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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Teknik Informatika (JUTIF)
Al Jauhar, Hafizh Syihabuddin
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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COMPARISON OF DBSCAN AND K-MEANS CLUSTER ANALYSIS WITH PATH-ANOVA IN CLUSTERING WASTE MANAGEMENT BEHAVIOUR PATTERNS Zuhdi, Muhammad Rizal; Al Jauhar, Hafizh Syihabuddin; Fernandes, Adji Achmad Rinaldo; Wardhani, Ni Wayan Surya
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4183

Abstract

This study aims to compare the effectiveness of DBSCAN and K-Means cluster analysis methods in clustering waste management behaviour patterns in Batu City. The data used is secondary data from previous research with a total of 395 respondents taken using the quota sampling method. DBSCAN classifies data based on density with the main parameters epsilon and MinPts, while K-Means uses the average centroid to determine the cluster. The analysis results show that DBSCAN produces a silhouette index of 0.664, which is higher than K-Means with a value of 0.574. DBSCAN also successfully identified noise as much as 10 data that did not belong to any cluster, while K-Means did not have a similar mechanism. The results of Path-ANOVA show that DBSCAN is the most optimal clustering with a more significant partition difference value. Further tests were conducted to strengthen the validation of clustering results using Path-ANOVA. Both methods produced two main clusters, with the second cluster showing higher quality in terms of maintenance, quality, and ease of use of environmental hygiene facilities. This research emphasises the importance of choosing an appropriate clustering method to ensure optimal clustering results, especially in data with complex characteristics.
APPLICATION OF DBSCAN FOR CLUSTERING SOCIETY BASED ON WASTE MANAGEMENT BEHAVIOR Al Jauhar, Hafizh Syihabuddin; Solimun, Solimun; Fitriani, Rahma
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp961-972

Abstract

This research aims to answer the challenge of identifying the characteristics of the Batu City community in waste management, where traditional clustering techniques are often suboptimal due to the presence of noise or objects that do not fit the general pattern. As a solution, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is applied, which allows the clustering of objects based on local density and detects the presence of noise or outliers in the data. DBSCAN is considered more flexible than other clustering methods, especially in clustering data that is not linear or has a non-uniform distribution. This study successfully identified three clusters of waste management behavior with a silhouette index of 0.875, indicating good cluster quality. The first cluster consists of communities with good environmental quality, active participation in the use of waste banks, and a deep understanding of 3R-based waste management. The second cluster has adequate infrastructure quality and high awareness of the potential economic benefits of waste, while the third cluster displays a pretty good level of understanding of the 3Rs and relatively good environmental quality. The results of this study provide important insights into the differences in waste management characteristics between clusters, with environmental quality proving to be a significant factor in cluster formation.
Co-Authors Fernandes, Adji Achmad Rinaldo Ni Wayan Surya Wardhani Rahma Fitriani Solimun, Solimun Zuhdi, Muhammad Rizal