Total Prediction Decision Support System Bakery and Cake Production Using Mamdani Fuzzy Method

Case Study: Neko-Neko Bakery & Cake Branch Burned Stone, Deli Serdang

Authors

  • Evi Rianto STMIK Pelita Nusantara
  • Jonson Manurung STMIK Pelita Nusantara

DOI:

https://doi.org/10.35335/mandiri.v10i2.95

Keywords:

Bakery and Cake, Fuzzy Mamdani, Prediction of Total Production

Abstract

The decision support system for predicting the amount of bakery and cake production using the fuzzy mamdani method is one of the processes for determining the prediction of the amount of bakery and cake production for the next month so that the desired production amount is according to needs. Neko-Neko Bakery & Cake so far in predicting the amount of production where the demand for bakery and cake is sometimes not fulfilled considering that the available bakery and cake production is not sufficient because the bakery and cake supplies in the production section do not meet and vice versa, namely the amount of production sometimes experiences excess in production. production so that it is not in accordance with demand, then there is often an error in predicting the amount of production in producing bakery and cake because the number of bakery and cake productions produced is not really needed while bakery and cake which are often needed are not produced since so far the Neko-Neko Bakery and Cake in Prediction data collection on the number of bakery and cake productions only relies on the Microsoft Office Excel application system where the data that is processed or processed is sometimes double in value. The method used in the decision support system to predict the amount of bakery and cake production is fuzzy mamdani. Mamdani fuzzy method is one method that has a simple structure and easy to understand. Mamdani fuzzy logic uses MIN-MAX or max-product operations with a predetermined set of rules, namely the previous IF…AND…THEN. Based on the application of this mamdani fuzzy method in predicting the amount of bakery and cake production, it can be stated that it is very feasible to apply to Neko-Neko Bakery & Cake. This decision support system was built using the PHP programming language and MySQL database.

References

Ade Hendini. 2016. Pemodelan UML Sistem Informasi Monitoring Penjualan dan Stok Barang (Studi Kasus : Distro Zhezha Pontianak). Jurnal Khatulistiwa Informatika, Vol. IV. No. 2. 107-116

Linda Santya, et al. 2019. Penerapan Metode Fuzzy Mamdani Untuk Pendukung Keputusan Penentuan Jumlah Produksi Lantak Sijimat. Jurnal Rekayasa Teknologi Nusa Putra. Vol. 4. No. 4.

Mochammad Rusli. 2017. Dasar Perancangan Kendali Logika Fuzzy. Malang: UB Press

Muchammad Abrori dan Amrul Hinung Prihamayu. 2015. Aplikasi Logika Fuzzy Mamdani Dalam Pengambilan Keputusan Penentuan Jumlah Produksi. Kaunia. Vol. XI. No. 2. 91-99

Muhamad Muslihudin and Oktafianto. 2016. Analisis dan Perancangan Sistem Informasi Menggunakan Model Terstruktur dan UML. Ed. I. Yogayakarta : ANDIProduct Assesment (WASPAS). JURIKOM (Jurnal Riset Komputer), 5(2), 103–108.

Sutabri, T. (2016). Sistem informasi manajemen.

Weiss, L. (2005). The state-augmenting effects of globalisation. New Political Economy, 10(3), 345–353.

Wicaksono, M. R., Sakaria, S., & Oktavia, C. A. (2020). Sistem Pendukung Keputusan Untuk Mempermudah Kinerja Dalam Proses Penerimaan Beasiswa Menggunakan Metode SAW (Simple Additive Weighting) Berbasis Web (Studi Kasus: SMAS Empat Lima 1 Babat). J-INTECH, 8(01), 30–38.

Ali, A. A., & Ali Kulaib, A. M. (2020). Inventory Control Using Fuzzy Inference System and Adaptive Neuro Fuzzy Inference System under Uncertain Conditions. Hadhramout University Journal of Natural & Applied Sciences, 17(2), 3.

