Prediksi harga FCPO Bursa Malaysia Menggunakan Support Vector Regression Berbasis Particle Swarm Optimization: FCPO Malaysia Stock Exchange Price Prediction Using Particle Swarm Optimization-Based Support Vector Regression

Fatwa Fatahillah Fatah; Roni Andarsyah; Cahyo Prianto

doi:10.57152/malcom.v6i3.2257

Authors

Fatwa Fatahillah Fatah Universitas Logistik dan Bisnis Internasional
Roni Andarsyah Universitas Logistik dan Bisnis Internasional https://orcid.org/0009-0007-5677-8427
Cahyo Prianto Universitas Logistik dan Bisnis Internasional https://orcid.org/0000-0003-1857-1189

DOI:

https://doi.org/10.57152/malcom.v6i3.2257

Keywords:

Crude Palm OilCrude Palm Oil, Prediksi Harga, Support Vector Regression, Particle Swarm Optimization

Abstract

Crude Palm Oil (CPO) merupakan komoditas minyak nabati strategis yang harganya dipengaruhi oleh dinamika penawaran dan permintaan global serta kebijakan perdagangan. Fluktuasi yang cepat dan sulit diprediksi ini berdampak pada seluruh rantai pasok dari petani hingga industri pengolahan dan pembuat kebijakan, sehingga dibutuhkan model prediksi yang akurat dan adaptif berbasis sinyal pasar harian. Penelitian ini membangun model Support Vector Regression (SVR) yang ditingkatkan menggunakan Particle Swarm Optimization (PSO), serta membandingkannya dengan SVR tanpa optimasi. Data yang digunakan dalam penelitian ini meliputi informasi harga harian minyak sawit (FCPO) di BURSA Malaysia Derivatives dari tahun 2020 hingga 2025. Hasil menunjukkan PSO menemukan konfigurasi yang efektif, dengan biaya minimum MSE = 0,018675, dan PSO-SVR melampaui SVR default, baik secara visual maupun secara metrik. Pada skala asli diperoleh MAE = 83,939, MAPE = 1,84%, RMSE = 119,881, dan R² = 0,9818. Hasil ini menunjukkan bahwa PSO-SVR mampu meningkatkan kinerja prediksi dibandingkan dengan SVR standar. Namun, nilai R² yang sangat tinggi perlu diinterpretasikan secara hati-hati mengingat karakteristik harga CPO yang volatil serta potensi risiko overfitting. Dengan demikian, PSO-SVR dapat dipertimbangkan sebagai pendekatan pendukung untuk prediksi harga CPO berbasis data pasar harian, dengan tetap memerlukan validasi berkala sebelum diterapkan dalam pengambilan keputusan operasional.

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