SELEKSI FITUR INFORMATION GAIN PADA ANALISIS SENTIMEN TERHADAP ULASAN APLIKASI FLIP DENGAN ALGORITMA SUPPORT VECTOR MACHINE

Isma'il Muhammad Falih, . (2022) SELEKSI FITUR INFORMATION GAIN PADA ANALISIS SENTIMEN TERHADAP ULASAN APLIKASI FLIP DENGAN ALGORITMA SUPPORT VECTOR MACHINE. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

[img] Text
ABSTRAK.pdf

Download (108kB)
[img] Text
AWAL.pdf

Download (667kB)
[img] Text
BAB 1.pdf

Download (248kB)
[img] Text
BAB 2.pdf
Restricted to Repository UPNVJ Only

Download (556kB)
[img] Text
BAB 3.pdf
Restricted to Repository UPNVJ Only

Download (319kB)
[img] Text
BAB 4.pdf
Restricted to Repository UPNVJ Only

Download (1MB)
[img] Text
BAB 5.pdf

Download (232kB)
[img] Text
DAFTAR PUSTAKA.pdf

Download (238kB)
[img] Text
RIWAYAT HIDUP.pdf
Restricted to Repository UPNVJ Only

Download (135kB)
[img] Text
LAMPIRAN.pdf
Restricted to Repository UPNVJ Only

Download (1MB)
[img] Text
HASIL PLAGIARISME.pdf
Restricted to Repository staff only

Download (15MB)
[img] Text
ARTIKEL KI.pdf
Restricted to Repository staff only

Download (854kB)

Abstract

Flip is an application to make transfers between different banks at no cost and provides services for buying credit and data packages that generally work as a bridge for transactions between different banks. Reviews given from Flip application users contain many constructive and critical opinions that can be used as input for Flip application developers. The purpose of this study is to build a sentiment classification model using the Support Vector Machine method and the Information Gain feature selection method for the Flip application review on Google Play services. In this study, the reviews will be divided into two categories that is positive and negative based on manual labeling by 3 assessors, which is then carried out preprocessing, feature selection, and split data of 80% data train and 20% data test before modeling. There are two models, namely a model without feature selection (SVM model) and a model with feature selection (SVM-IG model). The evaluation results show an accuracy is 91.97%, precision is 95.53%, recall is 91.45%, and AUC is 0.9215 for the SVM model, while for the SVM-IG model the accuracy is 96.25%, precision is 99.10%, recall is 94.87%, and AUC is 0.9672.

Item Type: Thesis (Skripsi)
Additional Information: [No.Panggil: 1810511040] [Pembimbing 1: Nurhafifah Matondang] [Pembimbing 2: Nurul Chamidah] [Penguji 1: Widya Cholil] [Penguji 2: Henki Bayu Seta]
Uncontrolled Keywords: Sentiment Analysis, Support Vector Machine, Feature Selection, Information Gain, Flip.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
Divisions: Fakultas Ilmu Komputer > Program Studi Informatika (S1)
Depositing User: Isma`Il Muhammad Falih
Date Deposited: 27 Jul 2022 05:52
Last Modified: 01 Aug 2022 06:40
URI: http://repository.upnvj.ac.id/id/eprint/19757

Actions (login required)

View Item View Item