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Rifky Permana
Universitas Bina Sarana Informatika
Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Public Health Social Sciences Other

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SENTIMENT ANALYSIS OF DIGITAL POPULATION IDENTITY (IKD) APPLICATION USING THE NAÏVE BAYES ALGORITHM Eka Putra Pratama; Ade Setiawan; Rifky Permana; Sandra Dewi Saraswati; Tri Hartati
Akrab Juara : Jurnal Ilmu-ilmu Sosial Vol. 11 No. 2 (2026): Mei
Publisher : Yayasan Azam Kemajuan Rantau Anak Bengkalis

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Abstract

This study aims to analyze user sentiment towards the digital identity application launched by the government. The methodology employed involves collecting user reviews and comments from various social media platforms and the official application. The data is then analyzed using Natural Language Processing (NLP) techniques to identify and categorize sentiments as positive, negative, or neutral. The Naive Bayes method will be implemented using a dataset containing user reviews about a digital identity application. The analysis results indicate that the majority of user sentiments towards the application are positive, with many users appreciating the ease of access and features that simplify administrative processes. However, there are also a number of negative sentiments indicating technical issues and concerns regarding data privacy. Neutral sentiments generally relate to suggestions for improvements and enhancements to the application. The findings of this study are expected to provide constructive feedback for application developers to improve the quality and security of the digital identity application, as well as to enhance user satisfaction in the future. The study uses Google Colab, a cloud-based service provided by Google that allows users to write and execute Python code directly from the browser. The researcher used the scraping method to collect review data from IKD applications on the Play Store.
Co-Authors Ade Setiawan Ade Setiawan Ahmad Fauzi Ahmad Fauzi Ahmad Fauzi Andika Bayu Hasta Yanto Andika Bayu Hasta Yanto Andika Bayu Hasta Yanto Andry Maulana Andry maulana Ani Oktarini Sari Ari Abdilah Atang Saepudin Aulia Rizki, Safana Betaria Safitri Budi Supriyadi Damainingati, Biru Epa Dede Firmansyah Saefudin Dewi Astutik Dwi Yuni Utami Eka Fitriani Eka Putra Pratama Eka Wulansari Fridayanthie, Eka Wulansari Eni Heni Hermailani Fauzi Ahmad Muda Febby Ariyanti Herdiana Fuad Nur Hasan Herdiana, Febby Ariyanti Intan Puji Christanti Mahmud Syarif Maulana, Andry Muhammad Rivaldi Nia Kurniasih Novalian Ramuchdani Resa Zultia Reynaldi Abdillah Richardus Eko Indrajit Ricki Sastra Riska Aryanti Sandra Dewi Saraswati Savira Dwi Gustina Savira Yuniar Siti Rohana Sri Diana Sri Diana Sulistyowati, Daning Nur Syaripah Syaripah, Syaripah Tika Adilah M Tri Hartati Yaurike, Ikke