Implementasi Metode Long Short-Term Memory untuk Prediksi Traffic Transaksi
DOI:
https://doi.org/10.29407/184f3174Abstract
Tingginya aktivitas transaksi produk digital pada ekosistem e-commerce menuntut efisiensi layanan yang optimal. Toko "Safa Shop" yang beroperasi di platform Shopee menghadapi kendala menentukan waktu siaga operasional akibat fluktuasi pesanan yang dinamis. Penelitian ini menerapkan algoritma Long Short-Term Memory (LSTM) untuk memprediksi volume transaksi per jam sebagai rekomendasi jadwal kerja bagi penjual. Model dilatih menggunakan data historis satu tahun dari Shopee, menghasilkan prediksi per jam untuk 24 jam ke depan. Tahapan penelitian meliputi pembersihan data, normalisasi, dan implementasi teknik sliding window. Evaluasi model menghasilkan nilai MAE sebesar 1,2890, RMSE sebesar 1,6900, dan MAPE sebesar 66,13% pada jam aktif. Pengujian fungsional dengan metode Black Box Testing menunjukkan seluruh modul sistem berjalan sesuai spesifikasi. Penelitian menyimpulkan bahwa LSTM mampu menyediakan dasar prediktif yang membantu penjual mengoptimalkan manajemen waktu operasional secara sistematis.
Keywords:
E-commerce, LSTM, Produk Digital, Sistem Prediksi, Traffic Transaksi##plugins.themes.default.displayStats.downloads##
References
[1] N. Sunendar, H. P. Putro, and R. Hesananda, "Prediksi Penjualan Aerosol Menggunakan Algoritma ARIMA, LSTM Dan GRU," INSOLOGI: Jurnal Sains dan Teknologi, vol. 4, no. 1, pp. 113-126, 2025, doi: 10.55123/insologi.v4i1.4868.
[2] I. N. Hasanah, A. Lasarudin, and W. Hasyim, "Penerapan Data Mining untuk Prediksi Penjualan Produk Terlaris Menggunakan Metode ANN dan LSTM," Jurnal Ilmu Komputer (JUIK), vol. 5, no. 2, pp. 141-154, 2025, doi: 10.31314/juik.v5i2.4180.
[3] N. L. Chusna, D. M. R. Tari, and A. Khumaidi, "Konfigurasi Hyperparameter Long Short Term Memory untuk Optimalisasi Prediksi Penjualan," Faktor Exacta, vol. 15, no. 4, pp. 290-300, 2023, doi: 10.30998/faktorexacta.v15i4.15286.
[4] B. Susilo, N. A. Ramdhan, and O. S. Bachri, "Penerapan Algoritma K-Nearest Neighbor untuk Prediksi Penjualan Produk Digital," MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 4, pp. 1466-1476, 2024, doi: 10.57152/malcom.v4i4.1517.
[5] K. Hidayat, W. Witanti, and E. Ramadhan, "Analisis Tren dan Prediksi Penjualan Restoran Menggunakan Model Time Series Prophet," METIK JURNAL, vol. 9, no. 2, pp. 457-467, 2025, doi: 10.47002/metik.v9i2.1101.
[6] B. Zuhri and N. H. Harani, Aplikasi Rekrutmen Karyawan Dengan Algoritma Artificial Neural Network (ANN) Dan Framework Flask. Yogyakarta: Penerbit Buku Pedia, 2023. [Online]. Available: https://books.google.co.id/books?id=yeCuEAAAQBAJ
[7] Rianto and P. I. Santosa, Data Preparation untuk Machine Learning & Deep Learning. Yogyakarta: Penerbit Andi, 2025. [Online]. Available: https://books.google.co.id/books?id=Y5U9EQAAQBAJ
[8] A. A. Anandari, G. Harsono, and A. F. Wadjdi, Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) Aplikasi Penginderaan Jauh untuk Kelautan & Perikanan Laut Tangkap. Sukabumi: CV Jejak (Jejak Publisher), 2024. [Online]. Available: https://books.google.co.id/books?id=9j0eEQAAQBAJ
[9] N. Yudistira, Deep Learning: Teori, Contoh Perhitungan, dan Implementasi. Yogyakarta: Deepublish, 2024. [Online]. Available: https://books.google.co.id/books?id=G6cTEQAAQBAJ
[10] D. Utomo and T. F. Utomo, Deep Learning Dasar Teori dan Implementasi dengan Python dan Keras. Yogyakarta: Diandra Kreatif, 2025. [Online]. Available: https://books.google.co.id/books?id=nqmQEQAAQBAJ
[11] N. Sriminarti, Y. P. Yustisi, and R. I. Hariman, Buku Ajar E-Commerce. Jambi: PT. Sonpedia Publishing Indonesia, 2024. [Online]. Available: https://books.google.co.id/books?id=3v8REQAAQBAJ
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Copyright on any article is retained by the author(s).
- The author grants the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
- The article and any associated published material is distributed under the Creative Commons Attribution-ShareAlike 4.0 International License
