SUMBOGO, RIFQI PUTRAWAN (2025) KOMPARASI KINERJA NAIVE BAYES DAN LSTM DALAM ANALISIS SENTIMEN TERHADAP DANANTARA. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
This study aims to analyze the sentiment of the Indonesian public toward Danantara, a national investment management agency, using the Naive Bayes and Long Short-Term Memory (LSTM) algorithms. Data were collected from the social media platform X (formerly Twitter) during the period from November 1, 2024, to June 30, 2025, resulting in 2,562 tweets. After a manual relevance filtering process, 924 tweets were identified as directly related to the topic of Danantara. Sentiment labeling was then carried out automatically using the IndoBERT model, classifying the tweets into three categories: positive, negative, and neutral. The labeling results showed a dominance of negative sentiment at 73.2%, followed by positive sentiment at 26.7%, and neutral sentiment at 0.1%. The Naive Bayes and LSTM models were subsequently used to compare sentiment classification performance on the labeled tweets. Based on model performance evaluation, the Naive Bayes algorithm yielded the best results with an accuracy of 78.91%, precision of 79.59%, recall of 78.91%, and an F1-score of 74.87%. In comparison, the LSTM model achieved an accuracy of 74.73%, precision of 73.53%, recall of 74.73%, and an F1-score of 73.98%. Keywords: Danantara, Sentiment, Naive Bayes, LSTM, Twitter. Penelitian ini bertujuan untuk menganalisis sentimen masyarakat Indonesia terhadap Danantara, sebuah badan pengelola investasi nasional, dengan menggunakan algoritma Naive Bayes dan Long Short-Term Memory (LSTM). Data dikumpulkan melalui media sosial X (sebelumnya Twitter) selama periode 1 November 2024 hingga 30 Juni 2025, menghasilkan 2.562 tweet. Setelah dilakukan tahap seleksi relevansi secara manual, diperoleh 924 tweet yang berkaitan langsung dengan topik Danantara. Selanjutnya, proses pelabelan sentimen dilakukan secara otomatis menggunakan model IndoBERT ke dalam tiga kategori: positif, negatif, dan netral. Hasil pelabelan menunjukkan dominasi sentimen negatif sebesar 73,2%, diikuti oleh sentimen positif sebesar 26,7%, dan netral sebesar 0,1%. Model Naive Bayes dan LSTM kemudian digunakan untuk membandingkan kinerja klasifikasi sentimen terhadap tweet yang telah dilabeli. Berdasarkan evaluasi performa model, algoritma Naive Bayes menunjukkan hasil terbaik dengan akurasi sebesar 78,91%, precision 79,59%, recall 78,91%, dan F1-score 74,87%. Sementara itu, model LSTM mencatatkan akurasi 74,73%, precision 73,53%, recall 74,73%, dan F1- score 73,98%. Kata kunci: Danantara, Sentimen, Naive Bayes, LSTM, Twitter
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