Peramalan Permintan Kendaraan Bermotor Roda Empat Di Indonesia Dengan Menggunakan Integrasi Metode Regresi Pls Dan Adaptive Neuro Fuzzy Inference Systems

Nugroho, Dicky Alfians Adi (2016) Peramalan Permintan Kendaraan Bermotor Roda Empat Di Indonesia Dengan Menggunakan Integrasi Metode Regresi Pls Dan Adaptive Neuro Fuzzy Inference Systems. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Dalam industri otomotif akurasi peramalan terhadap permintaan pasar merupakan sesuatu yang signifikan karena memiliki pengaruh terhadap proses manufaktur, pengiriman serta proses penjualan. Dengan beranggapan bahwa terdapat beberapa factor yang memiliki peran signifikan dalam peramalan permintaan terhadap kendaraan roda empat di Indonesia . Penelitian ini memanfaatkan factor-faktor yang dirasa memiliki pengaruh signifikan diantaranya adalah data harga bahan bakar kendaraan, pertumbuhan ekonomi, pertumbuhan populasi, indeks pencarian, daya beli masyarakat serta data tingkat penggaguran. Berdasarkan hal tersebut lahirlah tuga ini yang mana akan mengimplementasikan integrasi model Partial Least Square Regression dan Adaptive Neuro based Fuzzy Inference System (ANFIS) dalam peramalan permintaan terhadap kendaraan roda empat di Indonesia. Dalam tugas akhir ini Regresi PLS akan digunakaan dalam menentukan variable yang memiliki pengaruh signifikan dari variable yang diajukan di awal. Kemudian variable tersebut akan dijadikan sebagai masukan bersama dengan data histori penjualan dalam model Adaptive Neuro based Fuzzy Inference System untuk mendapatakan model serta hasil forecast. Untuk melakukan pengetesan terhadap hasil uji dari model Adaptive Neuro based Fuzzy Inference System, tugas akhir ini juga akan menyertakan hasil komparasi dari model peramalan lain yaitu model Autoregressive Integrated Moving Average (ARIMA) serta model Dekomposisi. =========================================================== In the automotive industry demant forecasting accuracy is important because it has an influence on the process of manufacturing, delivery and sales process. Assuming that there are several factors that have a significant role in forecasting of automotive industry in Indonesia. This study utilized the factors considered to have significant influence include fuel price, economic growth, population growth,goole search indexes, customer purchasing indexs and unemployment rate. Based on that argument this research is proposed, this reserch will implement the integration model of Partial Least Squares Regression and Adaptive Neuro Fuzzy based Inference System (ANFIS) in forecasting demand for four-wheel vehicles in Indonesia. In this final project we will utilize PLS regression to determine variables that has a significant effect on demand of automotive industry. Then those variables will be used as an input along with the sales history data in a model-based Adaptive Neuro Fuzzy Inference System to get the best model and forecast results. For testing the results of the model-based Adaptive Neuro Fuzzy Inference System, this reserch will also include the comparison of other forecasting model such as Autoregressive Integrated Moving Average (ARIMA), and the model decomposition.

Item Type: Thesis (Undergraduate)
Additional Information: RSSI 519.535 Nug p
Uncontrolled Keywords: Automobile Demand, PLS Regresi, ANFIS, Demand Forecasting
Subjects: Q Science > QA Mathematics > QA276 Mathematical statistics. Time-series analysis.
Divisions: Faculty of Information and Communication Technology > Information Systems > (S2) Master Theses
Depositing User: EKO BUDI RAHARJO
Date Deposited: 22 Nov 2019 06:43
Last Modified: 22 Nov 2019 06:43
URI: http://repository.its.ac.id/id/eprint/71944

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