Analisis Clustering Konsumsi Bahan Bakar dan Emisi CO2 Kendaraan dengan K-Means, Nearest Neighbor, dan DBScan
DOI:
https://doi.org/10.61124/sinta.v3i4.471Keywords:
clustering, DBSCAN, K-means, Nearest neighbor, PCAAbstract
Konsumsi bahan bakar dan emisi CO2 kendaraan merupakan indikator penting efisiensi energi pada sektor transportasi, sehingga pengelompokan kendaraan berdasarkan karakteristik tersebut diperlukan untuk analisis dan kebijakan. Algoritme clustering memiliki asumsi yang berbeda sehingga hasilnya dapat berbeda pada data yang sama. Penelitian ini bertujuan membandingkan kualitas dan stabilitas tiga algoritme clustering, yaitu K-means, Nearest Neighbor (NN) berbasis ambang jarak, dan DBSCAN, pada dataset FuelConsumptionCo2 yang berisi 1067 kendaraan. Empat fitur numerik non-redundan distandardisasi dan direduksi dengan Principal Component Analysis (PCA) menjadi dua komponen yang menjelaskan 96,1% varians. K-means dengan k=3 digunakan sebagai partisi acuan. Ambang jarak NN serta parameter ε dan MinPts DBSCAN dipilih untuk memaksimalkan Adjusted Rand Index (ARI) terhadap partisi tersebut. Kualitas cluster dievaluasi menggunakan Silhouette, Davies–Bouldin Index, dan Calinski–Harabasz Index, sedangkan kemiripan antarpartisi dilaporkan menggunakan ARI dan Normalized Mutual Information (NMI). Hasil menunjukkan DBSCAN mencapai Silhouette tertinggi (0,589) dengan 41 titik noise, diikuti K-means (0,573) dan NN (0,537); pada himpunan titik yang sama, K-means dan DBSCAN setara. K-means dan DBSCAN cukup stabil dengan ARI bootstrap rata-rata 0,97 sedangkan NN peka terhadap urutan data. Tiga cluster utama merepresentasikan kendaraan berkonsumsi rendah, menengah, dan tinggi. Karena data membentuk kontinum, hasil lebih tepat dibaca sebagai segmentasi.
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