Prediksi Penjualan Produk Untuk Mengestimasi Kebutuhan Bahan Baku Menggunakan Perbandingan Algoritma LSTM dan ARIMA
DOI:
https://doi.org/10.22441/format.2021.v10.i2.007Keywords:
Long Short Term Memory, Predictions, Sales, Machine Learning, ArimaAbstract
Perkembangan industri pangan di dunia termasuk Indonesia yang meningkat menyebabkan masyarakat terbiasa dengan segala sesuatu yang serba cepat dan praktis. Begitu pula dengan pola masyarakat dalam mengkonsumsi makanan yang sebisa mungkin dapat diselesaikan dalam waktu singkat. Hal tersebut berdampak pada timbulnya kebutuhan masyarakat terhadap makanan yang cepat dan instant. Kehadiran fast food langsung disukai masyarakat salah satunya produk yang dibuat dengan cara handmade, perusahaan ini menjual makanan frozen food dengan berbagai varian rasa dan memiliki pelanggan dari berbagai macam daerah. Makalah ini menyajikan hasil penelitian prediksi penjualan kebutuhan bahan baku dengan menggunakan perbandingan algoritma LSTM dan Arima untuk mengestimasi rasa yang paling laku di tahun yang akan datang. Hasil penelitian menunjukkan bahwa metode LSTM dengan komposisi data latih 63% dan data uji 37% , range interval [-1,1] dan epoch sebanyak 100 menghasilkan kinerja paling baik. Metode tersebut menghasilkan nilai rata – rata RMSE proses testing model LSTM sebesar 0,22 sementara model ARIMA sebesar 60,21. Hasil persentase rata-rata kesalahan model antara nilai perhari yang terkecil menggunakan MAPE,metode LSTM sebesar 29,57% dan model ARIMA sebesar 73%. Dari kinerja yang diperoleh masing-masing model, dapat disimpulkan bahwa penggunaan model LSTM untuk kasus ini lebih baik dari pada ARIMA.Downloads
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