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Etika Kartikadarma
Universitas Dian Nuswantoro
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Journal : journal of applied informatics and computing

 1 
A Sentiment Analysis of Free Nutritious Meal Program on Platform X: Comparing Naive Bayes, SVM, Random Forest, and IndoBERT Alrijal Nur Ilham; Etika Kartikadarma
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12959

Abstract

The Free Nutritious Meal Program (MBG), launched by the Indonesian government in January 2025, generated various public responses on social media, particularly on platform X. This study aims to analyze public sentiment toward the MBG Program and compare the performance of four sentiment classification methods: Naive Bayes, Support Vector Machine (SVM), Random Forest, and IndoBERT. The dataset was collected through tweet crawling using the keywords “MBG” and “Makan Bergizi Gratis” during the period of July–December 2025, resulting in 1,906 Indonesian-language tweets. The preprocessing stage included cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Sentiment labeling was performed using the InSet Lexicon and produced 1,113 negative tweets and 793 positive tweets. Manual validation on part of the dataset was conducted by two independent annotators and achieved a Cohen’s Kappa score of 0.78, indicating substantial agreement. For classical machine learning models, feature extraction was carried out using TF-IDF, while IndoBERT used contextual text representations without stemming. Class imbalance in classical models was handled using SMOTE, whereas IndoBERT applied class weighting. The experimental results show that IndoBERT achieved the best performance with an accuracy of 92.93%. Among the classical models, SVM produced the highest performance with an accuracy of 92.15%, followed by Naive Bayes and Random Forest. Word frequency analysis also revealed that positive sentiment was mainly associated with support for the program and nutrition-related topics, while negative sentiment was dominated by concerns about food safety, budget management, and criticism of the program. Based on the findings, IndoBERT is more effective in understanding the context of Indonesian-language tweets. However, TF-IDF-based classical models, especially SVM, still provide competitive performance with lower computational requirements, making them suitable for sentiment analysis in public policy studies.
A Comparison of Classical Machine Learning and IndoBERT on Sentiment Analysis of Danantara Program in X Silvan Pradana; Etika Kartikadarma
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13280

Abstract

The rapid growth of social media has made it a primary channel for the public to express opinions on national strategic economic policies, including the establishment of the Danantara entity. This study aims to map public sentiment on Platform X and compare the performance of classical frequency-based architectures with transformer-based models. A common research gap in previous studies is the reliance on Bag-of-Words models, which fail to capture local context and sarcasm in informal text. A total of 9,525 tweets from the period January–May 2025 were collected via crawling and labeled using a hybrid approach combining InSet Lexicon and manual validation by experts (Cohen’s Kappa = 0.81). To address significant class imbalance (66.5% negative), SMOTE was applied to classical models. Experimental results reveal a significant performance gap: the classical TF-IDF + SVM model achieved a positive-class F1-score of only 59% due to feature distortion caused by SMOTE in the TF-IDF space, while the fine-tuned IndoBERT model substantially outperformed it with a global accuracy of 95.80% and a positive-class F1-score of 81%. These findings demonstrate that the deep transformer approach is far more robust in extracting semantics from informal Indonesian social media text, with practical implications for public policy decision-making.
Co-Authors Abu Salam Adhitya Nugraha Aditya Wahyu Ramadhan Affandy Affandy Afida, Dita Ahmad Zainul Fanani Ajib Susanto Akbar Dwi Syahputra Alrijal Nur Ilham Alvin Jaya Hulu, Alvin Angga Apriano Hermawan Arnold Adimabua Ojugo Ashari Juang, Ashari Astuti, Yani Parti Azhara Devi Sandi Bimo Haryo Setyoko Catur Supriyanto Christy Atika Sari Cinantya Paramita De Rosal Ignatius Moses Setiadi Desi Purwanti Devva Ricovani Susanto Dhani, Iqbal Dhita Aulia Octaviani Dianna Yanuaresta Dico Tri Rosandi Doheir, Mohamed Dwi Puji Prabowo Edy Mulyanto Eferhire Valentine Ugbotu Egia Rosi Subhiyakto Egia Rosi Subhiyakto Egia Rosi Subhiyakto, Egia Rosi Ekaprana Wijaya Endri Mujiono Erika Devi Udayanti Erlin Dolphina Erwin Yudi Hidayat Fahmi Amiq Fahri Firdausillah Farikh Al Zami Fauzi Adi Rafrastara Filmada Ocky Saputra Habib Mustofa Hafidhoh, Nisa'ul Hafidhoh, Nisa’ul Hafidhoh, Nisa’ul Heribertus Himawan Ifan Rizqa Ifan Rizqa Ifan Rizqa Ifan Rizqa Ifan Rizqa Ifan Rizqa Ihwati Ummi Iskandar, Marcelino Johary, Lakui Junta Zeniarja Kumoro, Imanuel Dimas Cahyo Kurniawan, Defri Kusni Ingsih L. Budi Handoko Lakui Johary Marcelino Iskandar Meilani Dwi Permatasari Muhamad Ni'am Syukri Roni Asmi Muhammad Hafidz Muljono, - Najma Fatimah, Nandhita Nathaniel Alexander Nila Tristiarini, Nila Nisa'ul Hafidhoh Nova Rijati Octara Pribadi Pujiono Pujiono Purwanto Purwanto Rahma, Khalida Nur Raihan Yusuf Ricardus Anggi Pramunendar Rino Agung Robet Robet Rohman, Muhammad Syaifur Safa Firdaus, Muhammad Argya Sakti, Maulana Bima Saputra, Filmada Ocky Saraswati, Galuh Wilujeng Sari Ayu Wulandari Sari Wijayanti Sari Wijayanti Sari Wijayanti Setyawati, Vilda A. V. Silvan Pradana Sindhu Rakasiwi Sudibyo, Usman Sugiyanto - Suyud Widiono T. Sutojo Tabitha Chukwudi Aghaunor Tri Listyorini Trisnapradika, Gustina Alfa Usman Sudibyo Utomo, Danang Wahyu Wardatunizza, Indah Wibowo, Alrico Rizki Widayat Yutriatmansyah, Widi Widi Widayat Yutriatmansyah Wikan Isthika, Wikan Winarsih, Nurul Anisa Sri Yani Parti Astuti Yani Parti Astuti Yunita Kemala Sari Yutriatmansyah, Widi Widayat Yutriatmansyah, Widi Widayat Zaenal Arofi, Muhammad Labib