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All Journal Jurnal Syntax Transformation Jurnal INFOTEL
Hari Soetanto
Universitas Budi Luhur, Indonesia
Humanities Computer Science & IT Education Electrical & Electronics Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences

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Anomaly Detection in VPN (Virtual Private Network) Access Using Machine Learning Algorithms Ari Budi Prasetiyo; Hari Soetanto; Danang Harito Wibowo
Jurnal Syntax Transformation Vol 6 No 9 (2025): Jurnal Syntax Transformation
Publisher : CV. Syntax Corporation Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46799/jst.v6i9.1109

Abstract

This research discusses the physical and spatial characteristics of the Ahmad Djuhara Creative Space which is located in the Cirebon City State Building, West Java. In this research, the author uses a qualitative-descriptive research method by studying building characteristics with the architectural characteristics theory by N. John Habraken academically. The Ahmad Djuhara Creative Space building functions as a typical arts and culture workshop from Cirebon. From the study that has been carried out, it is concluded that what forms the physical and spatial character of the Ahmad Djuhara Creative Space building is the roof which has a repeating triangular shape, where the shape of the roof looks different from similar buildings.
Evaluating The Impact of Social Media Sentiment on University Enrollment Decisions Using Machine Learning Classifier Painem Painem; Hari Soetanto; Achmad Solichin; Anju A Nair
JURNAL INFOTEL Vol 18 No 1 (2026): February
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v18i1.1390

Abstract

Public sentiment expressed through social media is increasingly recognized as a potential factor influencing higher education enrollment decisions. This study investigates whether sentiments on Twitter regarding Universitas Budi Luhur correlate with the number of new student admissions. To achieve this, tweet data were collected and analyzed using four supervised machine learning algorithms—Support Vector Classifier (SVC), Naïve Bayes, K-Nearest Neighbor (KNN), and Logistic Regression (LR)—combined with two lexicon-based sentiment dictionaries: SentiWord and InSet. Experimental results demonstrate that the SentiWord-based approach consistently outperformed the InSet-based approach across all models, with the SVC-SentiWord combination achieving the highest F1-score of 0.86. Despite the strong performance of these models in classifying sentiment, correlation analysis reveals no statistically significant relationship between Twitter sentiment and actual student enrollment trends. These findings underscore the effectiveness of lexicon-enhanced machine learning in sentiment analysis while raising important questions about the real-world impact of online sentiment on university admissions.
Co-Authors Achmad Solichin Anju A Nair Ari Budi Prasetiyo Danang Harito Wibowo Painem Painem