Skripsi

ANALISIS SENTIMEN PADA APLIKASI JMO MOBILE DALAM PERBANDINGAN KINERJA SUPPORT VECTOR MACHINE DAN RANDOM FOREST

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JMO Mobile is a digital service application that enables the public to access employment-related information and benefits. User reviews serve as a valuable resource for evaluating service quality, yet systematic sentiment analysis on this application remains limited. This study aims to classify the sentiment of user reviews and compare the performance of Support Vector Machine (SVM) and Random Forest (RF) algorithms. A total of 41,673 reviews were collected through web scraping, then preprocessed through text cleaning, tokenization, stopword removal, stemming, and feature extraction using TF-IDF. The reviews were categorized into positive, negative, and neutral sentiments, and divided into training and testing datasets with an 80:20 ratio. The choice of SVM and RF was based on their proven effectiveness in text classification tasks, with SVM excelling in handling high-dimensional data and RF recognized for its stability in producing reliable results. Model evaluation was conducted using accuracy as the primary metric. The findings indicate that Random Forest achieved an accuracy of 86.15 percent, slightly outperforming SVM at 86.06 percent. While SVM showed superior performance in identifying positive sentiment, Random Forest demonstrated greater consistency across classifications. Overall, Random Forest is considered more suitable for sentiment analysis of public service application reviews. This study contributes an automated approach to understanding user perceptions and offers a reference for selecting classification algorithms in similar cases.

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Detail Information

Series Title

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Call Number

T1895382025

Publisher

Indralaya : Prodi Sistem Informasi, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xv, 93 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

006.350 7

Content Type

Text

Media Type

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Carrier Type

-

Edition

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Subject(s)

Prodi Sistem Informasi
Analisis Sintimen--Analisis Data

Specific Detail Info

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Statement of Responsibility

MI

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