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ITS » Master Theses » Manajemen Industri-S2 MMT
Posted by yeni at 28/12/2006 11:34:47  •  26797 Views


OPTIMASI KEUNTUNGAN BAURAN PRODUK BERAS DENGAN METODE ARIMA-REGRESI DAN PROGRAMA LINIER DI PB. ADIL MAKMUR

Created by :
Khairunnisa 



SubjectMatematika Terapan
Keywordoptimasi
bauran
beras
program linier
ARIMA
regresi.

Description:

PB. Adil Makmur adalah perusahaan penggilingan beras yang memiliki 4 jenis produk beras. Produk beras di PB Adil Makmur berasal dari campuran 1 atau lebih jenis bahan baku gabah dengan komposisi tertentu. Perusahaan berkepentingan untuk melakukan pengelolaan bahan baku yang memaksimalkan keuntungan melalui bauran produk beras. Perusahaan ingin mengetahui jenis bahan baku dan jumlah bahan baku yang harus dibeli untuk memenuhi permintaan beras periode 2003 dengan memperhatikan peramalan permintaan dan prediksi harga beli/jual. Dalam penelitian ini dilakukan peramalan permintaan/penjualan beras dengan menggunakan pendekatan fungsi transfer yang merupakan kombinasi ARIMA dan regresi. Metode ini dipakai karena dalam proses peramalan ini melibatkan variabel-variabel yang diduga dapat mempengaruhi permintaaan atau penjualan. Dalam penelitian ini dilakukan pula prediksi harga beli gabah dan harga jual beras dengan menggunakan multiple regression. Metode ini dipakai karena dalam proses prediksi ini melibatkan variabel-variabel yang diduga dapat mempengaruhi perubahan harga beli dan jual. Dalam optimasi keuntungan bauran produk beras ini digunakan program LINGO 5.0 untuk mencari solusi yang layak. Dalam penelitian ini termuat 288 variabel keputusan dan 745 konstrain. Dari hasil perhitungan diketahui bahwa bahan baku gabah 57.8% berasal dari GKS dan 42.2% dari GKG dengan penggunaan IR 64 A 85.23%, IR 64 B 9.8%, IR 64 C 2.67%, IR A 0.3%, IR B 1.06%, IR C 0.19%, GH A 0.39%, dan GH B 0.36%. Keuntungan maksimum yang akan diperoleh selama periode 2003 sebesar Rp 2.480.352.100.


Alt. Description

PB. Adil Makmur is a rice huller whose 4 kind of rice products. Rice products of PB. Adil Makmur made from one or more grains with various composition. PB. Adil Makmur consider to manage their resources to optimize the profit through rice product mix. They want to know how many kind of grains have to buy to meet demand in 2003. This research forecasted the demand with transfer function approach that combine ARIMA and regression. We use the term transfer function model to refer to a mode that predicts future values of a time series on the basis of past values of this series and on the basis of one or more related time series. This research also did multiple regression to predict the cost of grains and price of rice products and to know about relation of explanatory variables to them. Profit optimization of rice product mix used Lingo 5.0 to find feasible solution. This case had 288 variables and 745 constraint. From the result of Lingo 5.0 we may know the kind of grains 57.8% came from GKS and 42.2% from GKG with composition IR 64 A 85.23%, IR 64 B 9.8%, IR 64 C 2.67%, IR A 0.3%, IR B 1.06%, IR C 0.19%, GH A 0.39%, and GH B 0.36%. Optimize profit can be reached for periode 2003 is Rp 2.480.352.100.

Contributor:
  1. Dr. Ir. Moses L. Singgih, MSc, MRegSc
Date Create:28/12/2006
Type:Text
Format:pdf; 50 pages
Language:Indonesian
Identifier:ITS-Master-3100005021297
Collection ID:3100005021297
Call Number:519.536 Kha o


Source :
Theses Industry Management RTMT 519.536 Kha o, 2003

Coverage :
ITS Community

Rights :
Copyright @2003 by ITS Library. This publication is protected by copyright and permission should be obtained from the ITS Library prior to any prohibited reproduction, storage in a retrievel system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise. For information regarding permission(s), write to ITS Library


Publication URL :
http://digilib.its.ac.id/optimasi-keuntungan-bauran-produk-beras-dengan-metode-arimaregresi-dan-programa-linier-di-pb-adil-makmur-364.html




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ARIMA , bauran , beras , linier , optimasi , program , program linier , regresi.



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