Peramalan Hasil Panen Kelapa Sawit Menggunakan Metode Multifactors High Order Fuzzy Time Series yang Dioptimasi dengan K-Means Clustering (Studi Kasus: PT. Sandabi Indah Lestari Kota Bengkulu) - Yessica Inggir Febiola; Imam Cholissodin; Candra Dewi (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Based on the export volume data recorded by the Ministry of Agriculture from 2012 to 2016, palm oil (Elaeis guineensis Jacq) became one of the centres of Government and investor attention. In the oil palm plantation company, one of which is PT. Sandabi Indah Lestari Bengkulu occurs constraints on palm oil crops that do not match the target expected. This target is the sum of harvest and when it is used to harvest the oil palm. When crops do not match the target, it can cause budgets of production that do not match the planned. Therefore, the company requires forecasting the crops to minimize the constraints caused by the crops that are not suitable for the target. The Multifactor High Order Fuzzy Time series method that is optimized with K-means Clustering to determine which subintervals are used can help in forecasting the oil palm harvest. The plot of this method is the determination of the Universe of discourse, the determination of the number of clusters, the formation of subintervals with K-means Clustering, the formation of the fuzzy set, the fuzzification process, the formation of Fuzzy Logic Relationship (FLR), and the defuzzification process. This research uses several factors that lacte over the harvest of oil palm Lot month, land area, age of the plant, and the amount of oil palm trees. From the results of the tested the sum of clusters, the sum of orders, the sum of training data, and the optimal threshold in the successive are 8, 6, 107, and 6 with the best AFER value of 36,98%.