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

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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
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