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Faishal Basbeth
Institut Teknologi dan Bisnis Widya Gama Lumajang
Computer Science & IT

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Mapping Public Conversation on Indonesia's Free Nutritious Meal Program on Twitter (X) Using TF-IDF and K-Means Clustering Cahyasari Kartika Murni; Faishal Basbeth; Silviana Widya Lestari
Journal of Informatics Development Vol. 5 No. 1 (2026): Oktober 2026
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v5i1.2081

Abstract

Public programs now generate their own online commentary, and that commentary is worth reading systematically rather than anecdotally. This paper maps the topic structure of Indonesian-language conversation about the Free Nutritious Meal program (Makan Bergizi Gratis, MBG) on Twitter (X). We scraped 3,459 tweets posted in January and February 2025, and cleaned them through case folding, filtering, tokenization, stopword removal, and stemming, which left 2,926 documents, or 84.59% of the raw set. These were weighted with Term Frequency-Inverse Document Frequency (TF-IDF) into a 2,926 x 5,000 document-term matrix and partitioned with K-Means. The Elbow Method placed the optimal solution at K = 3, with an SSE of 2,760.3617 and a Silhouette Score of 0.0092. Three topics emerged: institutional endorsement tied to food security and self-sufficiency (94 tweets, 3.21%), everyday talk about how the program is being delivered and paid for (2,012 tweets, 68.76%), and the benefits of the program for children and their health (820 tweets, 28.02%). The Silhouette Score sits close to zero. We read this as a property of the corpus rather than a failure of the procedure: every document discusses the same program, so the clusters share a large common vocabulary and their boundaries are soft. The partition should therefore be treated as a first thematic map rather than a clean separation, and contextual representations such as Word2Vec, FastText, or IndoBERT are the obvious next step.
Comparative Analysis of Transformer Models for Emotion Classification in Indonesian Text Data Faishal Basbeth; Cahyasari Kartika Murni; Abdur Ro’uf
Journal of Informatics Development Vol. 5 No. 1 (2026): Oktober 2026
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v5i1.2082

Abstract

This study presents a comparative analysis of three transformer-based models, BERT, RoBERTa, and DistilBERT, for emotion classification on Indonesian text. The Indo4B dataset, which consists of five emotion labels (anger, fear, happiness, love, and sadness), is used as the benchmark corpus. All three models were trained using fine-tuning and hyperparameter-tuning procedures. The evaluation was conducted using accuracy, F1-score, model size, and training time as the primary performance metrics. The results indicate that RoBERTa achieves the best overall performance, with an accuracy of 90.83% and an F1-score of 91. Meanwhile, DistilBERT demonstrates approximately 60% faster training time compared to BERT, with only a 0.61% decrease in accuracy. These findings suggest that DistilBERT offers a highly efficient alternative while maintaining competitive classification performance.
Co-Authors Abdur Ro’uf Cahyasari Kartika Murni Silviana Widya Lestari