PENGEMBANGAN APLIKASI PERENCANAAN PRODUKSI PRODUK BAKERY DENGAN MENERAPKAN SISTEM INFERENSI FUZZY METODE MAMDANI, SUGENO, DAN TSUKAMOTO (STUDI KASUS: CV CAHAYA CIPTA MAKMUR)

R. Rully Mahendra, NIM.: 22106060032 (2026) PENGEMBANGAN APLIKASI PERENCANAAN PRODUKSI PRODUK BAKERY DENGAN MENERAPKAN SISTEM INFERENSI FUZZY METODE MAMDANI, SUGENO, DAN TSUKAMOTO (STUDI KASUS: CV CAHAYA CIPTA MAKMUR). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

	Text (PENGEMBANGAN APLIKASI PERENCANAAN PRODUKSI PRODUK BAKERY DENGAN MENERAPKAN SISTEM INFERENSI FUZZY METODE MAMDANI, SUGENO, DAN TSUKAMOTO (STUDI KASUS: CV CAHAYA CIPTA MAKMUR)) 22106060032_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf - Published Version Download (6MB)
	Text (PENGEMBANGAN APLIKASI PERENCANAAN PRODUKSI PRODUK BAKERY DENGAN MENERAPKAN SISTEM INFERENSI FUZZY METODE MAMDANI, SUGENO, DAN TSUKAMOTO (STUDI KASUS: CV CAHAYA CIPTA MAKMUR)) 22106060032_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf - Published Version Restricted to Registered users only Download (16MB) | Request a copy

Abstract

CV Cahaya Cipta Makmur is a food processing company that offers that offers a variety of bread products as new items marketed to the general public. As newly introduced products, their demand is fluctuating and uncertain. The company implements a make-to-order production system to align production quantities with incoming demand while avoiding waste caused by overproduction. However, based on an analysis of production data from January to March 2026, a discrepancy was found between production targets and actual finished goods, indicating inefficiencies in raw material usage within the company’s production planning. Fuzzy logic was chosen as an approach capable of providing systematic calculations to support production decision-making under conditions of uncertainty and data fluctuation. This study compares three fuzzy methods, namely Mamdani, Sugeno, and Tsukamoto, using historical production data from one of the bread products with the highest demand frequency. The results show that the Mean Absolute Percentage Error (MAPE) values for each method are 19.96% for Mamdani, 2.81% for Sugeno, and 54.85% for Tsukamoto. The Sugeno method yields the lowest MAPE value and is therefore identified as the best method with the highest level of accuracy among the three. Based on these results, the Sugeno method is used as the foundation for developing a decision support system for production planning that can be utilized by the company to determine optimal production quantities. This system takes into account the level of incoming demand as well as the available stock, and is expected to help the company minimize the risk of losses due to unsold products while maximizing the efficiency of raw material usage and the potential profit gained.

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