| dc.contributor.advisor | Sitompul, Opim Salim | |
| dc.contributor.advisor | Amalia | |
| dc.contributor.author | Senda, Tito Afwi | |
| dc.date.accessioned | 2024-09-06T09:21:32Z | |
| dc.date.available | 2024-09-06T09:21:32Z | |
| dc.date.issued | 2024 | |
| dc.identifier.uri | https://repositori.usu.ac.id/handle/123456789/96947 | |
| dc.description.abstract | This research discusses sentiment classification using the K-Nearest Neighbor (K-NN) algorithm with the RANDOM SEARCH CROSS VALIDATION (RSCV) approach. This method aims to classify ChatGPT app user reviews into three categories: positive, negative, and neutral. In this study, review data is collected from the Play Store and text preprocessing is performed. Furthermore, the K-NN model will use RSCV in determining parameter values. The evaluation results show that the K-NN model with the best parameters can classify the sentiment of reviews with good accuracy. | en_US |
| dc.language.iso | id | en_US |
| dc.publisher | Universitas Sumatera Utara | en_US |
| dc.subject | Sentiment classification | en_US |
| dc.subject | ChatGPT | en_US |
| dc.subject | K-NN | en_US |
| dc.subject | Natural Language Processing (NLP) | en_US |
| dc.subject | RANDOM SEARCH CROSS VALIDATION (RSCV) | en_US |
| dc.subject | SDGs | en_US |
| dc.title | Klasifikasi Sentimen Menggunakan Algoritma K-NN Berdasarkan Pendekatan Random Search Cross Validation | en_US |
| dc.title.alternative | Sentiment Classification Using K-NN Algorithm Based on Random Search Cross Validation Approach | en_US |
| dc.type | Thesis | en_US |
| dc.identifier.nim | NIM217038016 | |
| dc.identifier.nidn | NIDN0017086108 | |
| dc.identifier.nidn | NIDN0121127801 | |
| dc.identifier.kodeprodi | KODEPRODI55101#Teknik Informatika | |
| dc.description.pages | 82 Pages | en_US |
| dc.description.type | Tesis Magister | en_US |
