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All Journal JSM (Jurnal SIFO Mikroskil) Journal La Multiapp Engineering Science Letter
Nadya Sikana
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Movie Success Prediction Based on Feature and Trailer Comments Using Ensemble+LSTM Model Nadya Sikana; Purba, Ronsen
Journal La Multiapp Vol. 5 No. 5 (2024): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v5i5.1417

Abstract

Predicting the success of a movie is a very important aspect due to the high risks involved in movie production. The challenge lies in the uncertainty within the movie industry and selecting the appropriate machine learning model. We can combine movie features and sentiment analysis from social media using machine learning techniques to achieve movie success prediction. The methods used for predicting based on movie features are Ensemble models (Random Forest + Gradient Boosting). Meanwhile, the methods used for sentiment analysis of trailer comments is LSTM. The evaluation of the models used is based on RMSE and accuracy calculation. The final prediction of success obtains an RMSE of 0,8807 and an accuracy of 91,19%. This represents an improvement from previous research. Further research is recommended to implement the model in the movie industry
BERT Model Implementation for Dynamic Sentiment Analysis of Pertamina on Social Media X Purba, Ronsen; Lubis, Rivaldi; Sikana, Nadya; Situmorang, Gilbert Fernando
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.
Analisis Sentimen untuk Ulasan Produk E-Commerce Shopee Menggunakan BERT Sikana, Nadya; Winardi, Sunaryo; -, Gunawan; Situmorang, Gilbert Fernando; Lubis, Rivaldi
Jurnal Sifo Mikroskil Vol. 26 No. 2 (2025): JSM VOLUME 26 NOMOR 2 TAHUN 2025
Publisher : Fakultas Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55601/jsm.v26i2.1796

Abstract

Analisis sentimen sangat penting untuk memahami opini konsumen dan menyempurnakan strategi e-commerce. Analisis ini menghadapi tantangan seperti bahasa informal, ambiguitas semantik, dan inkonsistensi antara sentimen tekstual dan peringkat bintang, yang memengaruhi akurasi klasifikasi. Penelitian ini menerapkan model BERT (Bidirectional Encoder Representations from Transformersi) untuk mengklasifikasikan sentimen dalam ulasan pengguna Shopee. Data dikumpulkan dari penelitian sebelumnya dan menjalani praproses, termasuk tokenisasi, penghapusan stopword, dan normalisasi teks. Pendekatan analisis sentimen berbasis leksikon digunakan sebagai dasar perbandingan. Model BERT disempurnakan menggunakan optimasi hiperparameter, mencapai akurasi 83,08%, presisi 82,91%, recall 83,08%, dan F1-score 82,87%. Dibandingkan dengan studi sebelumnya yang menggunakan Naïve Bayes dengan N-Gram dan Information Gain, yang mencapai akurasi 92% tetapi presisi lebih rendah (56%), recall (65%), dan F1-score (60%), BERT mengungguli dengan metrik evaluasi yang lebih seimbang dan keandalan prediktif yang lebih besar. Hasil ini menunjukkan kemampuan BERT untuk menangkap konteks semantik dua arah, melampaui metode tradisional dalam menangani tugas analisis sentimen yang kompleks.
Co-Authors Gunawan - Rivaldi Lubis Ronsen Purba Situmorang, Gilbert Fernando Winardi, Sunaryo