Analisis Hubungan Antara Jumlah Bus dan Jumlah Penumpang Menggunakan Unsupervised Learning: Analysis of Passenger and Bus Numbers Using Unsupervised Learning

Aurell Octaviona Sinaga; Rossi Passarella

doi:10.57152/malcom.v5i3.2005

Authors

Aurell Octaviona Sinaga Universitas Sriwijaya
Rossi Passarella Univeristas Sriwijaya

DOI:

https://doi.org/10.57152/malcom.v5i3.2005

Keywords:

Fuzzy C-Means, Gaussian Mixture Model, K-Means, Sepctral Clustering, Unsupervised Learning

Abstract

Hubungan antara jumlah bus dan jumlah penumpang merupakan aspek penting dalam analisis transportasi perkotaan. Namun, pola distribusi dan kelompok data yang terbentuk sering kali tidak merata, sehingga menyebabkan ketidakseimbangan dalam hubungan tersebut. Penelitian ini bertujuan untuk mengidentifikasi pola distribusi dan mengelompokkan data jumlah bus dan penumpang menggunakan pendekatan unsupervised learning. Metode yang diuji coba terdiri dari empat model yaitu K-Means, Fuzzy C-Means (FCM), Gaussian Mixture Model (GMM), dan Spectral Clustering. Keempat model tersebut dibandingkan untuk mengukur seberapa baik model dapat mengelompokkan data dan mengungkap hubungan antara jumlah bus dan jumlah penumpang. Keempat model akan dievaluasi menggunakan Calinski-Harabasz Index, Silhouette Score, dan Davies-Bouldin Index untuk menemukan klaster optimal. Berdasarkan uji coba keempat model clustering menggunakan ketiga matriks evaluasi, semua model menunjukkan klaster optimalnya adalah 2 namun model K-Means memberikan kinerja terbaik dalam mengelompokkan data karena model K-Means memiliki nilai terbaik untuk setiap metrik evaluasi tersebut. Skor model K-Means pada Silhouette Score sebesar 0.5175, nilai model K-Means pada Davies-Bouldin Index sebesar 0.7241, dan skor untuk K-Means terhadap Calinski-Harabasz Index sebesar 1414.3874. Klaster 1 merepresentasikan jumlah bus dan jumlah penumpang yang tinggi sedangkan Klaster 2 merepresentasikan jumlah penumpang dan jumlah bus yang rendah.

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