Analisis Sentimen Masyarakat terhadap Program Makan Siang Gratis di Indonesia Tahun 2024 Menggunakan Long Short-Term Memory (LSTM)
Bibliographic record
Abstract
The free lunch program is a goverment initiative aimed at addressing the issue of stunting in Indonesia. This program focuses on toddlers, school-age children and pregnant women. Various opinions have emerged from the public regarding this initiative, especially through sosial media platform X (Twitter) and news portals. In this research, sentiment analysis was conducted to understand public responses to the program, whether they are positive, neutral or negative. To evaluate the accuracy of the sentiment analysis perfomed, a deep learning approach was applied using the Long Short-Term Memory (LSTM) algorithm. The results show that public sentiment varies responses, on social media X tend to be negative, while those on news portals tend to be positive toward the free lunch program in Indonesia. Through LSTM-based testing, sentiment analysis on tweet data achieved an accuracy of 88.6%, with a precision of 84.6%, recall of 88.6% and an F1-Score of 86.3%. Meanwhile, sentiment analysis on news portal data reached an accuracy of 89%, with a precision of 81.7%, recall of 89% and an F1-Score of 85.1%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".