Perbandingan Algoritma Deep Learning untuk Analisis Sentimen Ekowisata di Bogor: Comparison of Deep Learning Algorithm in Sentiment Analysis  Ecotourism in Bogor

Peni Agustini; Muhammad Iqbal; Vicha Amalia Akbar; Robert Kurniawan

doi:10.57152/malcom.v5i3.2191

Authors

Peni Agustini Politeknik Statistika STIS
Muhammad Iqbal Politeknik Statistika STIS
Vicha Amalia Akbar Politeknik Statistika STIS
Robert Kurniawan Politeknik Statistika STIS

DOI:

https://doi.org/10.57152/malcom.v5i3.2191

Keywords:

Bogor, Ekowisata, Deep Learning, Scraping, Sentimen

Abstract

Bogor memiliki destinasi ekowisata unggulan di Indonesia yang menawarkan keasrian alam dan kemudahan akses dari Jakarta. Namun, peningkatan jumlah wisatawan menimbulkan hambatan terhadap pengelolaan lingkungan, seperti pengelolaan sampah dan tekanan terhadap sumber daya alam. Media sosial, khususnya Google Maps, berperan penting dalam promosi dan memahami perilaku wisatawan melalui fitur ulasan. Studi ini bertujuan melakukan analisis sentimen mengenai ulasan ekowisata di Bogor yang diambil dari Google Maps, menggunakan metode Deep Learning berbasis neural network, yaitu Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), dan Long Short-Term Memory (LSTM), dan membandingkan performa ketiga model tersebut untuk menentukan metode terbaik dalam mengklasifikasikan sentimen pengunjung. Hasil studi ini menunjukkan, model CNN memiliki akurasi tertinggi yaitu sebesar 72 persen dan lebih unggul dibanding model RNN dan LSTM. Model CNN dapat digunakan sebagai acuan utama dalam menerapkan analisis sentimen pada topik yang sejenis.

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