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.
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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. Failure time data analysis. Survival analysis (Biometry) |
| Divisions: | Faculty of Information and Communication Technology > Information Systems > 59101-(S2) Master Thesis |
| 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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