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All Journal Algoritme Jurnal Mahasiswa Teknik Informatika JUSIFOR : Jurnal Sistem Informasi dan Informatika
Hasanati, Nida'ul
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science

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Bibliometrik Hate Speech: Tren Metode Penelitian dan Domain Implementasi Nurindah, Arrisa Aprilani; Hasanati, Nida'ul; Aini, Qurrotul
JUSIFOR : Jurnal Sistem Informasi dan Informatika Vol 4 No 2 (2025): JUSIFOR - Desember 2025
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/jusifor.v4i2.8652

Abstract

This study aims to map the development of research related to hate speech through a bibliometric analysis of scientific publications indexed in Scopus. Using the keywords “hate speech” and “analysis,” a total of 2,009 publication metadata were obtained and analyzed using R Studio, Biblioshiny, and VOSviewer. The results indicate a significant increase in the number of publications, particularly during the 2021–2024 period, reflecting the growing academic attention toward hate speech issues. Domain analysis reveals that research is predominantly focused on the fields of Technology and Social Sciences, especially in the context of automated detection, social media, and the impact of digital society. Deep learning–based methods such as BERT and LSTM are the most frequently used techniques, in line with recent trends in Natural Language Processing (NLP). Furthermore, the co-occurrence analysis reveals the formation of several thematic clusters, including artificial intelligence, deep learning, multilingual hate speech, and large language models.
Popularitas dan Tren Metode Pemodelan Expected Goals (xG): Sebuah Analisis Bibliometrik Akbar, Fadhil Raihan; Aini, Qurrotul; Hasanati, Nida'ul
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 2 (2026): April 2026 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v6i2.13402

Abstract

Data analysis has fundamentally altered contemporary football performance assessment, with Expected Goals (xG) emerging as a key metric. Despite its widespread use, comprehensive documentation of its modeling methodologies remains scarce. This study aims to map the global xG research landscape using bibliometric analysis. Scopus data was analyzed using VOSviewer to identify trends, prevalent methodologies, and implementation domains. Findings indicate a significant rise in publications from 2020 to 2025, signaling growing scholarly interest. Random Forest is identified as the most widely used technique, though recent trends suggest a resurgence of Logistic Regression. The primary application domain is Performance Analysis, followed by Tactical & Strategic Analysis. Keyword analysis reveals three main clusters: machine learning, regression models, and deep learning. It is concluded that current xG research trends are moving towards a balance between algorithmic complexity and model interpretability, while expanding beyond mere shot evaluation to more holistic performance metrics.
Co-Authors Akbar, Fadhil Raihan Nurindah, Arrisa Aprilani Qurrotul Aini