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All Journal Jasmine : Journal of Intelligent Systems and Machine Learning
Dody Herdiana
Universitas Sebelas April, Sumedang

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Public Sentiment Toward Rupiah Redenomination on Social Media X Agung Febrian; Dody Herdiana; M. Agreindra Helmiawan
JASMINE: Journal of Intelligent Systems and Machine Learning Vol. 1 No. 1 (2026)
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jasmine.v1i1.10121

Abstract

This study examines public sentiment toward the proposed Indonesian Rupiah redenomination policy using data collected from Social Media X. The research applies a structured computational sentiment analysis pipeline, beginning with automatic sentiment labeling using a transformer-based language model, followed by classification using a Support Vector Machine with Term Frequency–Inverse Document Frequency feature representation. The dataset was collected over a one-day period from 7 November 2025 to 8 November 2025 to capture immediate public reactions to the policy discourse. Experimental results show that the classification model achieved an accuracy of 0.68 with balanced classification performance. Rather than aiming to optimize predictive accuracy, this study focuses on identifying general sentiment tendencies and patterns of public opinion regarding currency redenomination. The findings indicate that negative sentiment dominates the discourse, reflecting public concern and hesitation toward the policy, while a substantial proportion of neutral sentiment suggests ongoing evaluation and uncertainty among users. These results highlight the complexity of public responses to monetary policy communication and demonstrate the potential of social media analysis to provide an indicative overview of public sentiment in the digital public sphere. The study also acknowledges limitations related to automatic labeling and the inherent ambiguity of social media language, emphasizing that the findings should be interpreted as exploratory insights rather than definitive conclusions.
Evaluation of Machine Learning Algorithms for Predicting Phishing Attacks in Higher Education Environments: An Experimental Framework for Enhancing Cybersecurity in Academic Institutions Akmal Muhammad Poetra; Dody Herdiana; Muhammad Agreindra Helmiawan
JASMINE: Journal of Intelligent Systems and Machine Learning 2026: Articles in Press
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jasmine.vi.10178

Abstract

This study evaluates the performance of several machine learning algorithms Logistic Regression, Support Vector Machine, Random Forest, and XGBoost in predicting phishing attacks within higher education environments. Due to the limited availability of anonymized institutional datasets, the research employs a conceptual experiment design and simulation-based approach that mirrors the characteristics of phishing incidents commonly encountered by academic users. The simulated dataset includes URL-based indicators, HTML features, email text elements, and behavioral metadata. The experimental protocol covers synthetic data generation, domain-specific feature engineering, stratified k-fold cross-validation, hyperparameter tuning via grid search, and performance evaluation using accuracy, precision, recall, F1-score, and ROC/AUC. The simulation results indicate that ensemble-based models (Random Forest and XGBoost) outperform linear and kernel-based models, especially in scenarios with class imbalance typical of campus environments. The discussion highlights implications for real-world campus cybersecurity operations, limitations of conceptual simulations, and future research needs such as real-world validation and the integration of user behavior features. The main contribution is a complete experimental framework that can be executed with real institutional datasets, providing guidance for model selection and deployment in higher education cybersecurity systems.
Co-Authors Agung Febrian Akmal Muhammad Poetra M. Agreindra Helmiawan Muhammad Agreindra Helmiawan