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All Journal Journal of Information Technology (JIfoTech) Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Pangestu, Andi Dwi
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Engineering Other

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PERANCANGAN SISTEM INFORMASI SIMPAN PINJAM PADA KOPERASI INPRASARLUB SALEMBA RAYA BERBASIS JAVA Pangestu, Yudha Wira; Fauzi, Ahmad; Pangestu, Andi Dwi
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 7, No 1 (2023): SEMNAS RISTEK 2023
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v7i1.6350

Abstract

Analisis Sentimen Ulasan Aplikasi iPusnas di Playstore dengan Metode Algoritma Random Forest Fauzi, Achmad; Pangestu, Andi Dwi; Solihin, Ade Kurnia; Sihombing, Redo Abeputra; Natsir, Fauzan
Journal of Information Technology Vol. 6 No. 1 (2026): Journal of Information Technology
Publisher : Institut Shanti Bhuana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46229/jifotech.v6i1.1096

Abstract

iPusnas is a digital library application developed by the National Library of Indonesia (Perpusnas RI) that allows users to borrow and read digital books via smartphones. As one of the most widely used digital library platforms in Indonesia, iPusnas has received thousands of user reviews on Google Play Store, reflecting various public sentiments about the application's performance and features. This study aims to analyze the sentiment of iPusnas user reviews on the Google Play Store using the Random Forest algorithm. Data were collected by scraping user reviews from the Play Store, followed by preprocessing steps including case folding, cleaning, normalization, tokenization, stopword removal, and stemming. Labeling was performed using the Lexicon-Based method. Feature extraction used TF-IDF (Term Frequency-Inverse Document Frequency), and data imbalance was addressed using the SMOTE (Synthetic Minority Over-sampling Technique) method. The results of the analysis showed that the Random Forest model achieved an accuracy of 73,60%, precision of 72,60%, recall of 72,10%, and F1-score of 72,30%, demonstrating its effectiveness in classifying positive and negative sentiments of iPusnas users.
Co-Authors Achmad Fauzi Fauzan Natsir Fauzi Ahmad Muda Pangestu, Yudha Wira Redo Abeputra Sihombing Solihin, Ade Kurnia