Pemanfaatan Analisis Sentimen Youtube untuk Prediksi Harga Saham: Studi pada Investor Retail Indonesia
DOI:
https://doi.org/10.25170/jm.v21i1.5184Kata Kunci:
LSTM, Bi-LSTM, machine learning, stock, sentimentAbstrak
Lonjakan partisipasi investor ritel dalam pasar saham Indonesia seiring dengan meningkatnya jumlah platform media sosial sebagai sumber informasi terkait saham. Khususnya di YouTube, semakin banyak pencipta konten yang menyebarkan wawasan tentang saham, termasuk aksi korporasi yang relevan. Penelitian ini bertujuan untuk memanfaatkan data sentimen yang diekstraksi dari video YouTube tertentu untuk memprediksi harga penutupan saham khususnya selama aksi korporasi, dengan menggunakan model Long Short-Term Memory (LSTM) dan Bidirectional LSTM (Bi-LSTM).
Studi ini tidak hanya fokus pada penggunaan sentimen dari video YouTube sebagai variabel prediktif, tetapi juga meliputi analisis tambahan yang melibatkan berbagai algoritma klasifikasi untuk meningkatkan akurasi prediksi. Hasil penelitian menunjukkan bahwa sementara sentimen dari YouTube merupakan variabel yang dapat digunakan untuk prediksi, model Bi-LSTM lebih unggul daripada LSTM dalam memprediksi harga saham seputar tanggal aksi korporasi. Selain itu, integrasi algoritma klasifikasi menunjukkan potensi untuk meningkatkan prediksi, menunjukkan kemungkinan peningkatan skor akurasi ketika diintegrasikan ke dalam model prediksi.
Penelitian ini memberikan wawasan tentang efektivitas analisis sentimen dari konten YouTube dalam memprediksi harga saham selama aksi korporasi, menyoroti potensi nilai penggunaan model Bi-LSTM dan dampak positif integrasi algoritma klasifikasi untuk meningkatkan akurasi prediksi. Temuan ini memiliki signifikansi bagi investor ritel Indonesia yang mencari alat pengambilan keputusan yang lebih baik dalam lanskap pasar saham yang dinamis.
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