Application of fuzzy time series method to determine medical equipment inventory

Authors

  • Rozai Iskandar Universitas Islam Negeri Sumatera Utara, Indonesia
  • Mhd. Furqan Universitas Islam Negeri Sumatera Utara, Indonesia

DOI:

https://doi.org/10.35335/mandiri.v14i1.417

Keywords:

Forecasting, Fuzzy Time Series, Inventory Planning, MAPE, Medical Equipment

Abstract

This study applied the Fuzzy Time Series (FTS) method to forecast monthly stock requirements for medical equipment at PT. Karya Metropolis. The FTS process including interval determination, fuzzification, fuzzy rule formation, and defuzzification successfully identified historical patterns in sales data and produced predictions closely aligned with actual values. Forecast results indicated the next month’s needs for several items, such as 154.5 units of gauze rolls, 129 units of leukocrepe, 26.5 units of hypafix, 25 liters of 95% alcohol, 487.5 oxygen nebulizer masks, 109.5 units of Vaseline swabs, and 61 Maxter gloves. Forecast accuracy was assessed using Mean Absolute Percentage Error (MAPE), where most items showed low error rates, including gauze rolls (6.16%), Vaseline swabs (7.21%), and Maxter gloves (9.28%). However, the oxygen nebulizer mask showed a higher MAPE value of 47.28%, indicating a need for method refinement or integration with other approaches for that item. Overall, the FTS method proved effective in supporting accurate, efficient, and measurable stock planning decisions for medical supplies.

References

Ahmad, J. (2024). Strategic Planning : Navigating Uncertainty in Business Management. Journal of Management & Social Science, 02(01), 14.

Arisandi, A., & Hafid, H. (2024). Akurasi Model Prediksi Menggunakan Metode Automatic Clustering Fuzzy Time Series pada Indeks Harga Konsumen di Kota Makassar. Journal of Mathematics: Theory and Applications, 6(1), 97–103. https://doi.org/10.31605/jomta.v6i1.3621

Azis, A., Zy, A. T., & Sunge, A. S. (2024). Prediksi Penjualan Obat Dan Alat Kesehatan Terlaris Menggunakan Algoritma K-Nearest Neighbor. Jurnal Teknologi Dan Sistem Informasi Bisnis, 6(1), 117–124. https://doi.org/10.47233/jteksis.v6i1.1078

Chukwuma-Eke, E. C., Ogunsola, O. Y., & Isibor, N. J. (2022). A Conceptual Approach to Cost Forecasting and Financial Planning in Complex Oil and Gas Projects. International Journal of Multidisciplinary Research and Growth Evaluation., 3(1), 819–833. https://doi.org/10.54660/.ijmrge.2022.3.1.819-833

Garcia, P. A., & Adams, J. (2023). Driven Decision Making: Leveraging Analytics and AI for-Data Strategic Advantage. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7363988&isnumber=7363706

Hafiz, M. A., & Sriani. (2023). Penerapan Logika Fuzzy Sugeno Untuk Optimasi Stok Biji Kopi Pada Kafe Rooster. Jurnal Fasilkom, 13(02), 165–172. https://doi.org/10.37859/jf.v13i02.5460

Hamdani, A. I., Agus Pranoto, Y., & Vendyansyah, N. (2020). Penerapan Metode Fuzzy Time Series Untuk Prediksi Penjualan Berbasis Web Pada Cv.AGVA Kota Pasuruan. JATI (Jurnal Mahasiswa Teknik Informatika), 4(1), 35–41. https://doi.org/10.36040/jati.v4i1.2433

Hayuningtyas, R. Y., & Sari, R. (2021). Aplikasi Peramalan Alat Kesehatan Menggunakan Single Moving Average. Jurnal Infortech, 3(1), 40–45. https://doi.org/10.31294/infortech.v3i1.10397

Ipan, Syaripuddin, & Nohe, D. A. (2022). Perbandingan Model Chen Dan Model Lee Pada Metode Fuzzy Time Series Untuk Peramalan Produksi Kelapa Sawit Provinsi Kalimantan Timur. Prosiding Seminar Nasional Matematika, Statistika, Dan Aplikasinya, 2(1), 28–36.

Komaria, V., Maidah, N. El, & Furqon, M. A. (2023). Prediksi Harga Cabai Rawit di Provinsi Jawa Timur Menggunakan Metode Fuzzy Time Series Model Lee. Komputika : Jurnal Sistem Komputer, 12(2), 37–47. https://doi.org/10.34010/komputika.v12i2.10644

Listyaning Pangestu, S., Rahmat, U., Program, S., Matematika, F., Matematika, D., Ilmu, P., Alam, U., Pamulang, T., & Selatan, I. (2024). Model Lee Metode Fuzzy Time Series Untuk Peramalan Penjualan Obat Antibiotik. Jurnal Matematika Dan Ilmu Pengetahuan Alam, 4(2).

