| dc.contributor.advisor | Syahputra, Muhammad Romi | |
| dc.contributor.author | Tambunan, Saputri Melisa | |
| dc.date.accessioned | 2024-08-30T08:54:23Z | |
| dc.date.available | 2024-08-30T08:54:23Z | |
| dc.date.issued | 2024 | |
| dc.identifier.uri | https://repositori.usu.ac.id/handle/123456789/96474 | |
| dc.description.abstract | The study aims to compare Generalized Poisson Regression (GPR) and Negative
Binomial Regression (NBR) in modeling rainfall amounts based on
meteorological factors in South Sumatra from 2021 to 2023, calculated from
January to December. Meteorological data such as air temperature, humidity,
wind speed, and air pressure were collected for analysis. The analysis was
conducted using R Studio. The results indicate that GPR was used to address
overdispersion, with significant variables being X1 and X1 , while NBR showed
significant influence from variables X1, X1, and X1 on rainfall amounts. The NBR
model provided the best fit with the lowest AIC value (427.97). | en_US |
| dc.language.iso | id | en_US |
| dc.publisher | Universitas Sumatera Utara | en_US |
| dc.subject | Generalized Poisson Regression | en_US |
| dc.subject | Negative Binomial Regression | en_US |
| dc.subject | rainfall | en_US |
| dc.subject | meteorological factors | en_US |
| dc.subject | South Sumatra | en_US |
| dc.subject | SDGs | en_US |
| dc.title | Komparasi Generalized Poisson Regression dan Negative Binomial Regression pada Pemodelan Jumlah Curah Hujan | en_US |
| dc.title.alternative | Comparison of Generalized Poisson Regression and Negative Binomial Regression in Modeling Rainfall | en_US |
| dc.type | Thesis | en_US |
| dc.identifier.nim | NIM212407011 | |
| dc.identifier.nidn | NIDN0115118903 | |
| dc.identifier.kodeprodi | KODEPRODI49401#Statistika | |
| dc.description.pages | 64 Pages | en_US |
| dc.description.type | Kertas Karya Diploma | en_US |
