Peramalan Penjualan Kendaraan Mobil Segmen B2B dengan Metode Regresi Linear Berganda, Jaringan Saraf Tiruan dan Jaringan Saraf Tiruan – Algoritma Genetika

Muhammad Agung Nugraha, Farizal Farizal, Djoko Sihono Gabriel


This study aims to create an effective forecasting model in predicting sales of car products in the B2B segment (Business to Business) to obtain estimates of product sales in the future. This research uses multiple linear regression and artificial neural networks that are optimized by genetic algorithms. Forecasting factors for car sales are generally issued by total national car sales, the Consumer Price Index, the Consumer Confidence Index, the Inflation Rate, Gross Domestic Product (GDP), and Fuel Oil Price. The author has also gotten the factors that play a role in the sale of B2B segment by diverting the survey to 106 DMU (Decision Making Unit) who decide to purchase cars in their company. Then we evaluate the results of the questionnaire in training data and simulations on the Artificial Neural Network. Optimized Artificial Neural Networks with Genetic Algorithms can improve B2B segment car sales' accuracy when comparing error values in the ordinary Artificial Neural Network and Multiple Linear Regression.


Artificial Neural Networks; Automotive Industry; Forecasting; Genetic Algorithms; Multiple Linear Regression

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