Penerapan Metode Moving Average untuk Memprediksi Stok Parfum
DOI:
https://doi.org/10.32877/bt.v7i1.1563
Keywords:
Data Mining, Forecasting , Linear Regresi , Moving Average , Persediaan Stok
Abstract
Kekurangan stok parfum karena mengalami kesulitan dalam memprediksi kebutuhan stok untuk bulan berikutnya adalah hal yang tidak boleh terjadi. Proses pengadaan stok yang memerlukan waktu menambah tantangan ini, mengakibatkan dampak negatif pada omzet penjualan toko. Penelitian ini bertujuan untuk mengatasi masalah tersebut dengan menerapkan metode data mining, khususnya teknik peramalan, untuk memprediksi kebutuhan stok parfum. Moving average dipilih karena kesederhanaan dan efektivitasnya dalam menangani data yang bersifat time-series. Metode ini menghitung rata-rata penjualan dalam periode waktu tertentu untuk memberikan estimasi kebutuhan stok di masa depan. Dengan pendekatan ini, kami dapat mengurangi dampak variabilitas data harian yang mungkin disebabkan oleh faktor musiman atau peristiwa tertentu, dan fokus pada tren yang lebih stabil. Dataset yang dianalisis mencakup data penjualan toko parfum dari Maret 2022 hingga September 2022, dan tiga parameter berbeda diuji dalam penelitian ini: periode 2 bulan, 3 bulan, dan 4 bulan. Hasil penelitian menunjukkan bahwa parameter 2 bulan memberikan prediksi paling akurat, dengan nilai error Mean Absolute Percentage Error (MAPE) sebesar 13,3%, menunjukkan tingkat akurasi yang baik. Kesimpulan dari penelitian ini adalah bahwa metode moving average dengan parameter 2 bulan efektif untuk memprediksi kebutuhan stok parfum, yang dapat membantu toko dalam mengelola persediaan dengan lebih efisien dan mengurangi risiko kekurangan stok. Temuan ini menunjukkan perlunya perencanaan persediaan yang lebih baik dan penggunaan metode prediksi yang tepat dalam industri parfum. Penelitian lebih lanjut mungkin diperlukan untuk mengoptimalkan pendekatan ini atau mengeksplorasi metode alternatif.
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