Analisa Dan Implementasi Long Short-Term Memory (LSTM) Dalam Kebutuhan Persediaan Barang di PT. Gunung Sari Indonesia
DOI:
https://doi.org/10.51903/juritek.v5i3.5578Keywords:
LSTM; Moving Average; Forecasting; Inventory Forecasting; Time Series; PT Gunung Sari.Abstract
Inventory management plays a vital role in maintaining smooth distribution and operational efficiency within companies. Inaccurate forecasting of inventory needs can cause over-stock or less-stock conditions, leading to increased costs and reduced customer satisfaction. This study applies the Long Short-Term Memory (LSTM) method to forecast inventory requirements based on historical sales data at PT. Gunung Sari Indonesia and compares it with the conventional Moving Average approach. The dataset includes sales transactions from January 2023 to December 2024. The research stages involve data preprocessing, LSTM model construction using window sizes of 14, 30, and 60 days, and performance evaluation using Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). The results indicate that the LSTM method is more adaptive to fluctuating sales patterns, while the Moving Average method provides more stable predictions for consistent sales patterns. The best MAPE values for the LSTM model range between 102–106%, while the Moving Average method yields values between 85–88%. Therefore, LSTM is preferable for datasets with irregular patterns, whereas Moving Average is more appropriate for stable sales trends.
References
[1] G. Tamami and M. Arifin, “Penggunaan LSTM dalam Membangun Prediksi Penjualan untuk Aplikasi Laptop Lens,” 2024, [Online]. Available: https://www.kaggle.com/datasets/artakusuma/laptope
[2] M. L. Ashari and M. Sadiki, “PREDIKSI DATA TRANSAKSI PENJUALAN TIME SERIES MENGGUNAKAN REGRESI LSTM,” 2020.
[3] H. Priyandanu, M. Tabrani, and Z. Mutaqin, “MANAJEMEN PERSEDIAAN BAHAN BAKU BERBASIS PADA PT. TUFFINDO NITTOKU AUTONEUM KARAWANG,” 2020.
[4] Amrullah, Affandi Egi, Riansyah Wahyu, and Sobirin, “Peramalan Penjualan Bulanan menggunakan metode Trend Moment pada Toko Suamzu Boutique,” Jurnal Sains Manajemen Informatika dan Komputer, vol. 19, no. 2, pp. 46–53, Aug. 2020, [Online]. Available: https://ojs.trigunadharma.ac.id/
[5] R. Gunawan, M. B. Dimiliu, K. Valerine, and S. P. Tamba, “ANALISIS PREDIKSI PENJUALAN TOKO FURNITUR DENGAN METODE LONG SHORT-TERM MEMORY (LSTM),” Jurnal Teknik Informasi dan Komputer (Tekinkom), vol. 7, no. 2, p. 716, Dec. 2024, doi: 10.37600/tekinkom.v7i2.1511.
[6] F. Rahman Lutfi and C. Sasongko, “Perencanaan Produksi dan Manajemen Persediaan pada Perusahaan Kue dan Roti,” Studi Akuntansi dan Keuangan Indonesia, vol. 5, no. 1, 2022.
[7] A. Mahfud Al et al., “Peramalan Data Time Series Seasonal Menggunakan Metode Analisis Spektral Berdasarkan data yang tersedia diperoleh model terbaik untuk peramalan penumpang pesawat di Bandar Udara Raden Intan II adalah Seasonal ARIMA (0,” 2020.
[8] E. Nur Cahyo and E. Susanti*, “Analisis Time Series Untuk Deep Learning Dan Prediksi Data Spasial Seismik: Studi Literatur,” J Teknol, vol. 15, no. 2, pp. 124–136, Jan. 2023, doi: 10.34151/jurtek.v15i2.3581.
[9] C. Chazar and B. E. Widhiaputra, “INFORMASI (Jurnal Informatika dan Sistem Informasi) Machine Learning Diagnosis Kanker Payudara Menggunakan Algoritma Support Vector Machine,” 2020.
[10] M. I. Faiza, Gunawan, and W. Andriani, “Tinjauan Pustaka Sistematis: Penerapan Metode Machine Learning untuk Deteksi Bencana Banjir,” Sep. 2022, doi: 10.33299/jpkop.22.2.1752.
[11] D. I. Puteri, “Implementasi Long Short Term Memory (LSTM) dan Bidirectional Long Short Term Memory (BiLSTM) Dalam Prediksi Harga Saham Syariah,” Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi, vol. 11, no. 1, pp. 35–43, May 2023, doi: 10.34312/euler.v11i1.19791.
[12] B. Masih, “Recurrent neural network model for time series analysis *,” 2024.
[13] R. Ardianto and S. Kartika Wibisono, “Analisis Deep Learning Metode Convolutional Neural Network Dalam Klasifikasi Varietas Gandum Analysis of Convolutional Neural Network Deep Learning Method in Durum Wheat Variety Classification,” vol. 6, 2023, doi: 10.56338/jks.v6i12.4938.
[14] I. G. Anjani, A. B. Saputri, A. N. P. Armeira, and D. Januarita, “Analisis Konsumsi Dan Produksi Minyak Kelapa Sawit Di Indonesia Dengan Menerapkan Metode Moving Average,” JURIKOM (Jurnal Riset Komputer), vol. 9, no. 4, p. 1014, Aug. 2022, doi: 10.30865/jurikom.v9i4.4506.
[15] P. Huriati, A. Erianda, A. Alanda, D. Meidelfi, and A. Irma Suryani, “Implementation of the Moving Average Method for Forecasting Inventory in Cv. Tre Jaya Perkasa,” 2022.
[16] R. Agung Permana and S. Sahara, “Prediksi Persediaan Barang Menggunakan Indikator Moving Average Studi Kasus Department Store Prediction of Goods Inventory Using the Moving Average Indicator, Department Store Case Study,” 2024.
[17] B. Hakim, “Analisa Sentimen Data Text Preprocessing Pada Data Mining Dengan Menggunakan Machine Learning,” JBASE - Journal of Business and Audit Information Systems, vol. 4, no. 2, Aug. 2021, doi: 10.30813/jbase.v4i2.3000.
[18] K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Global Transitions Proceedings, vol. 3, no. 1, pp. 91–99, Jun. 2022, doi: 10.1016/j.gltp.2022.04.020.
[19] T. Gori, A. Sunyoto, and H. Al Fatta, “Preprocessing Data dan Klasifikasi untuk Prediksi Kinerja Akademik Siswa,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 11, no. 1, pp. 215–224, Feb. 2024, doi: 10.25126/jtiik.20241118074.
[20] R. Oktafiani, A. Hermawan, and D. Avianto, “Pengaruh Komposisi Split data Terhadap Performa Klasifikasi Penyakit Kanker Payudara Menggunakan Algoritma Machine Learning,” Jurnal Sains dan Informatika, pp. 19–28, Jun. 2023, doi: 10.34128/jsi.v9i1.622.
[21] E. Sefry et al., “Analisis Peramalan Persediaan Barang Menggunakan Metode Moving Average Dan Exponential Smoothing Pada CV. Sanjaya Bangun Pratama,” 2024.
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