Manajemen Inventaris Menggunakan Metode Reorder Point Yang Terintegrasi Dengan Sarima (Studi Kasus: Rajaboga)
DOI:
https://doi.org/10.29407/ne7y4c17Keywords:
Forecasting, Manajemen Inventaris, Reorder Point, SARIMA, UKMAbstract
UKM Rajaboga masih mengelola inventaris secara manual, menyebabkan ketidaktepatan prediksi stok dan risiko kehabisan bahan baku. Penelitian ini mengembangkan sistem manajemen inventaris berbasis Django yang mengintegrasikan metode Reorder Point (ROP) dengan model Seasonal Autoregressive Integrated Moving Average (SARIMA) untuk memprediksi permintaan secara otomatis. Proses pengembangan mengikuti model Waterfall, mencakup analisis kebutuhan, perancangan sistem, implementasi, pengujian, hingga penerapan. Model SARIMA dilatih menggunakan data penjualan tiga tahun dan menghasilkan akurasi tinggi dengan nilai MAE 11,92 serta MAPE 7,67. Prediksi permintaan kemudian dikonversi menjadi kebutuhan bahan baku melalui Bill of Materials dan digunakan untuk menghitung ROP yang adaptif terhadap fluktuasi permintaan dan Lead Time. Hasil penerapan menunjukkan waktu rekap stok harian menurun dari 2–3 jam menjadi kurang dari 30 menit, serta meningkatkan ketepatan restok bahan baku. Sistem ini terbukti efektif dalam meningkatkan efisiensi operasional dan mendukung pengambilan keputusan inventaris berbasis data bagi UKM pangan.
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[1] P. Trivedi, F. Akhter, A. Khan, A. A. I. Shajrawi, N. Ratkovic, dan F. N. Kausar, “The Role of Technology in Enhancing Business Processes,” Journal of Management & Educational Research Innovation, vol. 1, no. 1, hlm. 33–51, 2023.
[2] D. Mardiati dan Y. Saputra, “RANCANG BANGUN INVENTORY SYSTEM MENGGUNAKAN METODE REORDER POINT (ROP) PADA TOKO BANGUNAN IRHAS PADANG,” ZONAsi: Jurnal Sistem Informasi, vol. 5, no. 1, 2023, doi: 10.31849/zn.v5i1.12758.
[3] A. Witthayapraphakorn dan S. Jaijit, “Using simulation to determine the reorder point under uncertainty of a retail store,” International Journal of Simulation Modelling, vol. 22, no. 2, 2023, doi: 10.2507/IJSIMM22-2-630.
[4] Y. Yuningsih, A. B. Setiawan, dan D. P. Pamungkas, “ANALISIS HASIL PREDIKSI UNTUK PENGADAAN STOK BAHAN DENGAN METODE EOQ PADA TOKO O’AYAM GEPREK,” Nusantara of Engineering (NOE), vol. 7, no. 2, hlm. 232–241, 2024.
[5] N. Nassibi, H. Fasihuddin, dan L. Hsairi, “Demand Forecasting Models for Food Industry by Utilizing Machine Learning Approaches,” International Journal of Advanced Computer Science and Applications, vol. 14, Jan 2023, doi: 10.14569/IJACSA.2023.01403101.
[6] K. Szostek, D. Mazur, G. Drałus, dan J. Kusznier, “Analysis of the Effectiveness of ARIMA, SARIMA, and SVR Models in Time Series Forecasting: A Case Study of Wind Farm Energy Production,” Energies (Basel), vol. 17, no. 19, Okt 2024, doi: 10.3390/en17194803.
[7] E. Baumann, H. Buba, T. Cox, dan C. Hsu, “An Introductory Approach to Time-Series Data Preparation and Analysis,” dalam Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM, 2023. doi: 10.36001/phmconf.2023.v15i1.3561.
[8] M. Slimane, N. Bedioui, dan M. Besbes, “Assessing integration orders for SARIMA modeling A hypothesis testing approach with information criterion hyperparameter selection, case of predicting gas consumption in central Tunisia,” Energy Strategy Reviews, vol. 61, Sep 2025, doi: 10.1016/j.esr.2025.101866.
[9] V. Arumugam dan V. Natarajan, “Time Series Modeling and Forecasting Using Autoregressive Integrated Moving Average and Seasonal Autoregressive Integrated Moving Average Models,” Instrumentation Mesure Metrologie, vol. 22, no. 4, 2023, doi: 10.18280/i2m.220404.
[10] T. H. Noor, A. M. Almars, M. Alwateer, M. Almaliki, I. Gad, dan E. S. Atlam, “SARIMA: A Seasonal Autoregressive Integrated Moving Average Model for Crime Analysis in Saudi Arabia,” Electronics (Switzerland), vol. 11, no. 23, 2022, doi: 10.3390/electronics11233986.
[11] Ajeng Ayu Arumsari, D. Hartanti, dan I. Oktaviani, “Implementasi Sistem Prediksi Pemesanan Stok Barang Menggunakan Algoritma SARIMA dan Reorder Point,” Adopsi Teknologi dan Sistem Informasi (ATASI), vol. 4, no. 1, hlm. 8–18, Jun 2025, doi: 10.30872/atasi.v4i1.2900.
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