Proceedings of the International Conference on Sustainable Green Tourism Applied Science - Social Applied Science 2025 (ICOSTAS-SAS 2025)

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Optimizing Denpasar Smart City Through AI and Big Data: Predictive Analysis of Public Services Using SEM-PLS

Authors
Kadek Jemmy Waciko1, *, Ni Nyoman Teristiyani Winaya1, Rifqi Nur Fakhrurozi1
1Business Administration Department, Politeknik Negeri Bali, Bali, Indonesia
*Corresponding author. Email: jemmywaciko@pnb.ac.id
Corresponding Author
Kadek Jemmy Waciko
Available Online 14 November 2025.
DOI
10.2991/978-94-6463-882-0_14How to use a DOI?
Keywords
Artificial Intelligence; Big Data Analytics; SEM-PLS
Abstract

This study develops a predictive model to evaluate the impact of Artificial Intelligence (AI), Big Data Analytics (BDA), and Digital Governance Infrastructure (IDG) on Smart City Success (KMS) in Denpasar, Indonesia. A conceptual framework based on SEM-PLS was designed to analyze the causal relationships among latent variables, including Public Management & Decision-making (PMD), E-Government & Public Services (ELP), and the mediating role of a Knowledgeable Smart Community (KSC). Using data collected from 222 respondents through Proportional Stratified Random Sampling and validated with Slovin’s formula, the model was empirically tested. The findings confirm that AI and BDA significantly enhance PMD, while IDG strengthens ELP. Both PMD and ELP positively influence Smart City Success (KMS), and KSC acts as a key mediator. The model demonstrates strong predictive relevance (Q2 > 0) and acceptable fit (SRMR < 0.08). These results provide actionable insights for policymakers aiming to improve data-driven governance, digital service delivery, and citizen engagement. This research contributes to both academic knowledge and practical implementation by offering a replicable framework for AI and BDA integration in urban governance, supporting sustainable Smart City development in Indonesia and similar emerging regions.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Sustainable Green Tourism Applied Science - Social Applied Science 2025 (ICOSTAS-SAS 2025)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
14 November 2025
ISBN
978-94-6463-882-0
ISSN
2352-5398
DOI
10.2991/978-94-6463-882-0_14How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Kadek Jemmy Waciko
AU  - Ni Nyoman Teristiyani Winaya
AU  - Rifqi Nur Fakhrurozi
PY  - 2025
DA  - 2025/11/14
TI  - Optimizing Denpasar Smart City Through AI and Big Data: Predictive Analysis of Public Services Using SEM-PLS
BT  - Proceedings of the International Conference on Sustainable Green Tourism Applied Science - Social Applied Science 2025 (ICOSTAS-SAS 2025)
PB  - Atlantis Press
SP  - 109
EP  - 119
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-94-6463-882-0_14
DO  - 10.2991/978-94-6463-882-0_14
ID  - Waciko2025
ER  -