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All Journal SEMINAR TEKNOLOGI MAJALENGKA (STIMA)
Aprianti Safarani Aprianti Safarani
Fakultas Teknik, Universitas Majalengka
Computer Science & IT Control & Systems Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI IDENTITAS KEPENDUDUKAN DIGITAL MENGGUNAKAN ALGORITMA NAIVE BAYES DENGAN PEMBOBOTAN TF-IDF Aprianti Safarani Aprianti Safarani; Budiman; Dadan Zaliluddin
SEMINAR TEKNOLOGI MAJALENGKA (STIMA) Vol 10 (2026)
Publisher : Universitas Majalengka

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Abstract

Research on the Digital Population Identity (IKD) application found a dominant negative sentiment of 74.81% based on an analysis of 10.308 clean reviews from 10,842 raw data collected through web scraping on the Google Play Store from January 2025 to March 2026. This indicates that the level of public satisfaction is still low. Technical obstacles during the registration process and system stability that is often disrupted after application updates are the main user concerns. This study successfully created a sentiment classification model with an accuracy of 87.88% and a negative class recall of 0.99 using the Naive Bayes algorithm and Frequency Term-Inverse Document Frequency (TF-IDF) weighting. The results of the word feature extraction recommend that the registration system architecture should be thoroughly evaluated, each software update should be thoroughly tested, and server capacity should be increased to provide a more stable and efficient user experience in the future in population administration services.
Co-Authors Budiman Dadan Zaliluddin