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This is a Quantitative Study study.

October 3, 2025EDUMATIC Jurnal Pendidikan Informatika

Analisis Sentimen Pinjaman Online: Studi Komparatif Algoritma Naïve Bayes, Decision Tree, dan KNN

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Authors

KMKhyntia MirandaUniversitas Teknokrat Indonesia RSRyan Randy SuryonoUniversity of Indonesia

Discussion

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Implication

This quantitative analysis compares sentiment detection effectiveness of naive bayes, decision tree, and knn algorithms, indicating decision tree's superior accuracy.

Key Points

The decision tree algorithm achieved the highest accuracy at 69%, revealing its strength in recognizing complex sentiment patterns.
Naive Bayes performed well in negative sentiment classification with 68% accuracy, demonstrating its efficiency in specific contexts.
KNN struggled with a 44% accuracy, indicating challenges in managing high-dimensional text data commonly found in social media.
Utilizing these algorithms can help online loan service providers better monitor public sentiment, enhancing customer service responsiveness.

Cite This Study

Miranda et al. (2025) studied this question.

synapsesocial.com/papers/68e040eda99c246f578b32a8 https://doi.org/10.29408/edumatic.v9i2.30142

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Implication

This quantitative analysis compares sentiment detection effectiveness of naive bayes, decision tree, and knn algorithms, indicating decision tree's superior accuracy.

Analisis Sentimen Pinjaman Online: Studi Komparatif Algoritma Naïve Bayes, Decision Tree, dan KNN

Discussion

Member takes

Implication

Key Points

Cite This Study

Analisis Sentimen Pinjaman Online: Studi Komparatif Algoritma Naïve Bayes, Decision Tree, dan KNN

Discussion

Member takes

Implication

Key Points

Cite This Study

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