| dc.contributor.advisor | Sutarman | |
| dc.contributor.author | Sya’ban, Muhammad Yandi Putra El | |
| dc.date.accessioned | 2025-01-10T02:30:02Z | |
| dc.date.available | 2025-01-10T02:30:02Z | |
| dc.date.issued | 2024 | |
| dc.identifier.uri | https://repositori.usu.ac.id/handle/123456789/99997 | |
| dc.description.abstract | In multinomial logistic regression parameter estimation using maximum
likelihood estimation, numerical approach with optimization method is used to get the
best estimated parameters of the mentioned model. The optimization method of
stochastic gradient descent (SGD) and the quasi-hyperbolic momentum (QHM)
variant are going to be studied. Based on the dataset that has been studied, it has been
found that the accuracy of classification using SGD with QHM is higher than using
plain SGD. | en_US |
| dc.language.iso | id | en_US |
| dc.publisher | Universitas Sumatera Utara | en_US |
| dc.subject | Large Dataset | en_US |
| dc.subject | Maximum Likelihood | en_US |
| dc.subject | Multinomial Logistic Regression | en_US |
| dc.subject | Quasi-Hyperbolic Momentum | en_US |
| dc.subject | Stochastic Gradient Descent | en_US |
| dc.title | Penaksiran Parameter Model Regresi Logistik Multinomial Menggunakan Metode Optimasi Stochastic Gradient Descent dengan Quasihyperbolic Momentum pada Dataset Besar | en_US |
| dc.title.alternative | Parameter Estimation of Multinomial Logistic Regression Using Stochastic Gradient Descent Optimization Method with Quasi-Hyperbolic Momentum on Large Dataset | en_US |
| dc.type | Thesis | en_US |
| dc.identifier.nim | NIM190803099 | |
| dc.identifier.nidn | NIDN0026106305 | |
| dc.identifier.kodeprodi | KODEPRODI44201#Matematika | |
| dc.description.pages | 54 Pages | en_US |
| dc.description.type | Skripsi Sarjana | en_US |
| dc.subject.sdgs | SDGs 4. Quality Education | en_US |
