Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika
Harist Abdillah
Universitas Islam Indonesia
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Optimasi Pengendalian Persediaan obat menggunakan Pendekatan Deep Learning Hybrid LSTM-GRU dan Optimasi Solver: Studi Kasus Apotek Harist Abdillah; Muhammad Ridwan Andi Purnomo
Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Vol. 4 No. 5 (2026): September : Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Inf
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/jupiter.v4i5.1551

Abstract

Pharmacy inventory management faces major challenges in balancing stockout and overstock risks caused by high demand uncertainty and the perishable nature of medicines. Conventional methods such as Reorder Point (ROP) and Economic Order Quantity (EOQ) assume stable demand, making them less responsive to real fluctuations. This study proposes a predict-then-optimize framework that integrates demand forecasting using a Hybrid Long Short-Term Memory–Gated Recurrent Unit (LSTM-GRU) model with a mathematical optimization (Solver) model for inventory control of 146 category-A medicines at Alkafi Pharmacy. Four years of historical sales data were used to train the forecasting model, and the results served as input for a perishable-inventory optimization model that minimizes total ordering, holding, shortage, and expiration costs. Testing on 52 weeks of data showed the Hybrid LSTM-GRU model achieving MAE 1.18, RMSE 5.10, MAPE 17.64%, and R² 0.52, outperforming rolling-mean, seasonal-naive, and naive baselines. Integrating the forecast with the Solver produced an order recommendation of 14,696.64 units against a forecast demand of 28,678.42 units, with zero lost sales, a simulated service level of 100%, and 6,162.80 expired units, markedly better than the manual system, which recorded 6,527.50 lost-sales units and 43,314.25 expired units per year.
Co-Authors Muhammad Ridwan Andi Purnomo