## 3 Results
### Experimental Results of the Machine Learning Based Approach
**Linguistic Specificity and Stemming Challenges:** A substantial limitation stems from the linguistic specificity of our dataset, which predominantly comprises comments in the Indonesian language. The choice of Sastrawi stemming, limited to the basic form of Indonesian words, raises concerns about the method's adaptability to diverse linguistic contexts. The intricacies of different languages, dialects, and linguistic nuances may impact the performance of our approach when applied to datasets outside the scope of the Indonesian language. Additionally, excluding online annotation tools like MonkeyLearn was strategic, considering potential mismatches with our linguistic context and stemming approach. It underscores the need for future research to explore the generalizability of our methodology to multilingual settings. Additionally, investigating alternative annotation tools is crucial. **Applicability to Hate Speech Variants and Contexts:** While our approach demonstrates effectiveness in detecting hate speech related to the context of COVID-19, its generalizability to diverse hate speech variants remains a subject of scrutiny. The efficacy of our approach in identifying overt hate speech is well-established. However, we acknowledge the inherent challenges of recognizing sarcasm and subtle forms of expression. These may need to be more effectively captured by our current method. The thematic specificity of our training data could pose challenges in adapting the model to recognize hate speech in different domains or contexts. Future research should prioritize expanding the dataset to encompass a broader spectrum of hate speech variants. It ensures the versatility and adaptability of our approach across a wide range of contexts. A robust hate speech detection model should transcend specific thematic constraints. It maintains relevance and effectiveness in real-world applications. **Dataset Limitation to COVID-19 Context:** An inherent limitation of our study lies in the deliberate restriction of the dataset. It focuses specifically on hate speech related to the COVID-19 context. While this focus aligns with the contemporary relevance of the issue, it introduces a thematic bias. This bias may limit the model's ability to discern hate speech in unrelated contexts. Future research endeavors should prioritize the diversification of the dataset. This includes encompassing hate speech in various thematic domains, ensuring a more comprehensive evaluation of our method's robustness and adaptability. ## 4 Conclusions

This research introduces a novel approach to automated text annotation, leveraging semi-supervised and self-learning strategies. The study showcases the effectiveness of our method in enhancing machine learning by integrating a meta-learner for hate speech annotation in Indonesian text. The proposed concept combines a meta-learner and a meta-vectorizer to get an annotation process with a minimal dataset applied. Our approach utilizes a minimal labeled dataset of 20%, 10%, and 5%, incorporating a sophisticated weighting mechanism and a threshold system with four predefined values: 0.6, 0.7, 0.8, and 0.9. The optimization process for annotating small datasets yielded remarkable results, achieving automatic annotations with only 5% labeling and a threshold of 0.9. Notably, the DT-TF-IDF method achieved the highest accuracy, reaching an impressive 97.1%. In addition, the model that has the best accuracy is followed by KNN-Word2 Vec (96.9%),SVM-Word2 Vec (96.8%), SVM-TF-IDF (93.4%), and DT-Word2 Vec (93.4%) in the same scenario (5%, 80%, 0.9). The findings indicate a consistent trend of improved accuracy with increasing threshold values, with the highest threshold yielding superior accuracy across all scenarios. This result is inversely proportional to the percentage of labeled training data--lower percentages correlate with higher accuracy values. Based on Table 1, we observe that the accuracy value increases as the dataset size decreases from largest to smallest.