APLIKASI SISTEM PREDIKSI PENJUALAN SPAREPART MOBIL DENGAN METODE FUZZY MAMDANI (STUDI KASUS : PT. KASAI TECK SEE)
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Kusuma, Ragiel Adnan
Putra, Rakhmadi Irfansyah
Cipta W, Max Teja Ajie
Putra, Rakhmadi Irfansyah
Cipta W, Max Teja Ajie
Subject
Teknik Informatika
Datestamp
2022-09-21 01:35:28
Abstract :
PT. Kasai Teck See is a car spare parts provider company experiencing problems if consumer demand is received simultaneously. Because the production machine has the maximum capacity to do production everyday. This will result in the process of sending goods being hampered because the production process is still not finished. This is why PT. Kasai want to study the historical pattern of sales to consumers so that it becomes a reference for the future. In the process of processing so far PT. Kasai only uses Microsoft Excel. Fuzzy Mamdani method can be used to make sales predictions using historical sales data of PT. Kasai Teck See. Sales data used for the Mamdani calculation is sales data of PT. Kasai in 2017 and 2018. The data used for the discussion of this method is to use demand data totaling 4000 and inventory totaling 300. Sales prediction results obtained in the amount of 4247 units. With an accuracy rate of 95.8791% using MAPE
PT. Kasai Teck See is a car spare parts provider company experiencing problems if consumer demand is received simultaneously. Because the production machine has the maximum capacity to do production everyday. This will result in the process of sending goods being hampered because the production process is still not finished. This is why PT. Kasai want to study the historical pattern of sales to consumers so that it becomes a reference for the future. In the process of processing so far PT. Kasai only uses Microsoft Excel. Fuzzy Mamdani method can be used to make sales predictions using historical sales data of PT. Kasai Teck See. Sales data used for the Mamdani calculation is sales data of PT. Kasai in 2017 and 2018. The data used for the discussion of this method is to use demand data totaling 4000 and inventory totaling 300. Sales prediction results obtained in the amount of 4247 units. With an accuracy rate of 95.8791% using MAPE
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