Identifikasi Klaster UMKM di Kota Bima menuju Indonesia Emas 2045 dengan Metode Support Vector Machine

Khairunnas; Alya Masitha; Rafiuddin

doi:10.51454/decode.v5i3.1359

Authors

Khairunnas Ilmu Komputer Universitas Muhammadiyah Bima
Alya Masitha Institut Teknologi Statistika dan Bisnis Muhammadiyah Semarang
Rafiuddin Ekonomi Syari’ah Universitas Muhammadiyah Bima

DOI:

https://doi.org/10.51454/decode.v5i3.1359

Keywords:

Artificial Intelligence, Data Mining, Machine Learning, Transformasi Digital, UMKM

Abstract

UMKM merupakan badan usaha yang berperan vital dalam perekonomian daerah, termasuk di Kota Bima dengan jumlah 23.936 unit pada tahun 2022. Mayoritas terdiri dari usaha mikro dengan Kecamatan Raba dan Rasanae Barat sebagai wilayah dengan konsentrasi tertinggi. Penelitian ini bertujuan mengembangkan model klasterisasi UMKM berbasis bidang usaha menggunakan pendekatan machine learning dengan kombinasi algoritma Fuzzy C-Means (FCM) dan Support Vector Machine (SVM). Data penelitian berjumlah 5.176 unit UMKM yang diperoleh dari Dinas Koperasi dan UKM, survei lapangan, serta dokumen administratif. Tahapan penelitian mencakup preprocessing data (penanganan missing values, feature selection, cleaning, normalisasi, dan reduksi dimensi), klasterisasi dengan FCM, klasifikasi menggunakan SVM, serta evaluasi model. Penentuan jumlah klaster optimum dilakukan dengan Silhouette Index dan Davies-Bouldin Index, menghasilkan tiga klaster utama: UMKM tradisional dengan investasi rendah dan digitalisasi minim, UMKM transisi dengan investasi lebih tinggi, serta UMKM digital progresif dengan adopsi teknologi dominan. Hasil evaluasi menunjukkan bahwa SVM mampu mengklasifikasikan klaster dengan akurasi 100% serta precision, recall, dan f1-score sempurna. Fitur Jumlah Investasi, TKI, dan Proxy Digitalisasi terbukti representatif dalam membedakan karakteristik UMKM. Temuan ini menunjukkan bahwa kombinasi FCM–SVM efektif untuk segmentasi UMKM serta dapat menjadi dasar kebijakan pengembangan yang tepat sasaran, mulai dari penguatan usaha tradisional hingga akselerasi transformasi digital.

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