegoro

Implementasi Geographically Weighted Logistic Regression Pada Laju Pertumbuhan Penduduk Di Bojonegoro

On Computational Bayesian Ordinal Logistic Regression Link Function in Cases of Cervical Cancer in Tuban

Aplikasi Komputasi Bayesian Regresi Dummy Pada Kasus Kanker Serviks di Kabupaten Tuban

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