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Prediksi Sentimen dan Pemodelan Topik dari Ulasan Aplikasi Identitas Kependudukan Digital

Primandika Hakiki(1*),Dhian Satria(2),Amalia Anjani Arifiyanti(3)
(1) UPN "Veteran" Jawa Timur
(2) UPN "Veteran" Jawa Timur
(3) UPN "Veteran" Jawa Timur
(*) Corresponding Author
DOI : 10.35889/jutisi.v14i1.2777

Abstract

In this digital era, the Indonesian government is digitizing services, for example, the Digital Population Identity (IKD) in 2023 as a continuation of the Identity card (KTP). The IKD application has received mixed reviews on the Google PlayStore and Apple AppStore. From these various reviews, sentiment analysis can be carried out to determine the level of user satisfaction, as well as topic modeling to determine topics that are frequently discussed by users. This study aims to predict sentiment using the Naive Bayes and PNN methods, and topic modeling using the LDA method. The results of sentiment prediction show that the fourth scenario has the highest accuracy with 97.12%, and positive and negative f1-scores of 97% each. The results of topic modeling show the best coherence for positive reviews on topic 2 (0.4076) and for negative reviews on topic 10 (0.5564). Interpretation of the results shows that positive reviews include identity verification, application development suggestions, ease of use, KTP digitization, and user experience, while negative reviews are related to technical constraints, access and installation, network constraints, features and data security, and application registration.

Keywords: Digital population identity; Sentiment predict; Topic modelling; Naïve Bayes; Probabilistic Neural Network

 

Abstrak

Di era digital ini, pemerintah Indonesia melakukan digitalisasi layanan, contohnya yaitu Identitas Kependudukan Digital (IKD) pada tahun 2023 sebagai kelanjutan dari Kartu Tanda Penduduk (KTP). Aplikasi IKD mendapat ulasan beragam di Google PlayStore dan Apple AppStore. Dari beragam ulasan tersebut, maka dapat dilakukan analisis sentimen untuk mengetahui tingkat kepuasan pengguna, serta pemodelan topik untuk mengetahui topik yang sering dibahas oleh pengguna. Penelitian ini bertujuan untuk memprediksi sentimen menggunakan metode Naive Bayes dan PNN, serta pemodelan topik menggunakan metode LDA. Hasil prediksi sentimen menunjukkan skenario keempat memiliki akurasi tertinggi dengan 97,12%, serta f1-score positif dan negatif masing-masing 97%. Hasil pemodelan topik menunjukkan koherensi terbaik untuk ulasan positif pada topik 2 (0,4076) dan untuk ulasan negatif pada topik 10 (0,5564). Interpretasi hasil menunjukkan bahwa ulasan positif mencakup verifikasi identitas, saran pengembangan aplikasi, kemudahan penggunaan, digitalisasi KTP, dan pengalaman penggunaan, sedangkan ulasan negatif terkait kendala teknis, akses dan instalasi, kendala jaringan, fitur dan keamanan data, dan pendaftaran aplikasi.

 

Keywords


Identitas Kependudukan Digital; Prediksi sentimen; Pemodelan topik; Naïve Bayes; Probabilistic Neural Network

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