Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jambura Journal of Mathematics International Journal of Quantitative Research and Modeling
Nisa, Nabilla Rida Tri
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science Mathematics Physics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Comparison of Poisson and Negative Binomial Regression Models in Identifying Factors Influencing Covid-19 Deaths in Indonesia. Nisa, Nabilla Rida Tri; Amanatullah Pandu Zenklinov; Husna Afanyn Khoirunissa; Nur Rezky Safitriani; Erlyne Nadhilah Widyaningrum; Rizka Amalia Putri; Morina A. Fathan
International Journal of Quantitative Research and Modeling Vol. 6 No. 4 (2025): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i4.1126

Abstract

This research compares Poisson Regression and Generalized Negative Binomial (GNB) Regression to underscore the factors that influence the growth of COVID-19 deaths in Indonesia. Count data such as mortality cases often violates the Poisson assumption of equidispersion (null mean equals variance) causing overdispersion. The GNB model is suggested as a remedy for overdispersed data crime prevention has become increasingly necessary for systematic development because secondary data from the Indonesian government has included dependable variables such as mortality rates for people aged over 60, diabetes mellitus, heart disease, lung disease, healthcare worker percentages, referral hospitals, and the population. The Poisson Regression reported R² of 87.67% and experienced overdispersion (θ₁ = 356.27, θ₂ = 417,597). The GNB model, in contrast, with a lower AIC (499.5566), overtook Poisson. Important factors that had significant impact on both models were mortality rates for individuals over 60, diabetes mellitus, healthcare workers, and referral hospitals, whereas heart and lung disease mortality rates were the ones that were not material. The GNB model had a better fit and tackled the issues of overdispersion in the Poisson Regression.
Comparative Analysis of Hierarchical Cluster Methods in Inflationary Cities in Indonesia Based on Sectoral Inflation Patterns Khoirunissa, Husna Afanyn; Safitriani, Nur Rezky; Widyaningrum, Erlyne Nadhilah; Putri, Rizka Amalia; Fathan, Morina A.; Nisa, Nabilla Rida Tri
Jambura Journal of Mathematics Vol 8, No 1: February 2026
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v8i1.35105

Abstract

This study aims to assess the performance of single linkage, complete linkage, and average linkage hierarchical clustering algorithms in grouping cities used as inflation benchmarks in Indonesia into clusters based on sectoral inflation patterns. The data utilized are 150 regencies/cities divided into 11 sectors that drive inflation, identified by BPS Indonesia. Prior to clustering, a distance analysis using Euclidean distances was conducted to measure similarity between regions. Evaluation of the optimal number of clusters was conducted by applying the stability measure approach (APN, AD, ADM, and FOM), which showed that creating five clusters produced the most stable results. The results of the analysis revealed that the single linkage approach had the lowest within-cluster to between-cluster standard deviation ratio compared to the other two approaches, which revealed a greater level of homogeneity between the clusters. From an economic perspective, this clustering pattern revealed impressive differences in sectoral inflation pressures between provinces, even between cities within a province. Consequently, the single linkage method is proposed as the optimal method for identifying spatial variations in sectoral inflation in Indonesia.
Co-Authors Amanatullah Pandu Zenklinov Erlyne Nadhilah Widyaningrum Fathan, Morina A. Husna Afanyn Khoirunissa Husna Afanyn Khoirunissa Morina A. Fathan Nur Rezky Safitriani Putri, Rizka Amalia Rizka Amalia Putri Safitriani, Nur Rezky