Comparison of Clustering Algorithms for Analyzing the Impact of Conflict on Poverty and Inflation
-
M Raykah Alam Ramadan
*
Universitas Sriwijaya, Palembang,
Indonesia
- Dhio Pratama Wiransyah Universitas Sriwijaya, Palembang, Indonesia
- Satria Ramadhani Universitas Sriwijaya, Palembang, Indonesia
- Rayya Ramadhan Simangunsong Universitas Sriwijaya, Palembang, Indonesia
- Ken Dhita Tania Universitas Sriwijaya, Palembang, Indonesia
- Alsella Meiriza Universitas Sriwijaya, Palembang, Indonesia
- Ahmad Rifai Universitas Sriwijaya, Palembang, Indonesia (*) Corresponding Author
Abstract
Armed conflict can have significant impacts on the social and economic conditions of a region, particularly on poverty levels and inflation. This study aims to analyze the impact of conflict on key economic indicators using a Knowledge Management System (KMS) approach and to compare the performance of clustering algorithms in identifying underlying data patterns. The research applies clustering analysis by comparing K-Means, DBSCAN, and Hierarchical Clustering algorithms to group data based on similarities in economic characteristics. The dataset used in this study consists of several indicators, including poverty levels before and during conflict, extreme poverty rates, inflation rates, GDP changes, and currency devaluation. Data preprocessing techniques such as normalization are applied to ensure comparability among variables. The evaluation of clustering performance is conducted using Silhouette Score and Davies–Bouldin Index to determine the most effective algorithm. The results show that clustering methods are able to identify distinct grouping patterns of regions based on the level of conflict impact on economic conditions. Among the evaluated algorithms, DBSCAN demonstrates superior performance in handling complex and uneven data distributions. The analysis also indicates a consistent tendency for poverty and inflation to increase during periods of conflict, highlighting the economic vulnerability of affected regions. Furthermore, the integration of clustering results into a Knowledge Management System enables the transformation of analytical outputs into structured knowledge that can support data-driven decision making. These findings are expected to contribute to the development of more effective economic policies and analytical frameworks in conflict-affected areas.
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Copyright (c) 2026 M Raykah Alam Ramadan, Dhio Pratama Wiransyah, Satria Ramadhani, Rayya Ramadhan Simangunsong, Ken Dhita Tania, Alsella Meiriza, Ahmad Rifai
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