Article Per Year (5 Year)
p-Index From 2021 - 2026
0.702
P-Index
This Author published in this journals
All Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Engineering Science Letter
Nadya Sikana
Universitas Mikroskil
Computer Science & IT Control & Systems Engineering Education Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
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.
Forecasting Rice Price Volatility Utilizing BiLSTM-SHAP to Ensure National Food Stability Juliana Damayanti Manurung; Nadya Sikana; Fandi Presly Simamora; Zoni Zikro Manurung
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.001360

Abstract

Rice price volatility in Indonesia remains a persistent economic issue, partly driven by climate variability and fluctuations in national rice production, prompting the government to resort to substantial annual imports. However, the extent to which domestic production factors and weather conditions influence future rice prices has not been quantitatively evaluated. This study aims to forecast short-term rice prices in Indonesia by integrating multiple time-series features, including rice prices, harvested area, paddy production, and weather features, using a Bidirectional Long Short-Term Memory (BiLSTM) network. Daily data from 2013 to 2024 were collected from the National Statistics Agency, Food Price Panel, and the Meteorology and Climatology Agency. Chronological split was applied for training, validation, and testing to preserve temporal dependency. The optimal model predicts rice prices seven days ahead using 256 hidden units, achieving MAE of 128.84 IDR, RMSE of 157.98 IDR, and R² of 0.694. SHAP analysis shows that historical rice prices have the strongest contribution with a SHAP value of 0.969652, significantly higher compared to other features. The results demonstrate that integrating agricultural and climatic inputs improves predictive performance while providing interpretable insights into price-forming factors.
An Intelligent System for Detecting Online Gambling Promotion on Social Media Based on BERT and Adaptive Browser Extension Gilbert Fernando Situmorang; Juliana Damayanti Manurung; Rivaldi Lubis; Nadya Sikana; Kenneth Lionggo
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 3 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i3.103511

Abstract

The proliferation of online gambling promotions on social media has created serious social and legal problems in Indonesia, yet existing detection approaches are largely post-hoc, rely on general purpose language models, and struggle to identify implicit and domain specific promotional language that is deliberately used to evade moderation. Moreover, most prior studies focus solely on offline model evaluation and are rarely integrated into real time, deployable systems that can directly protect users. To address these limitations, this study proposes an intelligent detection system utilizing IndoBERT enhanced with Domain Adaptive Pre-Training on a corpus of Indonesian online gambling related text, followed by fine-tuning for text classification. The adapted model demonstrates superior performance, achieving an F1-Score of 98.58% and outperforming baseline approaches including TF-IDF+SVM, BiLSTM, multilingual BERT, and IndoBERT without domain adaptation. To bridge the gap between model development and real-world application, the proposed model is further integrated into an adaptive browser extension capable of scanning, classifying, and filtering social media content in real-time. Functional testing on YouTube and X shows that the system effectively detects and masks online gambling promotional content without disrupting neutral or anti-gambling discourse. This research contributes both methodologically, by demonstrating the effectiveness of domain adaptive pre-training for detecting implicit promotional language, and practically, by delivering a deployable system that provides proactive protection for social media users.
Co-Authors Fandi Presly Simamora Gilbert Fernando Situmorang Gilbert Fernando Situmorang Juliana Damayanti Manurung Juliana Damayanti Manurung Kenneth Lionggo Naomi Prisella Rivaldi Lubis Ronsen Purba Zoni Zikro Manurung