Sentiment Analysis of User Reviews of Mutual Fund Investment Applications on Google Playstore using Long Short Term Memory (LSTM) Algorithm
DOI:
https://doi.org/10.30865/klik.v4i1.1109Keywords:
Mutual Fund; Investment Applications; Google Playstore; Sentiment Analysis; LSTMAbstract
Mutual fund investment is increasing, as evidenced by the increasing number of mutual fund application users on the Playstore platform in Indonesia. The Financial Services Authority (OJK) reported that the number of mutual funds in Indonesia until August 2022 reached 2,193 units. In this research, the data collection used is the data scrapping method on the Google Playstore website. The result of the scrapping data is an excel-formatted document of 3000 data which is then stored and processed using the Long Short Term Memory (LSTM) model. In order to facilitate the modeling stage later, the sentiment review data must go through a text preprocessing process. To improve the performance and performance of LSTM modeling more optimally, then in this study a choice of hyperparameters was made. The hyperparameters tested are Epoch, Batch Size and Layer LSTM. The highest accuracy value on the Ajaib dataset is 99.3% which is located at epoch 32 and batch size 50, the highest accuracy value on the Bareksa dataset is 95.1% which is located at epoch 32 and batch size 50, and the highest accuracy value on the Bibit dataset is 94.9% which is located at epoch and batch size 50. So that the highest accuracy value among the three datasets is obtained by the Ajaib dataset where the accuracy reaches 99.3%. From the test results of the three parameters, it proves that there is an increase in accuracy results that is good enough to reach the highest accuracy value of 0.9933.
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References
A. Citra Pratiwi, Susi Yunarti, “Pemanfaatan Media Sebagai Saluran Untuk MemperCitra Pratiwi, Susi Yunarti, A. (2021). Pemanfaatan Media Sebagai Saluran Untuk Memperoleh Informasi Tentang Investasi. Jurnal IKRAITH-HUMANIORA, Vol 5(3), 101.oleh Informasi Tentang Investasi,” J. IKRAITH-HUMANIORA, vol. Vol 5, no. 3, p. 101, 2021.
K. D. Pramita and K. D. Hendrayana, “Perlindungan Hukum Terhadap Investor Sebagai Konsumen dalam Investasi Online,” J. Pacta Sunt Servanda, vol. 2, no. 1, pp. 24–35, 2021.
M. D. Hendriyanto, A. A. Ridha, and U. Enri, “Analisis Sentimen Ulasan Aplikasi Mola Pada Google Play Store Menggunakan Algoritma Support Vector Machine,” INTECOMS J. Inf. Technol. Comput. Sci., vol. 5, no. 1, pp. 1–7, 2022, doi: 10.31539/intecoms.v5i1.3708.
E. Daryfayi, P. Daulay, and I. Asror, “Sentimen Analisis pada Ulasan Google Play Store Menggunakan Metode Naïve Bayes,” e-Proceeding Eng., vol. 7, no. 2, p. 8400, 2020.
P. A. Permatasari, L. Linawati, and L. Jasa, “Survei Tentang Analisis Sentimen Pada Media Sosial,” Maj. Ilm. Teknol. Elektro, vol. 20, no. 2, p. 177, 2021, doi: 10.24843/mite.2021.v20i02.p01.
M. R. Fahlevvi, “Analisis Sentimen Terhadap Ulasan Aplikasi Pejabat Pengelola Informasi Dan Dokumentasi Kementerian Dalam Negeri Republik Indonesia Di Google Playstore Menggunakan Metode Support Vector Machine,” J. Teknol. dan Komun. Pemerintah., vol. 4, no. 1, pp. 1–13, 2022, doi: 10.33701/jtkp.v4i1.2701.
A. S. Talita and A. Wiguna, “Implementasi Algoritma Long Short-Term [1] A. S. Talita and A. Wiguna, ‘Implementasi Algoritma Long Short-Term Memory (LSTM) Untuk Mendeteksi Ujaran Kebencian (Hate Speech) Pada Kasus Pilpres 2019,’ MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., ,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 19, no. 1, pp. 37–44, 2019, doi: 10.30812/matrik.v19i1.495.
Y. Romadhoni, K. Fahmi, and H. Holle, “Analisis Sentimen Terhadap PERMENDIKBUD No.30 pada Media Sosial Twitter Menggunakan Metode Naive Bayes dan LSTM,” J. Inform. J. Pengemb. IT, vol. 7, no. 2, pp. 118–124, 2022.
A. R. Isnain, H. Sulistiani, B. M. Hurohman, and A. Nurkholis, “Analisis Perbandingan Algoritma LSTM dan Naive Bayes untuk Analisis Sentimen,” vol. 8, no. 2, pp. 299–303, 2022.
