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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Journal of Applied Data Sciences
Nurjannah Nurjannah
Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Mathematics

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Bayesian Approach in Estimating Parameters of Zero-Inflated Negative Binomial Regression Model Using Cauchy Prior: Simulation Study on Pneumonia Santi Wahyu Salsabila; Achmad Efendi; Nurjannah Nurjannah
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.33245

Abstract

The Bayesian approach is one of the parameter estimation methods that can be applied to Zero-Inflated Negative Binomial (ZINB) regression analysis. The ZINB regression model is used to analyze over-dispersion data with excess zeros. This study aims to evaluate the performance of ZINB regression parameter estimation using a Bayesian approach with Cauchy prior in pneumonia studies. The analysis is applied to secondary data as well as to simulated data with various scenarios based on different sample sizes and proportions of zero values such that the optimal model can be determined. The results show that ZINB regression models using the Bayesian approach provide stable parameter estimates as sample sizes and proportions of zeros increase. In cases of under-five deaths due to pneumonia, the data often contains many zeros because not all regions report cases. The ZINB model effectively addresses over-dispersion and excess zeros through a combination of negative binomial and zero-inflation models. This provides more accurate modeling results to support policymaking. The Bayesian approach also provides flexibility in integrating prior information and handling small samples, making the ZINB model well suited for health data with rare events and many zeros.
Using Machine Learning Approach to Cluster Marine Environmental Features of Lesser Sunda Island Evellin Dewi Lusiana; Suci Astutik; Nurjannah Nurjannah; Abu Bakar Sambah
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.478

Abstract

Mapping marine ecosystems is acknowledged as a vital tool for implementing ecosystem services in practical situations. It provides a framework for effective marine spatial planning, enabling the designation of marine protected areas (MPAs) that consider ecological connectivity and habitat requirements. It also helps pinpoint areas of high biodiversity or ecological significance, allowing conservationists to prioritize these regions for protection and management. Numerous studies over decades have produced a vast amount of data that illustrates the features of the marine ecosystem. Therefore, the unsupervised learning is a promising technique to map marine ecosystem based on its environmental features. This study aims to compare unsupervised learning techniques to analyze marine environmental features in order to map marine ecosystem in Lesser Sunda waters. Eleven global environmental variables were accessed from global databases. The Lesser Sunda waters were delineated into groups according to their environmental characteristics using four unsupervised learning techniques: k-mean, fuzzy c-mean, self-organizing map (SOM), and density-based spatial clustering of applications with noise (DBSCAN). According to the findings, the Lesser Sunda waters can be divided into five to nine clusters, each with distinct environmental features. Moreover, the fuzzy c-mean method's clustering result outperformed the others based on the highest Silhouette (0.2204478) and Calinski-Harabasz (1741.099) Index. As an unsupervised learning technique, fuzzy c-mean clustering offered good performance in delineating Lesser Sunda Island marine waters with five clusters. The clustering results mostly consistent with existing conservation programs, even though there are several areas which needed international and multinational organization collaboration to effectively accomplish marine conservation objectives.
Co-Authors Abu Bakar Sambah Achmad Efendi Evellin Dewi Lusiana Santi Wahyu Salsabila Suci Astutik