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All Journal Algoritme Jurnal Mahasiswa Teknik Informatika
Yo'el Pieter Sumihar
Universitas Kristen Immanuel Yogyakarta
Computer Science & IT

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Analisis Sentimen Berbasis Aspek Menggunakan Hybrid IndoBERT-Conv1D-BiLSTM Pada Program Makan Bergizi Gratis Dimas Aditia Anugerah Setiady; Sunneng Sandino Berutu; Yo'el Pieter Sumihar
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 7 No 1 (2026): October 2026 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v7i1.16444

Abstract

This study analyzes public sentiment toward the Free Nutritious Meal Program (MBG) using an Aspect-Based Sentiment Analysis (ABSA) approach on X (Twitter) data. A hybrid IndoBERT-Conv1D-BiLSTM model is proposed to classify sentiment across six MBG aspects. A total of 16,227 tweets were collected and automatically labeled using an Indonesian RoBERTa model, resulting in 14,756 tweets for training. After Random Oversampling, the model achieved a validation accuracy of 91.82%, a test accuracy of 91.10%, and a macro F1-score of 0.91. Compared with IndoBERT-only, CNN-only, BiLSTM-only, and SVM with TF-IDF, the proposed model achieved the best performance. The findings indicate that the proposed model is effective for aspect-based sentiment analysis and supports more comprehensive policy evaluation.
Co-Authors Dimas Aditia Anugerah Setiady Sunneng Sandino Berutu