Aria Nanda Herdiawan, . (2023) PERBANDINGAN ALGORITMA K-NEAREST NEIGHBOR, RANDOM FOREST, DAN LOGISTIC REGRESSION TERHADAP KATEGORI REVIEW PADA MARKETPLACE XYZ. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
![[img]](http://repository.upnvj.ac.id/style/images/fileicons/text.png) |
Text
AWAL.pdf Download (627kB) |
|
Text
ABSTRAK.pdf Download (9kB) |
|
Text
BAB 1.pdf Restricted to Repository UPNVJ Only Download (32kB) |
|
Text
BAB 2.pdf Restricted to Repository UPNVJ Only Download (259kB) |
|
Text
BAB 3.pdf Restricted to Repository UPNVJ Only Download (310kB) |
|
Text
BAB 4.pdf Restricted to Repository UPNVJ Only Download (1MB) |
|
Text
BAB 5.pdf Download (93kB) |
|
Text
DAFTAR PUSTAKA.pdf Download (158kB) |
|
Text
RIWAYAT HIDUP.pdf Restricted to Repository UPNVJ Only Download (56kB) |
|
Text
LAMPIRAN.pdf Restricted to Repository UPNVJ Only Download (1MB) |
|
Text
HASIL PLAGIARISME.pdf Restricted to Repository staff only Download (781kB) |
|
Text
ARTIKEL KI.pdf Restricted to Repository staff only Download (514kB) |
Abstract
With the rapid development of technology in the world, it can make doing everything easier. One of them is shopping online. One platform that can be used for online shopping is a marketplace. Many reviews are given by buyers when completing transactions in marketplace. So classification is needed to distinguish categories of buyer reviews, besides that we can know the difference performance of the K-nearest neighbor, random forest, and logistic regression classification algorithms. By using review data of 1000 data which is divided into product classes 618 data, delivery 214 data, and service 168 data. The data was preprocessed using case folding, cleansing, normalization, tokenization, filtering, and stemming stages. Then the word weights in the data are calculated using tf-idf. The data will be divided into train data and test data. After that, classification was carried out with K-nearest neighbor, random forest, and logistic regression. Which produces the highest accuracy of K-nearest neighbor 76%, random forest 81%, and logistic regression 80%.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 1910511083] [Pembimbing: Jayanta] [Penguji 1: Theresia Wati] [Penguji 2: Anita Muliawati] |
| Uncontrolled Keywords: | Review, Marketplace, Text Classification, K-Nearest Neighbor, Random Forest, Logistic Regression |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | Aria Nanda Herdiawan |
| Date Deposited: | 21 Feb 2024 06:53 |
| Last Modified: | 21 Feb 2024 06:53 |
| URI: | http://repository.upnvj.ac.id/id/eprint/28959 |
Actions (login required)
 |
View Item |