ANALISIS PREDIKSI HARGA CABAI MENGGUNAKAN RANDOM FOREST DENGAN INTEGRASI DATA
DOI:
https://doi.org/10.29407/4qsr7b54Abstract
Fluktuasi harga cabai sering terjadi akibat pengaruh faktor produksi, kondisi cuaca, dan faktor musiman sehingga menyulitkan pelaku usaha maupun pemerintah dalam melakukan perencanaan. Penelitian ini bertujuan memprediksi harga cabai menggunakan algoritma Random Forest dengan memanfaatkan data historis harga, data agroklimat yang meliputi suhu, kelembapan, kecepatan angin, dan tutupan awan, serta faktor musiman berupa hari besar nasional. Data dari berbagai sumber diintegrasikan berdasarkan tanggal dan diproses melalui tahapan pembersihan data, transformasi variabel, pelatihan model, dan evaluasi. Kinerja model dievaluasi menggunakan Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, dan koefisien determinasi. Hasil penelitian menunjukkan bahwa model Random Forest mampu menghasilkan prediksi yang akurat dengan nilai Mean Absolute Percentage Error sebesar 7,86% dan koefisien determinasi sebesar 0,9052. Hasil tersebut menunjukkan bahwa Random Forest efektif digunakan untuk memprediksi harga cabai dan berpotensi menjadi alat bantu pengambilan keputusan dalam pengelolaan komoditas pangan.
Keywords:
agroklimat, harga cabai, machine learning, prediksi harga, Random Forest.##plugins.themes.default.displayStats.downloads##
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