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All Journal Jurnal Kolaboratif Sains
Ririn Eka Aryanindra
Program Studi S1-Teknik Informatika, Universitas Duta Bangsa Surakarta, Jl. Bhayangkara No. 55 – Surakarta – Jawa Tengah
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Implementasi Algoritma SARIMA dan Metode Reorder Point dalam Sistem Prediksi Stok Barang Berbasis Web (Studi Kasus di Nasuka Print Copy Center): Implementation of the SARIMA Algorithm and the Reorder Point Method in a Web-Based Inventory Forecasting System (A Case Study at Nasuka Print Copy Center) Ririn Eka Aryanindra
Jurnal Kolaboratif Sains (Special Issue) Jurnal Kolaboratif Sains (JKS) - July 2026
Publisher : Universitas Muhammadiyah Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56338/jks.v9i7.11636

Abstract

Pengelolaan persediaan pada usaha fotocopy sering menghadapi permasalahan overstock dan stockout akibat permintaan yang fluktuatif dan ketidakpastian lead time. Penelitian ini bertujuan mengimplementasikan algoritma Seasonal Autoregressive Integrated Moving Average (SARIMA) untuk memprediksi kebutuhan stok serta mengintegrasikannya dengan metode Reorder Point (ROP) dan Safety Stock guna menentukan waktu dan jumlah pemesanan yang optimal. Hasil penelitian berupa sistem berbasis web yang mampu menghasilkan prediksi stok dan memberikan rekomendasi pemesanan ulang secara otomatis. Sistem ini diharapkan dapat meningkatkan efisiensi pengelolaan persediaan dan mendukung pengambilan keputusan yang lebih akurat. ABSTRACT Inventory management in photocopy businesses often faces issues such as overstock and stockout due to fluctuating demand and uncertainty in lead time. This study aims to implement the Seasonal Autoregressive Integrated Moving Average (SARIMA) algorithm to forecast inventory demand and integrate it with the Reorder Point (ROP) and Safety Stock methods to determine optimal ordering time and quantity. The result is a web-based system capable of generating inventory forecasts and providing automatic reorder recommendations. This system is expected to improve inventory management efficiency and support more accurate data-driven decision-making.
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