Analisis Sentimen Ulasan Aplikasi Indodax Pada Google Play Store Dengan Algoritma Random Forest
DOI:
https://doi.org/10.47065/bulletincsr.v5i4.626Keywords:
Cryptocurrency; Indodax; Analysis Sentiment; Google Play Store; Random ForestAbstract
Crypto assets have become a global phenomenon with a significant increase in the number of investors in Indonesia. Indodax, as the largest crypto asset trading platform in Indonesia, has contributed to the growth of this ecosystem and received many user reviews through the Google Play Store. With more than 5 million downloads and 100 thousand reviews, sentiment analysis is an important tool to understand user perceptions of Indodax services. The results of manual labeling show that the majority of reviews are positive (3989 reviews), while neutral and negative sentiments are 477 and 534 reviews respectively. From the research and testing that has been carried out using the Random Forest method and optimizing with Hyperparameter Tuning GridSearchCV on 4 test scenarios. The best results were obtained in Scenario 4 (3 Preprocessing Stages (Cleaning, Case Folding, and Tokenization) + Random Forest & Hyperparameter Tuning) producing the best value, with Precision 81%, Recall 64%, F1-Score 70% and Accuracy 89%. With the best parameter values ??{'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}. This study shows that every experimental model that is optimized produces a higher value than experimental model that is not optimized.
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References
M. Arbina and M. I. F Putuhena, “Tata Kelola Pembentukan Regulasi Terkait Perdagangan Mata Uang Kripto (Cryptocurrency) sebagai Aset Kripto (Crypto Asset),” Mahadi Indones. J. Law, vol. 1, no. 1, pp. 33–57, 2022, doi: 10.32734/mah.v1i1.8314.
M. D. Firgayanda and H. M. Jumhur, “Analisis Pengaruh Financial Literacy Terhadap Keputusan Investasi Di Pasar Cryptocurrency (Studi Pada Pengguna Platform Indodax),” in e-Proceeding of Management, 2022, pp. 1768–1771.
A. Kusuma, E. Ermatita, and H. N. Irmanda, “Analisis Sentimen Pada Ulasan Aplikasi Indodax di Google Play Store Menggunakan Metode Support Vector Machine,” in Prosiding Seminar Nasional Mahasiswa Bidang Ilmu Komputer dan Aplikasinya, 2022, pp. 773–782.
N. Z. Rania and R. D. Syah, “Analisis Sentimen Terhadap Aplikasi Gojek Pada Play Store Menggunakan Metode Random Forest Classifier,” J. Ilm. Inform. Komput., vol. 29, no. 2, pp. 144–153, 2024, doi: 10.35760/ik.2024.v29i2.11877.
M. R. U. Pulungan, D. E. Ratnawati, and B. Rahayudi, “Analisis Sentimen Ulasan Aplikasi PeduliLindungi dengan Metode Random Forest,” J. Pengemb. Teknol. Inf. dan Ilmu Komun., vol. 6, no. 9, pp. 4378–4385, 2022, [Online]. Available: http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/download/11582/5142
V. Fransisco and D. B. Rarasati, “Analisis Sentimen Aplikasi Polri Super App Menggunakan Algoritma Random Forest,” J. Ilm. Sains dan Teknol., vol. 8, no. 2, pp. 183–195, 2024, doi: 10.47080/saintek.v8i2.3383.
C. G. Indrayanto, D. E. Ratnawati, and B. Rahayudi, “Analisis Sentimen Data Ulasan Pengguna Aplikasi MyPertamina di Indonesia pada Google Play Store menggunakan Metode Random Forest,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 3, pp. 1131–1139, 2023, [Online]. Available: http://j-ptiik.ub.ac.id
A. Miftahusalam, H. Pratiwi, and I. Slamet, “Perbandingan Metode Random Forest dan Naive Bayes pada Analisis Sentimen Review Aplikasi BCA Mobile,” in SIPTEK?: Seminar Nasional Inovasi dan Pengembangan Teknologi Pendidikan, 2023, pp. 1–8.
D. Reinaldo and Y. F. Riti, “Analisis Sentimen Ulasan Aplikasi Genshin Impact di Play Store Menggunakan Random Forest,” in Seminar Teknik Elektro, Sistem Informasi, dan Teknik Informatika, 2024, pp. 578–586. doi: 10.31284/p.snestik.2024.5883.
T. A. U. Azmi, L. Hakim, D. C. R. Novitasari, and W. D. U. D. Utami, “Application Random Forest Method for Sentiment Analysis in Jamsostek Mobile Review,” Telemat. J. Inform. dan Teknol. Inf., vol. 20, no. 1, p. 117, 2023, doi: 10.31315/telematika.v20i1.8868.
S. Nur Adhan, G. N. A. Wibawa, D. C. Arisona, I. Yahya, and R. Ruslan, “Analisis Sentimen Ulasan Aplikasi Wattpad Di Google Play Store Dengan Metode Random Forest,” AnoaTIK J. Teknol. Inf. dan Komput., vol. 2, no. 1, pp. 6–15, 2024, doi: 10.33772/anoatik.v2i1.32.
