SEMINAR TEKNOLOGI MAJALENGKA (STIMA)
Vol 10 (2026)

ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI IDENTITAS KEPENDUDUKAN DIGITAL MENGGUNAKAN ALGORITMA NAIVE BAYES DENGAN PEMBOBOTAN TF-IDF

Aprianti Safarani Aprianti Safarani (Fakultas Teknik, Universitas Majalengka)
Budiman (Fakultas Teknik, Universitas Majalengka)
Dadan Zaliluddin (Fakultas Teknik, Universitas Majalengka)



Article Info

Publish Date
01 Oct 2026

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.

Copyrights © 2026






Journal Info

Abbrev

stima

Publisher

Subject

Computer Science & IT Control & Systems Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

Description

Prosiding SEMINAR TEKNOLOGI MAJALENGKA (STIMA) adalah publikasi ilmiah yang memuat hasil-hasil penelitian orisinal dan terkini dari para akademisi, peneliti, dan praktisi di berbagai bidang teknik dan manajemen. Prosiding ini memiliki sifat multidisiplin, berfokus pada integrasi ilmu pengetahuan ...