Optimasi Random Search Pada K-Means Dan Agglomerative Clustering Untuk Pengelompokan Pangan Berdasarkan Profil Gizi

Hardiansyah Hardiansyah; Muhammad Iqbal; Ifan Prihandil

doi:10.69693/ijmst.v4i3.13024

Authors

Hardiansyah Hardiansyah Universitas Indonesia Membangun
Muhammad Iqbal Universitas Indonesia Membangun
Ifan Prihandil Universitas Indonesia Membangun

DOI:

https://doi.org/10.69693/ijmst.v4i3.13024

Keywords:

Agglomerative Clustering, Komposisi Pangan, K-Means, Profil Gizi, Random Search

Abstract

Basis data komposisi pangan memiliki dimensi gizi yang heterogen dan dapat mengandung nilai hilang, sehingga pengelompokan yang hanya mengandalkan parameter baku berisiko menghasilkan struktur klaster yang tidak stabil atau tidak substantif. Penelitian ini membandingkan K-Means dan Agglomerative Clustering dengan Ward linkage untuk mengelompokkan pangan berdasarkan profil gizi, serta mengoptimalkan konfigurasi keduanya melalui randomized hyperparameter search. Dataset awal terdiri atas 1.164 observasi pangan dan 24 atribut. Setelah penghapusan lima duplikasi identik, analisis menggunakan 1.159 observasi dan delapan atribut dengan kelengkapan memadai, yaitu air, energi, protein, lemak, karbohidrat, kalsium, fosfor, dan besi. Nilai hilang diimputasi dengan median dan data distandarisasi. Pencarian acak mengevaluasi jumlah klaster, transformasi, metode penskalaan, serta parameter spesifik algoritma dengan kendala ukuran klaster minimum 2% agar solusi tidak didominasi klaster pencilan. Evaluasi menggunakan Silhouette, Davies-Bouldin, Calinski-Harabasz, dan stabilitas bootstrap berbasis Adjusted Rand Index. K-Means terbaik menghasilkan lima klaster dengan Silhouette 0,4650, Davies-Bouldin 1,1714, Calinski-Harabasz 434,8470, dan stabilitas bootstrap rata-rata 0,9545. Agglomerative-Ward menghasilkan empat klaster dengan nilai masing-masing 0,4213, 1,4073, 376,0538, dan 0,8390. K-Means membentuk lima profil utama: tinggi air-energi rendah, karbohidrat-energi tinggi, protein tinggi, lemak-energi sangat tinggi, serta mineral-protein sangat tinggi. Hasil menunjukkan bahwa K-Means lebih kompak dan stabil, sedangkan kesepakatan antar model tetap tinggi dengan Adjusted Rand Index 0,8681. Kerangka optimasi yang menggabungkan validitas internal, batas ukuran klaster, dan stabilitas memberikan dasar pemilihan model yang lebih dapat dipertanggungjawabkan

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