Work

Analysis and Classification of International Logistics Performance Index Using a K-Nearest Neighbors-Optimized Method

Year: 2026

Type: article

Abstract: The logistics conditions in a country play a crucial role in ensuring the smooth flow of trade, which in turn leads to increased revenue and trust from other nations. A robust logistics environment must address several key factors, such as infrastructure, customs procedures, timeliness, and others. As a member of ASEAN, Indonesia has a favorable geographic advantage from an economic perspective, which represents a potential that must be fully exploited. Logistics performance is assessed through the Logistics Performance Index (LPI), administered by the World Bank, which serves as a benchmark for evaluating logistics performance in countries around the world. In this study, logistics data was integrated with Machine Learning (ML) technology using a classification technique based on an optimized k-Nearest Neighbor (KNN) method. The results show that the KNN method with Snake Algorithm Optimization (SAO) achieved the highest accuracy of 0.961.

Source: Engineering Technology & Applied Science Research

DOI: 10.48084/etasr.20407

Authors: Teguh Herlambang, Zuraini Othman, Puguh Triwinanto, Puspandam Katias, Mohd Sanusi Azmi

Institutions: Universitas Nahdlatul Ulama Surabaya, Universiti Teknikal Malaysia Melaka, National Research and Innovation Agency

Language: English

Topic: Global Politics and Economy

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $431