PENENTUANT JUMLAH CLUSTER OPTIMUM PADA SEGMEN RUTE PENERBANGAN MENGGUNAKAN DATA AUTOMATIC DEPENDENT SURVEILLANCE-BROADCAST
Abstract
Terdapat beberapa titik acuan dalam satu rute penerbangan untuk keperluan navigasi yang disebut waypoint. Pada penelitian ini penulis melakukan segmentasi untuk membagi satu rute penerbangan (Surabaya-Palu) menjadi 7 segmen yang terdiri dari 8 waypoint, dengan membuat garis imajiner secara tegak lurus melewati masing-masing waypoint. Pada tiap segmen dilakukan analisa terkait lokasi yang paling sering dilalui menggunakan pendekatan clustering.
Dalam penelitian ini penulis menggunakan algoritma clustering K-means dengan optimasi centroid yang mengimplementasikan algoritma Ant Lion Optimizer (ALO) atau disebut dengan K-means-ALO. Jumlah cluster ditentukan sebelumnya, kemudian dilakukan validasi pengelompokan internal dengan menggunakan silhouette index. Hasil metode pengelompokan diuji nilai performansinya. Hasil akhir dari jumlah cluster yang sudah ditentukan diambil nilai validitas cluster terbaik yaitu jumlah cluster yang optimum pada tiap segmen area penerbangan.
Pengujian dilakukan dengan membandingkan nilai silhouette index untuk setiap percobaan jumlah cluster terhadap kedua metode yaitu K-means dan K-means-ALO. Pada uji coba yang dilakukan, metode optimasi yang diusulkan menghasilkan validitas cluster yang lebih baik sesuai nilai silhouette index pada tiga segmen, yaitu segmen 2, 3, dan 5 akan tetapi signifikan di semua segmen berdasarkan uji statistik Analysis of Variance (ANOVA) dan uji lanjut Least Significant Difference (LSD).
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References
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