Analisis Sentimen Ulasan Aplikasi Identitas Kependudukan Digital Menggunakan Algoritma Logistic Regression dan K-Nearest Neighbor - Setiawan, Bagus; Baihaqi, Kiki Ahmad; Nurlaelasari, Euis; Handayani, Hanny Hikmayanti (Forum Kerjasama Pendidikan Tinggi, 2024)
The government has launched the latest innovation in data collection in the realm of population data which relies on digital technology through mobile applications using photos or QR codes which aims to reduce the use of physical prints of identity cards and the availability of blank KTPs with the aim of simplifying the administrative process and no longer requiring population documents. printing or saving in physical format such as an KTP file. In implementing the population identity application, some people feel anxious due to limited internet access, lack of knowledge about the application, as well as concerns about the security and privacy of identity data in digital format. This research aims to conduct sentiment analysis on reviews of digital population identity applications by comparing logistic regression and k-nearest neighbor algorithms. The dataset was taken using the Google Play Scraper library in Python which got 1700 raw data taken from 12-February to 26 March 2024 and then pre-processed and got 1108 clean data. The results of this research show that the comparison between the logistic regression algorithm and k-nearest neighbor algorithm shows that the k-nearest neighbor algorithm is better than the logistic regression algorithm with an accuracy result of 80.43%, a difference of 3.60% compared to k-nearest neighbor. So it can be concluded that the digital population identity application is still considered poor in its use because it has a negative sentiment of 73.9% and it can be seen in this research that the comparison results of the k-nearest neighbor algorithm prove that its performance is better than logistic regression