Multivariat Support Vector Regression untuk Peramalan Permintaan Suku Cadang Intermittent pada Industri Perkeretaapian: Multivariate Support Vector Regression for Intermittent Spare Parts Demand Forecasting in the Railway Industry

Syayid Al Afghoni; Iwan Vanany

doi:10.57152/malcom.v6i2.2549

Authors

Syayid Al Afghoni Institut Teknologi Sepuluh Nopember
Iwan Vanany Institut Teknologi Sepuluh Nopember

DOI:

https://doi.org/10.57152/malcom.v6i2.2549

Keywords:

Intermittent, Peramalan Permintaan, Suku Cadang, Support Vector Regression (SVR)

Abstract

Manajemen persediaan suku cadang dalam industri perkeretaapian menghadapi tantangan kompleks akibat pola permintaan yang bersifat intermittent. Ketidakakuratan dalam meramalkan kebutuhan komponen perawatan sering kali berujung pada risiko stockout yang menghambat operasional atau overstock yang membebani biaya penyimpanan. Penelitian ini bertujuan untuk meningkatkan akurasi peramalan suku cadang dengan mengembangkan model Support Vector Regression (SVR) yang mengintegrasikan variabel eksogen berupa frekuensi jadwal perawatan dan klasifikasi umur armada. Menggunakan data historis pemakaian suku cadang perawatan kereta di workshop perawatan PT KAI periode 2020–2024, kinerja model SVR dievaluasi dan dibandingkan dengan metode Croston serta Random Forest. Hasil pengujian menunjukkan bahwa model SVR berbasis kernel RBF mampu menangani volatilitas data secara efektif dengan menghasilkan tingkat kesalahan terendah (MAE 8,424 dan MASE 0,449). Model ini terbukti superior dibandingkan metode Croston yang cenderung under-forecasting dan Random Forest yang kurang responsif terhadap nilai ekstrem. Temuan ini mengindikasikan bahwa integrasi informasi siklus perawatan dan profil usia armada secara signifikan memperbaiki kemampuan generalisasi model pada data yang fluktuatif. Secara manajerial, penerapan model ini memungkinkan perencanaan inventori yang lebih proaktif, mendukung optimalisasi safety stock, dan menjamin ketersediaan suku cadang untuk keandalan armada kereta api

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