Allais, I., Perrot, N., Curt, C., & Trystram, G. (2007). Modelling the operator know-how to control sensory quality in traditional processes. Journal of Food Engineering, 83(2), 156–166.

Ariani, F., & Endra, R. Y. (2013). Implementation of fuzzy inference system with Tsukamoto method for study programme selection. International Conference on Engineering and Technology Development (ICETD).

Basadur, M., Graen, G. B., & Green, S. G. (1982). Training in creative problem solving: Effects on ideation and problem finding and solving in an industrial research organization. Organizational Behavior and Human Performance, 30(1), 41–70.

Cavalieri, S., & Pezzotta, G. (2012). Product–Service Systems Engineering: State of the art and research challenges. Computers in Industry, 63(4), 278–288.

Fitzgerald, L. F., & Shullman, S. L. (1993). Sexual harassment: A research analysis and agenda for the 1990s. Journal of Vocational Behavior, 42(1), 5–27.

Iriyanto, M. A. (2008). Sistem Pendukung Keputusan Untuk Produksi Jenang Menggunakan Logika Fuzzy Mamdani. UDiNus Repository, 2(1), 54–62.

Marbun, M., & Marbun, N. V. (2009). Perancangan sistem perencanaan jumlah produksi roti menggunakan metode fuzzy mamdani. Jurnal Mantik Penusa, 2(4).

McMinn, P. (2004). Search‐based software test data generation: a survey. Software Testing, Verification and Reliability, 14(2), 105–156.

Myroshnyk, Y. (2013). 1. WILD PLANT RAW MATERIALS USE IN PASTRY TECHNOLOGY. 79 МІЖНАРОДНА НАУКОВА КОНФЕРЕНЦІЯ МОЛОДИХ УЧЕНИХ, АСПІРАНТІВ І СТУДЕНТІВ, 425.

NAKANE, J., & HALL, R. W. (1991). Holonic manufacturing: flexibility—the competitive battle in the 1990s. Production Planning & Control, 2(1), 2–13.

Rizki, S. N. (2021). Fuzzy Implementation in The Selection of A Chieving Employees at PT. Sumber Barkah using Matlab Software. IJISTECH (International Journal of Information System & Technology), 5(2), 170–178.

Ruteri, J. M., & Xu, Q. (2009). Advanced Booking Discount Program: A Coordinating Strategy for SMEs Food Processors on Managing Demand Uncertainty. International Journal of Business and Management, 4(10), 139–147.

Saepudin, S., Miftah, M., Santya, L., & Mandala, V. (2019). PERBANDINGAN METODE FUZZY MAMDANI DENGAN TSUKAMOTO DALAM SISTEM PENDUKUNG KEPUTUSAN PENENTUAN JUMLAH PRODUKSI LANTAK SI JIMAT. JURNAL REKAYASA TEKNOLOGI UNIVERSITAS NUSA PUTRA, 6(1), 11–18.

Sakao, T., & Shimomura, Y. (2007). Service Engineering: a novel engineering discipline for producers to increase value combining service and product. Journal of Cleaner Production, 15(6), 590–604.

Storper, M., & Harrison, B. (1991). Flexibility, hierarchy and regional development: the changing structure of industrial production systems and their forms of governance in the 1990s. Research Policy, 20(5), 407–422.

Yogachi, E. F., Nasution, V. M., & Prakarsa, G. (2021). Design and Development of Fuzzy Logic Application Mamdani Method in Predicting The Number of Covid-19 Positive Cases in West Java. IOP Conference Series: Materials Science and Engineering, 1115(1), 12031.

Downloads

Published

2022-01-29

How to Cite

Rianto, E., & Manurung, J. . (2022). Total Prediction Decision Support System Bakery and Cake Production Using Mamdani Fuzzy Method: Case Study: Neko-Neko Bakery & Cake Branch Burned Stone, Deli Serdang. Jurnal Mandiri IT, 10(2), 57–62. https://doi.org/10.35335/mandiri.v10i2.95