Analisis Sentimen Tanggapan Pengguna Media Sosial X Terhadap Program Beasiswa KIP-Kuliah dengan Menggunakan Algoritma Support Vector Machine (SVM)

The KIP-Kuliah Scholarship is an Indonesian government program which aims to provide access to higher education for students from underprivileged families. This program has become a hot topic of discussion on social media, including social media. The object of research is comments on X's social media regarding the KIP-College Scholarship. Research methods include crawling data using google collabs, data preprocessing, Support Vector Machine model training, and model evaluation using RapidMiner. The research results show that the Support Vector Machine model is able to classify sentiment with an accuracy of 86.27%, but there is a bias towards negative sentiment. The majority of public responses are negative, often regarding misuse of scholarships. The suggestions given include collecting more balanced data, using dataset balancing techniques, implementing more complex models, and more in-depth evaluation to improve model performance. It is hoped that this research will provide input for the government in improving the KIP-College Scholarship distribution mechanism so that it is more targeted and reduces the potential for abuse.

Authors

Publication Details

Journal
Jurnal Indonesia Manajemen Informatika dan Komunikasi
Published
2024-09-15
DOI
https://doi.org/10.35870/jimik.v5i3.990
Citations
1
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.78
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article

Analisis Sentimen Tanggapan Pengguna Media Sosial X Terhadap Program Beasiswa KIP-Kuliah dengan Menggunakan Algoritma Support Vector Machine (SVM)

Tundo Tundo, Frencis Matheos Sarimole, Sugiyono Sugiyono, Ika Amelia
1 citations
Jurnal Indonesia Manajemen Informatika dan Komunikasi
Data Mining and Machine Learning Applications
0.78
article

Analisis Sentimen Tanggapan Pengguna Media Sosial X Terhadap Program Beasiswa KIP-Kuliah dengan Menggunakan Algoritma Support Vector Machine (SVM)

Tundo Tundo, Frencis Matheos Sarimole, Sugiyono Sugiyono, Ika Amelia
article en
1 citations

Abstract

The KIP-Kuliah Scholarship is an Indonesian government program which aims to provide access to higher education for students from underprivileged families. This program has become a hot topic of discussion on social media, including social media. The object of research is comments on X's social media regarding the KIP-College Scholarship. Research methods include crawling data using google collabs, data preprocessing, Support Vector Machine model training, and model evaluation using RapidMiner. The research results show that the Support Vector Machine model is able to classify sentiment with an accuracy of 86.27%, but there is a bias towards negative sentiment. The majority of public responses are negative, often regarding misuse of scholarships. The suggestions given include collecting more balanced data, using dataset balancing techniques, implementing more complex models, and more in-depth evaluation to improve model performance. It is hoped that this research will provide input for the government in improving the KIP-College Scholarship distribution mechanism so that it is more targeted and reduces the potential for abuse.

Jurnal Indonesia Manajemen Informatika dan KomunikasiVol. 5(3)
Openalex Percentile: Top 21%
Data Mining and Machine Learning Applications
0.78
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Analisis Sentimen Tanggapan Pengguna Media Sosial X Terhadap Program Beasiswa KIP-Kuliah dengan Menggunakan Algoritma Support Vector Machine (SVM) — Tundo Tundo, Frencis Matheos Sarimole, et al. · Jurnal Indonesia Manajemen Informatika dan Komunikasi (2024) | TGRS Research Map | TGRS