D. N. I. Huda, C. Prianto, and R. M. Awangga, “Analisis Sentimen Perbandingan Layanan Jasa Pengiriman Kurir Pada Ulasan Play Store Menggunakan Metode Random Forest dan Descision Tree,” J. Ilm. Inform., vol. 11, no. 2, pp. 150–158, 2023, doi: 10.33884/jif.v11i02.7952.
M. F. Y. Herjanto and C. Carudin, “Analisis Sentimen Ulasan Pengguna Aplikasi Sirekap Pada Play Store Menggunakan Algoritma Random Forest Classifer,” J. Inform. dan Tek. Elektro Terap., vol. 12, no. 2, pp. 1204–1210, 2024, doi: 10.23960/jitet.v12i2.4192.
O. Manullang, C. Prianto, and N. H. Harani, “Analisis Sentimen Untuk Memprediksi Hasil Calon Pemilu Presiden Menggunakan Lexicon Based Dan Random Forest,” J. Ilm. Inform., vol. 11, no. 2, pp. 159–169, 2023, doi: 10.33884/jif.v11i02.7987.
Tangguh Prana Welas Sukma and M. R. Pribadi, “Analisis Sentimen Review Pengguna Viu Pada Play Store Dengan Algoritma Random Forest,” J. Softw. Eng. Comput. Intell., vol. 2, no. 01, pp. 9–16, 2024, doi: 10.36982/jseci.v2i01.4016.
N. C. Ramadani, “Analisis Sentimen Untuk Mengukur Ulasan Pengguna Aplikasi Mobile Legend Menggunakan Algoritma Naive Bayes, SVM, Random Fores, Decision Tree, dan Logistic Regression,” J. Sist. Inf., vol. 16, no. 1, pp. 123–138, 2024, doi: 10.18495/jsi.v16i1.152.
M. A. A. Jihad, Adiwijaya, and W. Astuti, “Analisis Sentimen Terhadap Ulasan Film Menggunakan Algoritma Random Forest,” in eProceedings of Engineering, 2021, pp. 10153–10166.
F. A. Larasati, D. E. Ratnawati, and B. T. Hanggara, “Analisis Sentimen Ulasan Aplikasi Dana dengan Metode Random Forest,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 9, pp. 4305–4313, 2022.
F. M. Rayhan et al., “Analisis Sentimen Root Cause Analisis Kepuasan,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 8, pp. 1–10, 2024.
R. Aryanti, T. Misriati, and A. Sagiyanto, “Analisis Sentimen Aplikasi Primaku Menggunakan Algoritma Random Forest dan SMOTE untuk Mengatasi Ketidakseimbangan Data,” J. Comput. Syst. Informatics, vol. 5, no. 1, pp. 218–227, 2023, doi: 10.47065/josyc.v5i1.4562.
I. Muhamad Malik Matin, “Hyperparameter Tuning Menggunakan GridsearchCV pada Random Forest untuk Deteksi Malware,” J. Multinetics, vol. 9, no. 1, pp. 43–50, 2023, doi: 10.32722/multinetics.v9i1.5578.
H. Chyntia Morama, D. E. Ratnawati, and I. Arwani, “Analisis Sentimen berbasis Aspek terhadap Ulasan Hotel Tentrem Yogyakarta menggunakan Algoritma Random Forest Classifier,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 4, pp. 1702–1708, 2022
A. A. Putri, S. Agustian, R. Abdillah, and Pizaini, “Penerapan Metode Logistic Regression Untuk Klasifikasi Sentimen Pada Dataset Twitter Terbatas,” J. Sist. Inf., vol. 7, no. 1, pp. 95–107, 2025, doi: 10.31849/zn.v7i1.24804.
S. Nanda, D. Mualfah, and D. A. Fitri, “Analisis Sentimen Kepuasan Pengguna Terhadap Layanan Streaming Mola Menggunakan Algoritma Random Forest,” J. Apl. Teknol. Inf. dan Manaj., vol. 3, no. 2, pp. 210–219, 2022, doi: 10.31102/jatim.v3i2.1592.
Y. R. Nasution, A. H. Lubis, and T. F. Eliza, “Analisis Sentimen Platform X Mengenai Pro Kontra Rekrutmen Guru Melalui Marketplace Menggunakan Metode Naïve Bayes,” Bull. Comput. Sci. Res., vol. 4, no. 6, pp. 406–713, 2024, doi: 10.47065/bulletincsr.v4i6.368.
P. Yohana, S. Agustian, and S. K. Gusti, “Klasifikasi Sentimen Masyarakat terhadap Kebijakan Vaksin Covid-19 pada Twitter dengan Imbalance Classes Menggunakan Naive Bayes,” in Seminar Nasional Teknologi Informasi, Komunikasi dan Industri, 2022, pp. 69–80. [Online]. Available: http://ejournal.uin-suska.ac.id/index.php/SNTIKI/article/view/19012
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