Lucas, P. O., Orang, O., Silva, P. C. L., Mendes, E. M. A. M., & Guimarães, F. G. (2022). A Tutorial on Fuzzy Time Series Forecasting Models: Recent Advances and Challenges. Learning and Nonlinear Models, 19(2), 29–50. https://doi.org/10.21528/lnlm-vol19-no2-art3

Muhammad Alwi Baihaqi, & Sriani. (2023). Penerapan Metode Logika Fuzzy Sugeno untuk Optimasi Persediaan Stok Masker pada Apotek Intravena. Jurnal KomtekInfo, 10, 141–149. https://doi.org/10.35134/komtekinfo.v10i4.455

Muhammad Nur, Eis Nur Rizki, Abdul Alimul Karim, & Resy Kumala Sari. (2024). Peramalan Jumlah Penumpang Domestik Pada Bandar Udara Sultan Syarif Kasim II Dengan Menggunakan Metode Winter’s Exponential Smoothing. Jurnal Teknologi Dan Manajemen Industri Terapan, 3(I), 57–66. https://doi.org/10.55826/tmit.v3ii.302

Nasution, R. Z., & Sriani, S. (2023). Fuzzy Time Series and Data Visualization for Forecasting Sales of Grocery Ingredients. PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic, 11(2), 425–434. https://doi.org/10.33558/piksel.v11i2.7383

Rahmawati, Inayati, S., Yuliana, & Hanafiah, A. (2021). Prediction of the number of participants bpjs recipient of assistance budget using the fuzzy time series cheng method. Barekeng, 15(2), 373–384. https://doi.org/10.30598/barekengvol15iss2pp373-384

Ritonga, S. H. F., & Armansyah. (2025). Analisis Fuzzy Tsukamoto Untuk Penentuan Hunian Kos Berdasarkan Preferensi Individu. TEKNIKA: Jurnal Ilmiah Bidang Ilmu Rekayasa, 19(1), 59–70.

Sari, F., Mahmud, S. F., & Faisal, R. (2023). Sistem Optimalisasi Pengadaan Alat Kesehatan Menggunakan Metode Fuzzy Time Series. Jurnal Media Informatika Budidarma, 7(4), 1766. https://doi.org/10.30865/mib.v7i4.6405

Setiawan, I. (2021). Rancang Bangun Aplikasi Peramalan Persediaan Stok Barang Menggunakan Metode Weighted Moving Average (Wma) Pada Toko Barang Xyz. Jurnal Teknik Informatika, Vol. 13, No. 3, Agustus 2021, 13(3), 1–9.

Sunarsi, D. (2023). Strategi pemasaran berbasis digital. 3(2), 2.

Wantoro, A., Verdian, A., Rusliyawati, R., & Utami, Y. T. (2023). Penerapan Logika Fuzzy Dengan Fis Mamdani Untuk Kontrol Volume Televisi. Jurnal Teknik Dan Sistem Komputer, 4(1), 38–48. https://doi.org/10.33365/jtikom.v4i1.2693

Yolanda, R., Rahmi, D., Kurniati, A., & Yuniati, S. (2024). Penerapan Metode Triple Exponential Smoothing dalam Peramalan Produksi Buah Nenas di Provinsi Riau. Jurnal Teknologi Dan Manajemen Industri Terapan, 3(I), 1–10. https://doi.org/10.55826/tmit.v3ii.285

Yudha, F. A., & Putri, R. A. (2024). Penerapan metode fuzzy time series dalam prediksi produksi kulit pie. IJAI, 16(2), 39–55.

Zellner, M., Abbas, A. E., Budescu, D. V., & Galstyan, A. (2021). A survey of human judgement and quantitative forecasting methods. Royal Society Open Science, 8(2). https://doi.org/10.1098/rsos.201187

Zufria, I., Fadhillah, N., Islam, U., & Sumatera, N. (2024). PREDIKSI PENJUALAN IKAN DENGAN METODE FUZZY. 4307(August), 1097–1102.

Downloads

Published

2025-07-15

How to Cite

Iskandar, R., & Furqan, M. (2025). Application of fuzzy time series method to determine medical equipment inventory . Jurnal Mandiri IT, 14(1), 48–56. https://doi.org/10.35335/mandiri.v14i1.417