Increasing Performance of Multiclass Ensemble Gradient Boost uses Newton-Raphson Parameter in Physical Activity Classifying

Wungo, Supriyadi La; Aziz, Firman

doi:10.22146/ijccs.73179

Open access Jurnal SINTA 2

Increasing Performance of Multiclass Ensemble Gradient Boost uses Newton-Raphson Parameter in Physical Activity Classifying

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 16 No. 3 (2022) · pp. 291–300

DOI: 10.22146/ijccs.73179
ISSN: 2460-7258, 1978-1520
Published: 2022-07-31
Publisher: IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language: eng
License: creativecommons.org/licenses/by-sa/4.0

Abstract

The sophistication of smartphones with various sensors they have can be used to recognize human physical activity by placing the smartphone on the human body. Classification of human activities, the best performance is obtained when using machine learning methods, while statistical methods such as logistic regression give poor results. However, the weakness of the logistic regression method in classifying human activities is corrected by using the ensemble technique. This paper proposes to apply the Multiclass Ensemble Gradient Boost technique to improve the performance of the Logistic Regression classification in classifying human activities such as walking, running, climbing stairs, and descending stairs. The results show that the Multiclass Ensemble Gradient Boost Classifier by Estimating the Newton-Raphson Parameter succeeded in improving the performance of logistic regression in terms of accuracy by 29.11%.

Keywords

Collaboration network of the authors

9 authors · 17 co-authorship links · 6 across different organisations

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Universitas Pancasakti Makassar
Lambung Mangkurat University
Hasanuddin University
Institut Teknologi Bacharuddin Jusuf Habibie
Affiliation unknown

Different organisations
Same organisation
Affiliation unknown
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Co-author	Organisation	Joint articles
Jeffry Jeffry	Institut Teknologi Bacharuddin Jusuf Habibie	2
Misbahuddin Buang	Affiliation unknown	2
Nasruddin Nasruddin	Universitas Pancasakti Makassar	2
Nurul Fathanah Mustamin	Lambung Mangkurat University	1
Firmansyah Firmansyah	Universitas Pancasakti Makassar	1
Pertiwi Ishak	Universitas Pancasakti Makassar	1
Armin Lawi	Hasanuddin University	1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en-US">Increasing Performance of Multiclass Ensemble Gradient Boost uses Newton-Raphson Parameter in Physical Activity Classifying</dc:title>
	<dc:creator>Wungo, Supriyadi La</dc:creator>
	<dc:creator>Aziz, Firman</dc:creator>
	<dc:subject xml:lang="en-US">Computer and Informatics</dc:subject>
	<dc:subject xml:lang="en-US">Physical Activity; Classification; Multiclass Ensemble GradientBoost; Newton Rapshon Parameter; Smartphone</dc:subject>
	<dc:description xml:lang="en-US">The sophistication of smartphones with various sensors they have can be used to recognize human physical activity by placing the smartphone on the human body. Classification of human activities, the best performance is obtained when using machine learning methods, while statistical methods such as logistic regression give poor results. However, the weakness of the logistic regression method in classifying human activities is corrected by using the ensemble technique. This paper proposes to apply the Multiclass Ensemble Gradient Boost technique to improve the performance of the Logistic Regression classification in classifying human activities such as walking, running, climbing stairs, and descending stairs. The results show that the Multiclass Ensemble Gradient Boost Classifier by Estimating the Newton-Raphson Parameter succeeded in improving the performance of logistic regression in terms of accuracy by 29.11%.</dc:description>
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	<dc:date>2022-07-31</dc:date>
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	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 16, No 3 (2022): July; 291-300</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/73179/34378</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
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