Publisher

BSIBSI

Journal Computer Science (CO-SCIENCE)Computer Science (CO-SCIENCE) Abstract

Makanan Bergizi Gratis (MBG) program is a strategic initiative of the Indonesian government to improve the nutritional quality of schoolchildren. This research seeks to examine public sentiment regarding the MBG program by leveraging 10,000 tweets obtained from Kaggle. The method used combines Natural Language Processing (NLP) and Machine Learning approaches, several algorithms such as Logistic Regression, Support Vector Machine (SVM), Random Forest, Naive Bayes, XGBoost, and LightGBM were tested to compare classification performance. The dataset contains a collection of public reviews categorized into three sentiment classes: positive, negative, and neutral. The analysis process includes text cleaning, tokenization, stopword removal, and stemming to obtain a cleaner text representation. Text features were then extracted using the Term Frequency–Inverse Document Frequency (TF-IDF) method. The results showed that the Logistic Regression 97% with an F1-score of 0.9552 models showed the most optimal performance. Sentiment analysis revealed 65% positive responses, 25% neutral, and 10% negative, with the dominant keywords being “nutrisi, “sehat, “anak sekolah, and “gratis. The results visualization, in the form of a Word Cloud and a bar chart, indicate that public opinion tends to be positive towards the implementation of the MBG program, particularly regarding improving the nutrition of schoolchildren. This research is expected to provide input for policymakers in evaluating public perceptions of the implementation of food-based social programs.

Conclusion

The experimental results demonstrate that the application of Natural Language Processing (NLP) with the TF-IDF technique can transform public opinion text into effective numerical features for sentiment classification.Logistic Regression and LightGBM models demonstrated optimal performance.Logistic Regression excels in its ability to linearly separate classes in text data processed using TF-IDF, resulting in accurate classification.LightGBM, on the other hand, stands out for its efficient training time and better memory utilization, especially when processing data with a large number of features.Overall, the analysis results indicate that public sentiment toward the Free Nutritional Meals program tends to be positive, with the majority of opinions supporting the program as an appropriate step to improve child health and nutritional equity in schools.This research successfully demonstrates that machine learning can be used effectively to analyze public perceptions of national-scale social policies.

Future Research

Berdasarkan penelitian ini, terdapat beberapa saran penelitian lanjutan yang dapat dilakukan. Pertama, penelitian selanjutnya dapat mengeksplorasi penggunaan model bahasa yang lebih canggih, seperti transformer-based models (misalnya, IndoBERT), untuk meningkatkan akurasi analisis sentimen, terutama dalam menangkap nuansa bahasa Indonesia yang kompleks. Kedua, penelitian dapat memperluas cakupan data dengan menggabungkan data dari berbagai sumber media sosial dan platform online lainnya untuk mendapatkan gambaran yang lebih komprehensif tentang opini publik. Ketiga, penelitian dapat menginvestigasi faktor-faktor kontekstual yang memengaruhi sentimen publik, seperti demografi, lokasi geografis, dan tingkat pendidikan, untuk memahami lebih dalam bagaimana faktor-faktor ini berkontribusi terhadap persepsi masyarakat terhadap program MBG. Dengan menggabungkan ketiga saran ini, diharapkan penelitian selanjutnya dapat memberikan wawasan yang lebih mendalam dan bermanfaat bagi pembuat kebijakan dalam merancang dan mengevaluasi program-program sosial.

References

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