Pengembangan Sistem Prediksi Pendapatan Usaha Barbershop Menggunakan Metode Long Short-Term Memory (LSTM)
DOI:
https://doi.org/10.29407/3g4f4043Abstract
Usaha barbershop merupakan salah satu sektor jasa yang terus berkembang seiring meningkatnya kebutuhan masyarakat terhadap layanan perawatan rambut dan gaya hidup modern. Pengelolaan pendapatan yang masih dilakukan secara manual menyebabkan pemilik usaha mengalami kesulitan dalam menganalisis pola pendapatan serta memperkirakan pendapatan pada periode mendatang. Penelitian ini bertujuan mengembangkan sistem prediksi pendapatan usaha barbershop berbasis website menggunakan metode Long Short-Term Memory (LSTM). Sistem dikembangkan menggunakan model Waterfall dengan memanfaatkan Python, Streamlit, TensorFlow, dan SQLite. Data penelitian berupa transaksi pendapatan harian yang diolah melalui tahapan preprocessing, normalisasi, pelatihan model, dan evaluasi prediksi. Hasil penelitian menunjukkan bahwa sistem mampu mengelola transaksi harian, melakukan pelatihan model, menampilkan hasil prediksi, serta menghasilkan laporan pendapatan secara otomatis. Berdasarkan hasil evaluasi model diperoleh nilai Mean Absolute Error (MAE) sebesar 125.984, Root Mean Square Error (RMSE) sebesar 166.939, Mean Absolute Percentage Error (MAPE) sebesar 17,76%, dan tingkat akurasi sebesar 82,2%. Hasil tersebut menunjukkan bahwa metode LSTM mampu mempelajari pola historis pendapatan dan menghasilkan prediksi yang cukup baik sehingga dapat digunakan sebagai pendukung pengambilan keputusan pada usaha barbershop.
Keywords:
Barbershop, LSTM, Prediksi, Machine Learning, Time Series##plugins.themes.default.displayStats.downloads##
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