MODEL SKORING ADAPTIF KINERJA UMKM BERBASIS KPI MENGGUNAKAN QUANTILE CLIPPING DAN DATA-DRIVEN WEIGHTING

Abdul Halim; Nurul Chafid; Mochammad Darip

doi:10.46764/teknimedia.v7i1.368

Abdul Halim Ilmu Komputer, Universitas Bina Bangsa
Nurul Chafid Ilmu Komputer, Universitas Bina Bangsa
Mochammad Darip Universitas Bina Bangsa

DOI: https://doi.org/10.46764/teknimedia.v7i1.368

Kata Kunci: UMKM, KPI, quantile clipping, adaptive weighting, data-driven scoring, robust normalization

Abstrak

Monitoring kinerja Usaha Mikro, Kecil, dan Menengah (UMKM) merupakan tantangan bagi pemerintah daerah karena jumlah pelaku usaha yang besar, heterogenitas skala usaha, serta variasi indikator finansial yang sering mengandung nilai ekstrem. Kondisi tersebut menyebabkan evaluasi berbasis Key Performance Indicators (KPI) menjadi rentan bias apabila menggunakan normalisasi dan pembobotan konvensional. Penelitian ini bertujuan mengembangkan model skoring adaptif kinerja UMKM berbasis KPI yang robust terhadap outlier dan lebih objektif dalam pembobotan indikator. Metode yang diusulkan mengintegrasikan quantile clipping pada persentil P5–P95 untuk menstabilkan distribusi KPI, dilanjutkan normalisasi min–max ke rentang 0–100. Selanjutnya, bobot KPI ditentukan secara data-driven menggunakan standar deviasi (adaptive weighting) untuk merepresentasikan kontribusi indikator berdasarkan variasi aktual data. Eksperimen dilakukan pada dataset 1000 UMKM Kota Serang menggunakan tiga KPI utama, yaitu ROI, Profit Margin, dan Growth Rate. Hasil menunjukkan bahwa bobot adaptif yang diperoleh adalah ROI 0,308, Profit Margin 0,353, dan Growth Rate 0,339. Perbandingan ranking antara fixed weighting dan adaptive weighting menghasilkan korelasi Spearman 0,9879, serta terjadi perubahan 2 entitas pada Top-10. Temuan ini. menunjukkan bahwa metode adaptif mempertahankan stabilitas ranking sekaligus meningkatkan objektivitas evaluasi. Model yang diusulkan efisien secara komputasi dan berpotensi diterapkan pada sistem monitoring kinerja berbasis multi-indikator.

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