Peramalan Kebutuhan Saldo K_One Top Up Menggunakan Algoritma Long-Short Term Memory(LSTM) Berdasarkan Data Time Series

Authors

  • Rachmad Abadi Universitas Nusantara PGRI Kediri Indonesia
  • Ardi Sanjaya Universitas Nusantara PGRI Kediri Indonesia
  • Ahmad Bagus Setiawan Universitas Nusantara PGRI Kediri Indonesia

DOI:

https://doi.org/10.29407/x9nzc890

Abstract

Pengelolaan saldo yang akurat menjadi kunci dalam menjaga kelancaran layanan bisnis digital seperti top up pulsa, voucher game, dan e-wallet. K_One Top Up masih menerapkan sistem manual dalam mengelola saldo, sehingga rentan terhadap kesalahan dan inefisiensi. Penelitian ini bertujuan untuk meramalkan kebutuhan saldo harian menggunakan algoritma Long Short-Term Memory (LSTM) berbasis data time series. Model dibangun dengan dua lapisan LSTM bertingkat dan satu lapisan output, serta diuji melalui beberapa konfigurasi neuron dan epoch. Hasil pengujian menunjukkan bahwa LSTM mampu mengenali pola tren saldo dengan cukup baik, khususnya pada produk dengan fluktuasi moderat seperti pulsa. Konfigurasi terbaik memberikan nilai evaluasi akurasi yang paling rendah, menunjukkan potensi LSTM dalam membantu pengambilan keputusan pengelolaan saldo secara prediktif. Hasil penelitian ini penting untuk meningkatkan efisiensi operasional dan kualitas layanan K_One Top Up melalui sistem peramalan yang andal.

Keywords:

LSTM, Deep Learning, Time Series, Peramalan

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Published

2025-07-10

Issue

Vol. 9 No. 3 (2025): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2025

Section

Articles

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Copyright (c) 2025 Rachmad Abadi, Ardi Sanjaya, Ahmad Bagus Setiawan

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How to Cite

Peramalan Kebutuhan Saldo K_One Top Up Menggunakan Algoritma Long-Short Term Memory(LSTM) Berdasarkan Data Time Series. (2025). Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 9(3), 2046-2055. https://doi.org/10.29407/x9nzc890