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All Journal Brilliance: Research of Artificial Intelligence
Herfia Rhomadhona
Politeknik Negeri Tanah Laut, Indonesia
Decision Sciences, Operations Research & Management Mathematics Other

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Comparative Analysis of K-Means, PSO-KMeans and Butterfly Optimization Algorithm for Road Damage Clustering Herfia Rhomadhona; Widiya Astuti Alam Sur; Norminawati Dewi; Winda Aprianti; Jaka Permadi
Brilliance: Research of Artificial Intelligence Vol. 6 No. 2 (2026): Brilliance: Research of Artificial Intelligence, Article Research May 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i2.8389

Abstract

Road damage on access routes to coastal tourism areas in Tanah Laut Regency, South Borneo has become a critical issue affecting travel safety and tourist visits. Various types of pavement distress such as cracking, depression, bump and sags, patching and potholes, polished aggregat, rutting, and swelling create complex data patterns that require robust analytical methods. This study adopts a data-driven approach to compare the performance of three clustering algorithms K-Means, Hybrid PSO–KMeans, and the Butterfly Optimization Algorithm (BOA) to determine the optimal grouping structure of road damage data. The dataset consists of seven types of road distress obtained from field surveys across three coastal locations. Data preprocessing was carried out through normalization and standardization to ensure consistency in scale, followed by clustering analysis with varying numbers of clusters (k = 2 to 7). The Silhouette Coefficient was used to evaluate clustering performance and determine the optimal number of clusters. The results show that the optimal clustering structure is achieved at k = 3, representing three levels of road damage severity: minor, moderate, and severe. Among the evaluated methods BOA produced the highest Silhouette Score of 0.7559, outperforming Hybrid PSO–KMeans (0.6583) and K-Means (0.442) indicating more compact and well-separated clusters. These findings suggest that BOA is more effective in handling complex and heterogeneous road damage data. Practically, this approach can support data-driven decision-making in prioritizing road maintenance.
Implementation of K-Means Clustering for Social Assistance Recipients with Silhouette Score Evaluation Herfia Rhomadhona; Wiwik Kusrini; Winda Aprianti; Jaka Permadi
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.5900

Abstract

The distribution of direct social assistance continues to face several challenges, particularly regarding inaccurate targeting and unequal allocation. One of the main causes of this issue is the lack of transparency in the distribution process, where assistance is often granted to individuals with familial ties to local committee members or even government officials. As a result, the groups most in need frequently do not receive the aid they deserve. This condition is also evident in Tanjung Village, Bajuin Subdistrict, Tanah Laut Regency. The manual process of grouping prospective aid recipients contributes to inaccuracies in targeting, which in turn leads to public dissatisfaction. To address this issue, this study applies the K-Means Clustering method to group potential social assistance recipients using data from 150 individuals and three main attributes: age, occupation, and income. The method clusters the data based on the similarity of characteristics, thus supporting a more equitable and efficient identification process. The evaluation is conducted using the Silhouette Coefficient to assess the quality of clustering. The results indicate that the highest Silhouette Score is achieved at k=2k = 2k=2, with a value of 0.8278, suggesting that dividing the data into two clusters provides the most optimal configuration. The Silhouette Score tends to decrease as the number of clusters increases, confirming that adding more clusters does not necessarily improve the quality of separation.
Co-Authors Jaka Permadi Norminawati Dewi Widiya Astuti Alam Sur Winda Aprianti Wiwik Kusrini