Skripsi

ANALISIS SENTIMEN PUBLIK TERHADAP PROGRAM KIP-KULIAH MENGGUNAKAN ALGORITMA RANDOM FOREST PADA MEDIA SOSIAL X

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Penilaian

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KIP-K program or The Indonesia Smart College Card is a funding assistance from the government for students with economic difficulties who want to experience educational opportunities in higher education. This program can’t be separated from public discussion, especially regarding the issue of misuse of funds by recipients, inconsistency in fund disbursement and falsification of registration files. These problems make the public view that the KIP-K program is often still misdirected. The research aims to examine public sentiment or perception towards the KIP-K program using Random Forest algorithm combined with Word2Vec as a word weighting technique and Random Oversampling (ROS) as a balancing technique to overcome data imbalance. The dataset obtained comes via platform X or Twitter) a total of 4423 tweets with the keywords “kip-k” or “kipk” and with a vulnerable time during 2024. The model’s performance demonstrated a high accuracy of 96,57%, precision, recall, and f1-score at the same value of 97%. The results indicate that the model is effective in analyzing sentiment accurately and maintaining a balanced performance between the two sentiment classes. Based on research in this study, the Random Forest algorithm combined with Word2Vec and Random Oversampling (ROS) can produce high accuracy and can overcome data imbalance.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507003750	T177017	T1770172025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1770172025

Publisher

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

Collation

xviii, 78 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

005.360 7

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Sistem informasi

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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