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Open access Jurnal SINTA 2

Classification Methods Performance On Logistic Package State Recognition

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 17 No. 4 (2023) · pp. 337–346

DOI
10.22146/ijccs.82697
ISSN
2460-7258, 1978-1520
Published
2023-10-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

In the distribution sector, logistic package experience activities, such as transport, distribution, storage, packaging, and handling. Even though those processes have reasonable operational procedures, sometimes the package experience mishandling. The mishandling is hard to identify because many packages run simultaneously, and not all processes are monitored. An Inertial Measurement Unit (IMU) is installed inside a package to collect three acceleration and rotation data. The data is then labeled manually into four classes: correct handling, vertical fall, and thrown and rotating fall. Then, using cross-validation, ten classifiers were used to generate a model to classify the logistic package status and evaluate the accuracy score. It is hard to differentiate between free-fall and thrown. The classification only uses the accelerometer data to minimize the running time. The correct handling classification gives a good result because the data pattern has few variations. However, the thrown, free-fall and rotating data give a lower result because the pattern resembles each other. The average accuracy of the ten classifications is 78.15, with a mean deviation of 4.31. The best classifier for this research is the Gaussian Process, with a mean accuracy of 94.4 % and a deviation of 3.5 %.

Keywords

Collaboration network of the authors

13 authors · 21 co-authorship links · 2 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Gadjah Mada
  • Udayana University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Novia Arum SariUniversitas Gadjah Mada2
Stanislaus Jiwandana PinasthikaUniversitas Gadjah Mada1
Raden Bagus Muhammad AdryanPutra Adhy WijayaAffiliation unknown1
Delfia Nur Anrianti PutriUniversitas Gadjah Mada1
Afiahayati AfiahayatiUniversitas Gadjah Mada1
I Nyoman Prayana TrisnaUdayana University1
Rabbani Nur KumoroUniversitas Gadjah Mada1
Audrey Shafira FattimaUniversitas Gadjah Mada1
William Hilmy SusatyoUniversitas Gadjah Mada1

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">Classification Methods Performance On Logistic Package State Recognition</dc:title>
	<dc:creator>Auzan, Muhammad</dc:creator>
	<dc:creator>Rahadian Fudholi, Dzikri</dc:creator>
	<dc:creator>Josianlie P, Paulus</dc:creator>
	<dc:creator>Fuadin, M Ridho</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Classification; Logistic; IoT; IMU; Mishandling</dc:subject>
	<dc:description xml:lang="en-US"> In the distribution sector, logistic package experience activities, such as transport, distribution, storage, packaging, and handling. Even though those processes have reasonable operational procedures, sometimes the package experience mishandling. The mishandling is hard to identify because many packages run simultaneously, and not all processes are monitored. An Inertial Measurement Unit (IMU) is installed inside a package to collect three acceleration and rotation data. The data is then labeled manually into four classes: correct handling, vertical fall, and thrown and rotating fall. Then, using cross-validation, ten classifiers were used to generate a model to classify the logistic package status and evaluate the accuracy score. It is hard to differentiate between free-fall and thrown. The classification only uses the accelerometer data to minimize the running time. The correct handling classification gives a good result because the data pattern has few variations. However, the thrown, free-fall and rotating data give a lower result because the pattern resembles each other. The average accuracy of the ten classifications is 78.15, with a mean deviation of 4.31. The best classifier for this research is the Gaussian Process, with a mean accuracy of 94.4 % and a deviation of 3.5 %.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Gadjah Mada</dc:contributor>
	<dc:date>2023-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/82697</dc:identifier>
	<dc:identifier>10.22146/ijccs.82697</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 17, No 4 (2023): October; 337-346</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/82697/37706</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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