Sentiment Analysis on SocialMedia Using TF-IDF Vectorization and H2O Gradient Boosting for Student Anxiety Detection

Ningsih, Maylinna Rahayu; Unjung, Jumanto

doi:10.15294/sji.v12i1.20582

Jurnal

Sentiment Analysis on SocialMedia Using TF-IDF Vectorization and H2O Gradient Boosting for Student Anxiety Detection

Scientific Journal of Informatics · Vol. 11 No. 4 (2025) · hlm. 1137–1144

DOI: 10.15294/sji.v12i1.20582
ISSN: 2407-7658, 2460-0040
Terbit: 2025-03-24
Penerbit: Universitas Negeri Semarang
Bahasa: eng

Abstrak

Purpose: Mental health issues are now a concern for many people. Anxiety or often called Anxiety that is excessive and prolonged has also become the forefront of various psychological disorders that trigger impacts such as stress to suicide. People using social media platforms tend to be a medium for expressing opinions sharing information and even expressing daily emotions. Many studies have shown a correlation between expressing emotional statements on social media and mental disorders. This research aims to conduct sentiment analysis of Anxiety on social media using H2O Gradient Boosting by implementing TF-IDF Vectorization which is set to max feature. Methods: This research utilizes 6980 post data from social media. The method applied is by conducting Exploratory Data Analysis then Data preprocessing, Tf-Idf Vectoriztion with max feature experiments 100, 250, 500, 1000 and 2000, H2O Gradient Boosting Model, Cross Validation, and Model performance evaluation. Result: The results of this study show good model performance through max feature TF-IDF = 250 with an accuracy value of 99%, Specificity 99.57%, and Eror Rate of 0.0106. Novelty: So that the use of the H2O Gradient Boosting model succeeded in providing good performance in classifying anxiety sentiment.

Kata kunci

Jaringan kolaborasi para penulis

12 penulis · 28 tautan kepenulisan bersama · 1 lintas organisasi

Arahkan kursor atau ketuk sebuah nama untuk menyorot rekan penulisnya. Klik dua kali untuk membuka halaman penulis.

Tun Hussein Onn University of Malaysia
Institut Teknologi Sepuluh Nopember
State University of Semarang
Afiliasi tidak diketahui

Organisasi berbeda
Organisasi sama
Afiliasi tidak diketahui
Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel

Tampilkan sebagai tabel

Rekan penulis	Organisasi	Artikel bersama
Much Aziz Muslim	Tun Hussein Onn University of Malaysia	3
M. Faris Al Hakim	Afiliasi tidak diketahui	2
Yopi Julia Nurriski	Afiliasi tidak diketahui	2
Fathimah Az Zahra Sanjani	Afiliasi tidak diketahui	2
Dimas Fanny Hebrasianto Permadi	Institut Teknologi Sepuluh Nopember	1
Annisaa Utami	Afiliasi tidak diketahui	1
Yesy Diah Rosita	Afiliasi tidak diketahui	1
Dwika Ananda Agustina Pertiwi	Afiliasi tidak diketahui	1
Kamilah Ahmad	Afiliasi tidak diketahui	1
Rofik Rofik	State University of Semarang	1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/sji/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.

Metadata asli (oai_dc)

<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Sentiment Analysis on SocialMedia Using TF-IDF Vectorization and H2O Gradient Boosting for Student Anxiety Detection</dc:title>
	<dc:creator xml:lang="en">Ningsih, Maylinna Rahayu </dc:creator>
	<dc:creator xml:lang="en">Unjung, Jumanto</dc:creator>
	<dc:subject xml:lang="en">Anxiety</dc:subject>
	<dc:subject xml:lang="en">H2O Gradient Boosting</dc:subject>
	<dc:subject xml:lang="en">Analysis Sentiment</dc:subject>
	<dc:subject xml:lang="en">TF-IDF Vectorization</dc:subject>
	<dc:description xml:lang="en">Purpose: Mental health issues are now a concern for many people. Anxiety or often called Anxiety that is excessive and prolonged has also become the forefront of various psychological disorders that trigger impacts such as stress to suicide. People using social media platforms tend to be a medium for expressing opinions sharing information and even expressing daily emotions. Many studies have shown a correlation between expressing emotional statements on social media and mental disorders. This research aims to conduct sentiment analysis of Anxiety on social media using H2O Gradient Boosting by implementing TF-IDF Vectorization which is set to max feature.
Methods: This research utilizes 6980 post data from social media. The method applied is by conducting Exploratory Data Analysis then Data preprocessing, Tf-Idf Vectoriztion with max feature experiments 100, 250, 500, 1000 and 2000, H2O Gradient Boosting Model, Cross Validation, and Model performance evaluation.
Result: The results of this study show good model performance through max feature TF-IDF = 250 with an accuracy value of 99%, Specificity 99.57%, and Eror Rate of 0.0106.
Novelty: So that the use of the H2O Gradient Boosting model succeeded in providing good performance in classifying anxiety sentiment.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2025-03-24</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/sji/article/view/20582</dc:identifier>
	<dc:identifier>10.15294/sji.v12i1.20582</dc:identifier>
	<dc:source xml:lang="en">Scientific Journal of Informatics; Vol. 11 No. 4: Special Issue; 1137-1144</dc:source>
	<dc:source>2407-7658</dc:source>
	<dc:source>2460-0040</dc:source>
	<dc:source>10.15294/sji.v11i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/sji/article/view/20582/3318</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Scientific Journal of Informatics</dc:rights>
</oai_dc:dc>