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All Journal Jurnal Syntax Transformation Research Horizon Jurnal INFOTEL
Hari Soetanto
Universitas Budi Luhur, Indonesia
Arts Humanities Computer Science & IT Economics, Econometrics & Finance 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.
A Comparative Analysis of Machine Learning Regression Models for TikTok User Engagement Prediction Lidiasonata Sitohang; Swinsikya Sitohang; Hari Soetanto
Research Horizon Vol. 6 No. 3 (2026): Research Horizon - Juni 2026
Publisher : LifeSciFi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54518/rh.6.3.2026.1166

Abstract

The rapid growth of TikTok has made user engagement prediction a critical challenge for content creators and digital marketers, particularly given the high multicollinearity among interaction features such as likes, comments, and shares. This study aims to conduct a comparative analysis of three machine learning models, namely linear regression, elastic net, and support vector regression, in predicting TikTok user engagement levels. The methodology employs a quantitative approach using the cross-industry standard process for data mining framework, evaluating model performance through mean absolute error, root mean squared error, mean absolute percentage error, and coefficient of determination metrics. Findings reveal that the elastic net is the most reliable model, achieving a mean absolute error of 3.98 and root mean squared error of 9.37 with a coefficient of determination of 1.000, supported by consistent cross-validation results across five folds. Linear regression produced trivial perfect scores due to the direct summation relationship between input features and the target variable, while support vector regression demonstrated suboptimal performance with a mean absolute error of 74.58, indicating difficulty in capturing linear data patterns. These results suggest that regularization-based models offer a more practical and generalizable framework for social media engagement prediction, providing actionable insights for practitioners in developing data-driven content strategies.
Sentiment Analysis of User Comments on Vidio.com Digital Streaming Platform in Indonesia Violinna Hutagalung; Hari Soetanto
Research Horizon Vol. 6 No. 3 (2026): Research Horizon - Juni 2026
Publisher : LifeSciFi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54518/rh.6.3.2026.1381

Abstract

The development of digital streaming platforms has transformed the way people access entertainment, including in Indonesia, making sentiment analysis of user experience crucial for evaluating service quality. This study aims to analyze Vidio.com user sentiment based on 173,868 comments during April 2026 and identify the distribution of sentiment and key factors influencing user perceptions. The method used is lexicon-based sentiment analysis with data preprocessing and classification of positive, negative, and neutral polarities using a computational approach on large-scale text data. The results show that sentiment is dominated by negative (52.3%), followed by neutral (44.3%) and positive (3.4%), with the main issues stemming from ad interruptions and technical issues such as buffering and content playback. These findings indicate that user experience is still influenced by monetization strategies and infrastructure quality, particularly regarding excessive advertising. In conclusion, improving service quality and optimizing advertising strategies have important implications for platform development to increase user satisfaction and engagement. The implication is that the results of this study can be used as a basis for data-based decision-making in improving user experience through improving streaming systems, reducing ad interruptions, and strengthening continuous sentiment evaluation to support business strategies that are more responsive to user needs.
Co-Authors Achmad Solichin Anju A Nair Ari Budi Prasetiyo Danang Harito Wibowo Lidiasonata Sitohang Painem Painem Swinsikya Sitohang Violinna Hutagalung