Analisis Pengaruh Influencer Terhadap Produk Yang Dijual Menggunakan Metode Long Short Term Memory
DOI:
https://doi.org/10.22441/jitkom.v10i1.001Keywords:
Analisis Sentimen, Crawling Data, Influencer, LSTM, ProdukAbstract
Media sosial merupakan salah satu platform yang banyak digunakan oleh masyarakat untuk berinteraksi, berbagi informasi, dan mengikuti perkembangan terkini. Media sosial juga menjadi sarana pemasaran yang efektif bagi para pelaku bisnis, khususnya dalam bidang produk konsumen. Salah satu strategi pemasaran yang umum di media sosial adalah memanfaatkan influencer. Penelitian ini mengeksplorasi dan menganalisis pengaruh influencer terhadap produk yang dijual dengan menggunakan teknik crawling data dan metode Long Short Term Memory (LSTM). Data dikumpulkan dari platform Twitter untuk mengidentifikasi pola perilaku konsumen terkait pemasaran produk melalui influencer, berdasarkan komentar positif dan negatif. Teknik crawling data digunakan untuk mengumpulkan data yang relevan dari platform online, sedangkan metode LSTM digunakan untuk memodelkan pola temporal yang kompleks. Hasil penelitian menunjukkan bahwa dari perbandingan tiga model LSTM, model LSTM 1 lapisan mencapai akurasi sebesar 63%, model LSTM 2 lapisan mencapai akurasi sebesar 74%, dan model BILSTM mencapai akurasi sebesar 76%. Dengan dataset yang telah dibersihkan sebanyak 4,353 data, terdiri dari 1,595 data berlabel "Positif" dan 2,758 data berlabel "Negatif", serta menggunakan metode BI-LSTM dengan perbandingan 30% data testing dan 70% data training, diperoleh akurasi sebesar 77% pada data training. Selain itu, nilai recall sebesar 63%, presisi sebesar 70%, dan nilai f1-score sebesar 66% juga dicapai.References
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