Proceedings of the International Conference on Sustainable Green Tourism Applied Science - Social Applied Science 2024 (ICoSTAS-SAS 2024)

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Optimizing Public Services through Spatial Data Analysis (SDA) and Machine Learning Towards an Inclusive Smart City in Denpasar

Authors
Kadek Jemmy Waciko1, *
1Business Administration Department, Politeknik Negeri Bali, Badung, Bali, Indonesia
*Corresponding author. Email: jemmywaciko@pnb.ac.id
Corresponding Author
Kadek Jemmy Waciko
Available Online 31 December 2024.
DOI
10.2991/978-94-6463-622-2_19How to use a DOI?
Keywords
Machine Learning; Smart City; Spatial Data Analysis (SDA)
Abstract

The rapid population growth and urbanization in Denpasar present significant challenges for local governments in delivering quality and inclusive public services. This study explores the optimization of public service delivery through the integration of spatial data analysis (SDA) and machine learning within the Smart City framework. Using a mixed-methods approach, the study combines quantitative data collected from 98 Welfare Service Recipients (WSR) through a structured questionnaire with qualitative insights gathered from interviews with key stakeholders across various government agencies. Data were analyzed using statistical techniques for the quantitative portion and thematic analysis for the qualitative portion. The findings indicate moderate awareness of challenges in public service optimization, with technological infrastructure and community engagement highlighted as critical areas needing improvement. Although the benefits of SDA and machine learning are acknowledged, challenges in implementation emphasize the need for improved training and stronger collaboration among stakeholders. This research contributes to the ongoing discourse on Smart City development by identifying key challenges and opportunities for leveraging advanced technologies to create a more efficient, inclusive, and sustainable urban environment in Denpasar.

Copyright
© 2024 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 2024 (ICoSTAS-SAS 2024)
Series
Advances in Economics, Business and Management Research
Publication Date
31 December 2024
ISBN
978-94-6463-622-2
ISSN
2352-5428
DOI
10.2991/978-94-6463-622-2_19How to use a DOI?
Copyright
© 2024 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
PY  - 2024
DA  - 2024/12/31
TI  - Optimizing Public Services through Spatial Data Analysis (SDA) and Machine Learning Towards an Inclusive Smart City in Denpasar
BT  - Proceedings of the International Conference on Sustainable Green Tourism Applied Science - Social Applied Science 2024 (ICoSTAS-SAS 2024)
PB  - Atlantis Press
SP  - 166
EP  - 175
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-622-2_19
DO  - 10.2991/978-94-6463-622-2_19
ID  - Waciko2024
ER  -