Prediksi Stok Produk Sari Roti Untuk Penjualan Online Melalui Whatsapp Menggunakan Metode LightGBM dan LSTM
DOI:
https://doi.org/10.30736/informatika.v8i2.1083Keywords:
LSTM, Sari Roti, Mape, EnsembleAbstract
Penelitian ini bertujuan untuk mengetahui akurasi dari metode forecasting berdasarkan model Long short term memory network (LSTM) dan LightGBM dalam memprediksi jumlah stok yang harus disediakan di depo sari roti untuk mencukupi permintaan customer online melalui Chatbot sari roti. Data penjualan yang di teliti dalam penelitian ini adalah perusahaan PT Nippon Indosari Corpindo selama periode Juni 2021 – Oktober 2022. Penelitian ini dilakukan menggunakan data penjualan berupa lokasi penjualan, total harga diskon, harga per item, dan jumlah item terjual sebagai parameternya dan 4 jenis roti yang dijadikan sampel. Dalam penelitian ini di hitung masing-masing mean absolute percentage error (MAPE) dari forecasting LSTM dan LightGBM. Parameter yang mempengaruhi hasil prakira metode LSTM dalam penelitian ini yaitu epoch untuk model LSTM, Lags pada Mode LightGBM, dan Perbandingan rasio pada model ensemble LSTM dan LightGBM. Akurasi terbaik diperoleh dengan menggunakan model ensemble pada jenis roti tawar kupas dengan evaluas MAPE terbaik diperoleh yaitu 9,58%.
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