PERAMALAN PENJUALAN ASINAN BUAH MENGGUNAKAN HOLT WINTERS PYTHON DI TOKO BERBUAH DINGIN
DOI:
https://doi.org/10.37792/jukanti.v8i2.1882Keywords:
forecasting, holt-winters, inventory, mae, mape, penjualanAbstract
ABSTRAK
Penelitian ini bertujuan untuk meramalkan penjualan asinan buah tahun 2025 menggunakan metode Holt–Winters Exponential Smoothing sebagai dasar perencanaan persediaan dan strategi bisnis. Data diperoleh dari catatan penjualan bulanan Januari 2023–Desember 2024 melalui metode dokumentasi. Tahap prapemrosesan dilakukan dengan smoothing untuk mereduksi fluktuasi acak, pembersihan outlier agar hasil lebih stabil, serta normalisasi untuk menyamakan skala data. Selanjutnya, data dibagi menjadi data latih untuk membangun model dan data uji untuk mengevaluasi akurasi. Holt–Winters dipilih karena mampu menangkap pola tren dan musiman dalam data penjualan. Evaluasi akurasi menggunakan Mean Absolute Error (MAE) dan Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan nilai MAE sebesar 20,18 dan MAPE 9,13%, yang termasuk kategori sangat akurat karena MAPE < 10%. Pola peramalan menggambarkan penurunan penjualan di pertengahan tahun serta peningkatan kembali di akhir tahun, sejalan dengan perilaku konsumsi masyarakat pada periode tertentu. Berdasarkan hasil penelitian, dapat disimpulkan bahwa metode Holt–Winters efektif digunakan untuk meramalkan penjualan asinan buah, serta bermanfaat bagi pelaku usaha dalam mengoptimalkan manajemen persediaan dan merumuskan strategi penjualan yang sesuai dengan pola musiman yang teridentifikasi.
Kata kunci : forecasting, holt-winters, inventory, mae, mape, penjualan
ABSTRACT
This study aims to forecast fruit pickle sales in 2025 using the Holt–Winters Exponential Smoothing method as a basis for inventory planning and business strategy. The data were obtained from monthly sales records from January 2023 to December 2024 through documentation. Preprocessing included smoothing to reduce random fluctuations, outlier removal to ensure stability, and normalization to unify data scaling. The dataset was then divided into training data for model building and testing data for accuracy evaluation. The Holt–Winters method was chosen because it can capture both trend and seasonal components in sales data. Model accuracy was measured using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The results show that the model achieved an MAE of 20.18 and a MAPE of 9.13%, which is categorized as highly accurate since the MAPE value is below 10%. The forecast pattern indicates a sales decline in the middle of the year followed by an increase towards the end of the year, reflecting seasonal consumer behavior. In conclusion, the Holt–Winters method is effective in forecasting fruit pickle sales and can support business owners in optimizing inventory management and designing sales strategies aligned with identified seasonal patterns.
Keywords: forecasting, holt-winters, inventory, mae, mape, sales
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