Penerapan Long Short-Term Memory Untuk Analisis Sentimen Ulasan Aplikasi Cryptocurrency Exchange Indonesia
DOI:
https://doi.org/10.29407/82zx9d63Abstract
Cryptocurrency exchange merupakan platform digital yang banyak digunakan untuk melakukan transaksi aset kripto. Banyaknya ulasan pengguna pada Google Play Store dan Apple App Store menyulitkan proses analisis secara manual sehingga diperlukan metode analisis sentimen otomatis. Penelitian ini bertujuan menganalisis sentimen ulasan pengguna lima aplikasi cryptocurrency exchange di Indonesia, yaitu Pintu, Tokocrypto, Indodax, Ajaib, dan Reku. Metode yang digunakan adalah Long Short-Term Memory (LSTM) dengan word embedding FastText. Dataset penelitian terdiri dari 27.500 ulasan yang diperoleh melalui web scraping dan API. Tahapan penelitian meliputi preprocessing data, pelatihan model, dan evaluasi menggunakan confusion matrix serta classification report. Hasil pengujian menunjukkan model memperoleh akurasi sebesar 86%. Analisis sentimen menunjukkan bahwa Indodax memperoleh sentimen positif tertinggi secara keseluruhan sebesar 89,58%, sedangkan Tokocrypto unggul pada kategori Trading dan Lainnya. Hasil penelitian dapat digunakan sebagai bahan evaluasi bagi pengembang aplikasi dan referensi bagi pengguna dalam memilih cryptocurrency exchange.
Keywords:
Analisis Sentimen, Cryptocurrency Exchange, FastText, LSTM, Ulasan Pengguna.##plugins.themes.default.displayStats.downloads##
References
[1] R. A. Saputra, D. P. Ray, and F. Irwiensyah, “Analisis Sentimen Aplikasi Tokocrypto
Berdasarkan Ulasan Pada Google Play Store Menggunakan Metode Naïve Bayes,” KLIK:
Kajian Ilmiah Informatika dan Komputer, vol. 4, no. 4, 2024, doi:
10.30865/klik.v4i4.1707.
[2] H. F. B. Wewengkang, D. Wungguli, N. Imansyah Yahya, I. K. Hasan, and S. Nurmardia
Abdussamad, “Implementasi Bidirectional LSTM Dengan Word Embedding FastText
Dalam Analisis Sentimen Ulasan Pengguna Aplikasi Maxim,” Riset Mahasiswa
Matematika, vol. 4, no. 5, pp. 204–217, 2025, doi: 10.18860/jrmm/v4i5.33358.
[3] A. O. Indarso, H. N. Irmanda, and R. Astriratma, “Sentiment Analysis of Cryptocurrency
Exchange Application on Twitter Using Naïve Bayes Classifier Method,” Jurnal
Informatika dan Teknologi Informasi, vol. 20, no. 1, pp. 15–30, 2023, doi:
10.31515/telematika.v20i1.9044.
[4] M. R. Haziq, Y. Sibaroni, and S. S. Prasetyowati, “Word Embedding Optimization In
Sentiment Analysis Of Reviews On Mytelkomsel App Using Long Short-Term Memory
And Synthetic Minority Over-Sampling Technique,” Jurnal Teknik Informatika (Jutif),
vol. 5, no. 6, pp. 1581–1589, Dec. 2024, doi: 10.52436/1.jutif.2024.5.6.2498.
[5] U. Khairani, V. Mutiawani, and H. Ahmadian, “Pengaruh tahapan preprocessing terhadap
model Indobert dan Indobertweet untuk mendeteksi emosi pada komentar akun berita
Instagram,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 11, no. 4, pp. 887–894,
2024, doi: 10.25126/jtiik.1148315.
[6] Sutriawan, Siti Mutmainnah, Teguh Ansyor Lorosae, and Sahrul Ramadhan, “Model Text
Embedding dan TF-IDF+Ngram untuk Meningkatkan Kinerja Algoritma Binary
Classifier pada Klasifikasi SMS Palsu,” Jurnal Sistem Informasi Triguna Dharma (JURSI
TGD), vol. 4, no. 1, pp. 55–64, Jan. 2025, doi: 10.53513/jursi.v4i1.10582.
[7] M. Liebenlito, A. A. Yesinta, and M. I. S. Musti, “Deteksi Clickbait pada Judul Berita
Online Berbahasa Indonesia Menggunakan FastText,” Journal of Applied Computer
Science and Technology, vol. 5, no. 1, pp. 56–62, Mar. 2024, doi:
10.52158/jacost.v5i1.655.
[8] Nailah Azzahra, Merry Dwi Handayani, and Awwaliyah Aliyah, “Evaluasi Kinerja AI
berbasis Recurrent Neural Network (RNN) dalam Mengidentifikasi Ancaman Phising
pada URL Website,” Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi, vol.
3, no. 3, pp. 15–37, Jun. 2025, doi: 10.62951/bridge.v3i3.485.
[9] M. Krichen and A. Mihoub, “Long Short-Term Memory Networks: A Comprehensive
Survey,” AI, vol. 6, no. 9, p. 215, Sep. 2025, doi: 10.3390/ai6090215.
[10] G. Van Houdt, C. Mosquera, and G. Nápoles, “A review on the long short-term memory
model,” Artif. Intell. Rev., vol. 53, no. 8, pp. 5929–5955, Dec. 2020, doi:
10.1007/s10462-020-09838-1.
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