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All Journal Engineering Science Letter
Gilbert Fernando Situmorang
Universitas Mikroskil
Computer Science & IT Control & Systems Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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BERT Model Implementation for Dynamic Sentiment Analysis of Pertamina on Social Media X Ronsen Purba; Rivaldi Lubis; Nadya Sikana; Gilbert Fernando Situmorang
Engineering Science Letter Vol. 4 No. 02 (2025): Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.esl.001139

Abstract

This study aims to investigate the dynamics of public sentiment on platform X in response to the Pertamina corruption scandal, exploring how trust and perception shifted before and after the incident. Utilizing BERT-based sentiment classification model trained on real-world social media posts, the model achieved a validation loss of 0.5078 and an F1-score of 82.12%, demonstrating strong predictive performance for large-scale sentiment analysis. Results revealed a significant rise in negative sentiment and a decline in positive sentiment following the public disclosure of the scandal on February 25, 2025, reflecting a deep erosion of public trust in Pertamina. Qualitative thematic analysis further identified a shift from neutral or positive discussions focused on service quality and innovation to emotionally charged critiques emphasizing betrayal, distrust and institutional failure. These findings highlight the value of integrating deep learning classification with qualitative insights to monitor real-time public opinion and institutional reputation. The study underscores the critical need for transparency and effective communication strategies during reputational crises to rebuild public confidence. Limitations include the focus on a single social media platform, suggesting future research should incorporate cross-platform and multilingual analyses. Practically, this research offers actionable insights for corporate crisis management and contributes to understanding social media’s role in shaping public trust and accountability in the digital age.
Hybrid Machine Learning for Crime Prediction in Indonesia toward Society 5.0 Nadya Sikana; Rivaldi Lubis; Gilbert Fernando Situmorang; Naomi Prisella
Engineering Science Letter Vol. 4 No. 03 (2025): Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.esl.001359

Abstract

Crime remains a major social challenge in Indonesia, requiring innovative approaches to enhance prevention and law enforcement. This study proposes a hybrid machine learning framework that integrates the Temporal Fusion Transformer (TFT) for time-series forecasting and Extreme Gradient Boosting (XGBoost) for classification and feature analysis. Using socio-economic and demographic data from the Indonesian Central Bureau of Statistics (2010-2023) across 38 provinces, the framework aims to predict crime incidence and classify crime resolution effectiveness. The results show that TFT effectively captures temporal dependencies, achieving robust forecasting accuracy (R2 = 0.9893), while XGBoost delivers high classification performance (Accuracy = 98.87%). Feature importance analysis highlights the dominant role of case resolution rate, government consumption expenditure, school participation rates and life expectancy in shaping crime patterns. Compared to baseline models such as LSTM and Random Forest, the hybrid TFT + XGBoost approach demonstrates superior balance between accuracy, robustness and interpretability. These findings provide actionable insights for policymakers to design data-driven crime prevention strategies, align with Indonesia’s digital transformation agenda, and support the vision of Society 5.0.
Co-Authors Nadya Sikana Naomi Prisella Rivaldi Lubis Ronsen Purba