Public Sentiment Analysis on the Development of the Nusantara Capital City (IKN) using the Naive Bayes Algorithm

Authors

  • Baginda Oloan Lubis Fakultas Teknik dan Informatika, Program Studi Rekayasa Perangkat Lunak Universitas Bina Sarana Infomatika, Kota Depok Jawa Barat, Indonesia https://orcid.org/0000-0002-6839-9287
  • Benny Firmansyah Fakultas Teknik dan Informatika, Program Studi Sistem Informasi Universitas Bina Sarana Infomatika, Kota Depok Jawa Barat, Indonesia
  • Hilmy Putra Fakultas Teknik dan Informatika, Program Studi Sistem Informasi Universitas Bina Sarana Infomatika, Kota Depok Jawa Barat, Indonesia
  • Soni Irawan Fakultas Teknik dan Informatika, Program Studi Sistem Informasi Universitas Bina Sarana Infomatika, Kota Depok Jawa Barat, Indonesia
  • Ainindia Nur Alizah Fakultas Teknik dan Informatika, Program Studi Sistem Informasi Universitas Bina Sarana Infomatika, Kota Depok Jawa Barat, Indonesia
  • Muhammad Ibrahim Adnan Fakultas Teknik dan Informatika, Program Studi Sistem Informasi Universitas Bina Sarana Infomatika, Kota Depok Jawa Barat, Indonesia
  • Daniel Marulitua Situmorang Fakultas Teknik dan Informatika, Program Studi Sistem Informasi Universitas Bina Sarana Infomatika, Kota Depok Jawa Barat, Indonesia

DOI:

https://doi.org/10.51903/nx1aza77

Keywords:

Ibu Kota Nusantara, Naive Bayes, Public Sentiment, TikTok Analysis, Social Media Mining

Abstract

The development of Ibu Kota Nusantara (IKN) has become one of Indonesia’s most widely discussed national strategic projects due to its broad socio-economic and political implications. Social media platforms, particularly TikTok, play a crucial role in shaping and reflecting public opinion, making them essential data sources for real-time sentiment analysis. This study aims to analyze public sentiment regarding the progress of IKN’s development using the Naive Bayes algorithm. TikTok comments related to IKN were collected and subjected to a series of preprocessing steps, including case-folding, cleansing, tokenization, stopword removal, and stemming. The processed data were manually labeled into positive, negative, and neutral sentiment classes before being used to train a classification model. The findings demonstrate that the Naive Bayes algorithm effectively classifies public sentiment regarding IKN, as evidenced by satisfactory evaluation metrics, including accuracy, precision, recall, and F1-score. The sentiment distribution indicates diverse public perceptions: a significant portion expresses skepticism about the government's readiness and the project's feasibility. In contrast, others are optimistic about the project's long-term economic benefits. This study contributes to understanding public responses toward national infrastructure projects in the digital era. It also provides policymakers and government institutions with valuable insights to refine public communication strategies and strengthen transparency related to IKN development. Overall, the research highlights the importance of data-driven approaches for capturing public sentiment in the evolving digital landscape.

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Published

2026-01-21

Issue

Vol. 5 No. 1 (2026): January: Jurnal Ilmiah Sistem Informasi

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Copyright (c) 2026 Baginda Oloan Lubis, Benny Firmansyah, Hilmy Putra, Soni Irawan, Ainindia Nur Alizah, Muhammad Ibrahim Adnan, Daniel Marulitua Situmorang

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