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All Journal Jurnal Pemikiran dan Pengembangan Sekolah Dasar (JP2SD) Al-Iqtishad : Jurnal Ilmu Ekonomi Syariah (Journal of Islamic Economics) Jurnal Mirai Management Dialogia: Jurnal Studi Islam dan Sosial Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Bulletin of Computer Science Research Khaira Ummah: Jurnal Pengabdian Kepada Masyarakat Journal of Scientific Research, Education, and Technology Tuturan: Jurnal Ilmu Komunikasi, Sosial dan Humaniora JURNAL EKONOMI BISNIS DAN MANAJEMEN Jurnal Ekonomi Bisnis dan Manajemen Jurnal Media Akademik (JMA) Inovasi Kesehatan Global Vitamin: Jurnal Ilmu Kesehatan Umum Jurnal Pengabdian Masyarakat Managing: Jurnal Bisnis dan Manajemen
Muhammad Iqbal Maulana
Unknown Affiliation
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Bulletin of Computer Science Research

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Analisis Sentimen Ulasan Aplikasi Indodax Pada Google Play Store Dengan Algoritma Random Forest Muhammad Iqbal Maulana; Yusra; Muhammad Fikry; Surya Agustian; Siti Ramadhani
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.626

Abstract

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.
Co-Authors Abdul Ghofur abdur rohman Abdurrahman Faris Indriya Himawan Aliffia Fajar Agustin Andra Arema Saputra Arsila Dwi Lestari Bima Mandala Putra Cut Masdalena Devy Mareta Priyanti Sukma Dicky Wahyu Febriansyah Dinda Nur Fadila Diyah, Maulidiyah Aulia Rahma Farida Farkhatuz Zuhriyah Fata Zumala Hayati Firdaus Nur Fitrianda Henna Haati Auniah Ida Farida Ikhsan Kamelia Khasanah Lina Hikmawati Luky Mudiarti Moh Ismail Muchamad Arryan Naufal Putra Muhammad Ersya Farabi Muhammad Fikry Nailul Insani Najmi Maulita Nashr Akbar Nayla Fadillah Febrianty Nur Azizah Wandinia Ghozali Rahmad Saiful Ramadhani Ratnaningrum Z.D Rezki Rahmawati Rifqiyan Humaidillah Rosi Choirunnisa Siti Ramadhani Surya Agustian Tatha Amelia Fretika Titis Angga Rini Trisakti Handayani Winanjar Rahayu Wizar Putri Melaratna Yuni Zaroh Yusra Yusuf Waishol Zul Faizah