Thermal Comfort Prediction Accuracy with Machine Learning between Regression Analysis and Naïve Bayes Classifier

Hidayatus Sibyan (), Jozef Svajlenka, Hermawan Hermawan, Nasyiin Faqih and Annisa Nabila Arrizqi
Additional contact information
Hidayatus Sibyan: Department of Informatics Engineering, Qur’anic Science University (Universitas Sains Al-Qur’an), Jl. Hasyim Asy’ari Km. 03, Wonosobo 56351, Indonesia
Jozef Svajlenka: Department of Construction Technology and Management, Technical University of Košice, 042 00 Košice, Slovakia
Hermawan Hermawan: Department of Architecture, Qur’anic Science University (Universitas Sains Al-Qur’an), Jl. Hasyim Asy’ari Km. 03, Wonosobo 56351, Indonesia
Nasyiin Faqih: Department of Civil Engineering, Qur’anic Science University (Universitas Sains Al-Qur’an), Jl. Hasyim Asy’ari Km. 03, Wonosobo 56351, Indonesia
Annisa Nabila Arrizqi: Department of Civil Engineering, Universitas Islam Indonesia, Jl. Kaliurang Km.14,5, Yogyakarta 55584, Indonesia

Sustainability, 2022, vol. 14, issue 23, 1-18

Abstract: Various data analysis methods can make thermal comfort prediction models. One method that is often used is multiple linear regression statistical analysis. Regression analysis needs to be checked for accuracy with other analytical methods. This study compares the making of a thermal comfort prediction model with regression analysis and naïve Bayes analysis. The research method used quantitative methods for data collection regarding thermal comfort. The thermal comfort variable, consisting of eight independent variables and one dependent variable, was measured at Wonosobo High School, Indonesia. The analysis to make the prediction model was carried out with two different analyses: multiple linear regression analysis and naïve Bayes analysis. The results show that naïve Bayes is more accurate than multiple linear regression analysis.

Keywords: machine learning; thermal comfort; regression; naïve Bayes; prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/23/15663/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/23/15663/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:23:p:15663-:d:983341

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().