A. A. Mudding and Arifin A Abd Karim, “Analisis Sentimen Menggunakan Algoritma Lstm Pada Media Sosial,” J. Publ. Ilmu Komput. dan Multimed., vol. 1, no. 3, pp. 181–187, 2022, doi: 10.55606/jupikom.v1i3.517.
J. Informatika, F. Matematika, and P. Alam, “ANALISIS SENTIMEN MENGGUNAKAN ARSITEKTUR LONG SHORT-TERM MEMORY ( LSTM ) TERHADAP FENOMENA CITAYAM FASHION WEEK,” vol. 6, pp. 86–94, 2022.
M. A. Amrustian, W. Widayat, and A. M. Wirawan, “Analisis Sentimen Evaluasi Terhadap Pengajaran Dosen di Perguruan Tinggi Menggunakan Metode LSTM,” vol. 6, pp. 535–541, 2022, doi: 10.30865/mib.v6i1.3527.
Merinda Lestandy, Abdurrahim Abdurrahim, and Lailis Syafa’ah, “Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent Neural Network dan Naïve Bayes,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 4, pp. 802–808, 2021, doi: 10.29207/resti.v5i4.3308.
N. K. Gondhi, E. Sharma, A. H. Alharbi, R. Verma, and M. A. Shah, “Efficient Long Short-Term Memory-Based Sentiment Analysis of E-Commerce Reviews,” vol. 2022, 2022.
A. W. Sari, T. I. Hermanto, and M. Defriani, “Sentiment Analysis Of Tourist Reviews Using K-Nearest Neighbors Algorithm And Support Vector Machine,” Sinkron, vol. 8, no. 3, pp. 1366–1378, 2023, doi: 10.33395/sinkron.v8i3.12447.
M. F. El Firdaus, N. Nurfaizah, and S. Sarmini, “Analisis Sentimen Tokopedia Pada Ulasan di Google Playstore Menggunakan Algoritma Naïve Bayes Classifier dan K-Nearest Neighbor,” JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 5, p. 1329, 2022, doi: 10.30865/jurikom.v9i5.4774.
P. F. Supriyadi and Y. Sibaroni, “Xiaomi Smartphone Sentiment Analysis on Twitter Social Media Using IndoBERT,” JURIKOM (Jurnal Ris. Komputer), vol. 10, no. 1, pp. 19–30, 2023, doi: 10.30865/jurikom.v10i1.5540.
B. Kurniawan, A. Ari Aldino, and A. Rahman Isnain, “Sentimen Analisis Terhadap Kebijakan Penyelenggara Sistem Elektronik (Pse) Menggunakan Algoritma Bidirectional Encoder Representations From Transformers (Bert),” J. Teknol. dan Sist. Inf., vol. 3, no. 4, pp. 98–106, 2022, [Online]. Available: http://jim.teknokrat.ac.id/index.php/JTSI
M. Hadyan Baqi, Y. Sibaroni, and S. Suryani Prasetiyowati, “Comparative Analysis of Naive Bayes Model Performance in Hate Speech Detection in Media Social Twitter,” J. Ris. Komputer), vol. 10, no. 1, pp. 2407–389, 2023, doi: 10.30865/jurikom.v10i1.5493.
M. R. Faisal and U. L. Mangkurat, “Ekstraksi Fitur Menggunakan Model Word2Vec Untuk Analisis Sentimen Pada,” no. July, 2020.
M. Rusli, “Ekstraksi Fitur Menggunakan Model Word2Vec Pada Sentiment Analysis Kolom Komentar Kuisioner Evaluasi Dosen Oleh Mahasiswa,” Klik - Kumpul. J. Ilmu Komput., vol. 7, no. 1, p. 35, 2020, doi: 10.20527/klik.v7i1.296.
Moch Farryz Rizkilloh and Sri Widiyanesti, “Prediksi Harga Cryptocurrency Menggunakan Algoritma Long Short Term Memory (LSTM),” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 1, pp. 25–31, 2022, doi: 10.29207/resti.v6i1.3630.
L. Ren, J. Dong, X. Wang, Z. Meng, L. Zhao, and M. J. Deen, “A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life,” IEEE Trans. Ind. Informatics, vol. 17, no. 5, pp. 3478–3487, 2021, doi: 10.1109/TII.2020.3008223.
D. R. Alghifari, M. Edi, and L. Firmansyah, “Implementasi Bidirectional LSTM untuk Analisis Sentimen Terhadap Layanan Grab Indonesia,” J. Manaj. Inform., vol. 12, no. 2, pp. 89–99, 2022, doi: 10.34010/jamika.v12i2.7764.
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