Optimizing goods placement in logistics transportation using machine learning algorithms based on delivery data

Syawab, Moh. Husnus, Arif, Yunifa Miftachul ORCID: https://orcid.org/0000-0002-2183-0762, Nugroho, Fresy ORCID: https://orcid.org/0000-0001-9448-316X, Kusumawati, Ririen ORCID: https://orcid.org/0000-0001-6090-7219, Crysdian, Cahyo ORCID: https://orcid.org/0000-0002-7488-6217 and Almais, Agung Teguh Wibowo ORCID: https://orcid.org/0000-0001-7770-1954 (2024) Optimizing goods placement in logistics transportation using machine learning algorithms based on delivery data. Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer, 8 (2). pp. 201-209. ISSN 2598-3288

Text 23520.pdf - Published Version Available under License Creative Commons Attribution. Download (436kB)

Abstract

This study addresses the challenge of predicting the optimal placement of goods for expeditionary transportation. Efficient placement is crucial to ensure that goods are transported in a manner that maximizes space and minimizes the risk of damage. This study aims to develop a prediction system using the K-Nearest Neighbor (KNN) method, which is based on expert data from expedition vehicles. To evaluate the effectiveness of the KNN method, the researcher compared it with the Support Vector Machine (SVM) method. By doing so, they sought to determine which method delivers more accurate predictions for the optimal placement of goods. The test results revealed that the KNN method outperformed SVM, achieving a higher accuracy of 95.97% compared to SVM's 92.85%. Additionally, KNN demonstrated a lower Root Mean Square Error (RMSE) of 0.18, indicating more precise predictions, while SVM had an RMSE of 0.271. These findings suggest that KNN is the more effective method for predicting the optimal placement of goods in expeditionary transportation.

Item Type:	Journal Article
Keywords:	classification; goods placement; k-nearest neighbor (KNN); support vector machine (SVM)
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems 09 ENGINEERING > 0915 Interdisciplinary Engineering > 091599 Interdisciplinary Engineering not elsewhere classified 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing 08 INFORMATION AND COMPUTING SCIENCES > 0899 Other Information and Computing Sciences
Divisions:	Graduate Schools > Magister Programme > Graduate School of Informatics Engineering
Depositing User:	Ririen Kusumawati
Date Deposited:	15 Apr 2025 10:16

Downloads

Origin of downloads

Actions (login required)

View Item