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All Journal Jurnal Informatika Jurnal Ilmiah Matrik JURNAL ILMIAH INFORMATIKA Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jurnal Abdimas Mahakam METIK JURNAL Building of Informatics, Technology and Science Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika ILKOMNIKA: Journal of Computer Science and Applied Informatics JATI (Jurnal Mahasiswa Teknik Informatika) IJITEE (International Journal of Information Technology and Electrical Engineering) Journal of Intelligent Decision Support System (IDSS) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sains Komputer dan Teknologi Informasi Jurnal Teknik Informatika (JUTIF) Jurnal Informatika Terpadu International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) Jurnal Informatika Teknologi dan Sains (Jinteks) Sci-Tech Journal Sewagati: Jurnal Pengabdian Masyarakat Indonesia Safari : Jurnal Pengabdian Masyarakat Indonesia Jurnal Publikasi Teknik Informatika (JUPTI) Pandawa : Pusat Publikasi Hasil Pengabdian Masyarakat Jurnal Informatika Polinema (JIP) GEMBIRA (Pengabdian Kepada Masyarakat) Buffer Informatika JSE Journal of Science and Engineering
Naufal Azmi Verdikha
Teknik Informatika, Universitas Muhammadiyah Kalimantan Timur
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IMPLEMENTATION OF GEMINI PRE-PROCESSING ON 2024 SIREKAP REVIEWS USING THE RANDOM FOREST ALGORITHM Amru Omar; Naufal Azmi Verdikha; Muhamad Ridwan
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 5 No. 3 (2026): FEBRUARY
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.18939969

Abstract

This study aims to classify reviews of the SIREKAP 2024 application by utilizing Large Language Model (LLM)-based Gemini pre-processing, Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction, and the Random Forest algorithm as the classification method. The data used consist of user reviews obtained from the Google Play Store and categorized into five rating classes. Model performance evaluation was conducted using the 10-Fold Cross-Validation method with the Macro F1-Score metric. The testing results indicate that the lowest F1-Score achieved was 31.87%, while the highest reached 37.28%, with an overall average Macro F1-Score of 34.62%. These findings demonstrate that the Random Forest algorithm is capable of producing relatively stable classification performance through its ensemble learning mechanism, which combines multiple decision trees. However, its performance is still influenced by the imbalance in data distribution across classes. Therefore, Random Forest plays a role in maintaining prediction stability and reducing overfitting, although further development is required to improve classification performance on imbalanced review data
Co-Authors Abdul Rahim Achmad, Arda Fahmi Adhistya Erna Permanasari Ahmad Ilham Ahmad Ridhani Mubaraq Alam, Fajar Amru Omar Arbansyah Aspianur Asslia Johar Latipah Assliah Johar Latipah Bulan Suci Cahayawati Dwiagam, Junianda Haris Evitasari, Yuliana Dilla Fahrezi, Irqi Anbi Faldi Faldi Faldi Faldi Fendy Yulianto Fiqri, Mohammad Hiqmal Fitri Damayanti Habid, Reza Hasudungan, Rofilde Hendra Saputra Hery Tri Waloyo Hidayati Ramadhani, Novia Husni Thamrin Ilham, Muhammad Fauzan Nur Johar Latipah, Asslia Latipah, Asslia Johar Muhamad Ridwan Muhamad Wahyu Tirta Muhammad Najeri Al Syahrin Muhammad Taufiq Sumadi Muhammad Taufiq Sumadi Muthmainnah Muthmainnah Nuraini, Nabila Nurdiansyah, Rendy Octaviany, Dinda Nur Pranata, Aditya Pranata, Ricky Albin Rahmad Fahrozi, Mu. Aldi Reza Andriyanti Ridhoi, Reno Ririn Wahyuni Rudiman Rudiman, Rudiman Sari, Septa Intan Permata Sayekti Harits Suryawan Susanto, Muhammad Riza Radyaka Syahrandi, Akmal Taghfirul Azhima Yoga Siswa Takhta Perlawanan Putra Sinawang Teguh Bharata Adji Tri Wahyudi Trisha NurHalisha Tyas, Intan Kinanthi Damarin Wawan Joko Pranoto Wawan Joko Pranoto Wisnu Ismunandar Zayni Zayni