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Muhammad Rafli Pasha Handoko
Universitas Islam Negeri Sumatera Utara
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Dentistry Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

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Penerapan Sistem Forecasting untuk Optimasi Ketersediaan Obat pada Klinik Pertamina Menggunakan Metode Single Moving Average Muhammad Rafli Pasha Handoko; Aninda Muliani
INSOLOGI: Jurnal Sains dan Teknologi Vol. 5 No. 2 (2026): April 2026
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v5i2.7703

Abstract

Ineffective drug inventory management may result in overstock and stock shortages that disrupt healthcare services and reduce operational efficiency. This issue was identified at Klinik Pertamina, particularly in managing Lansoprazole stock, which demonstrates fluctuating demand patterns across periods. This study aims to design and implement a web-based drug forecasting system using the Single Moving Average (SMA) method to support accurate and data-driven procurement planning. The system was developed following the Waterfall model, consisting of requirement analysis, system design, implementation, testing, and maintenance. Forecasting accuracy was evaluated using Mean Absolute Deviation (MAD) and Mean Squared Error (MSE) as quantitative error indicators. Based on nine forecasting periods, the system produced a MAD value of 291 and an MSE value of 146,184. These results indicate that the SMA method with parameter N = 3 is capable of generating relatively stable predictions despite significant demand fluctuations in certain periods. The novelty of this research lies in integrating the SMA method into a web-based information system that automatically computes forecasting results and error metrics to support practical decision-making. Therefore, the proposed system contributes to improving inventory control, minimizing stock-related risks, and enhancing operational performance at Klinik Pertamina.
Co-Authors Aninda Muliani