Prediksi harga mobil audi bekas menggunakan metode multiple linear regression, regression tree, dan random forest

Pormes, Tenny (2022) Prediksi harga mobil audi bekas menggunakan metode multiple linear regression, regression tree, dan random forest. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Harga mobil bekas dipengaruhi oleh banyak faktor, di antaranya adalah transmisi, jarak tempuh, bahan bakar, dll. Oleh sebab itu, untuk membantu menentukan harga mobil bekas dapat menggunakan model Machine Learning. Salah satu algoritma yang dapat digunakan dalam Machine Learning adalah model regresi linier. Model Regresi memiliki kemampuan untuk melihat hubungan antara variabel-variabel dependen dan independen. Hal ini akan memudahkan penjual atau pembeli untuk menentukan harga mobil bekas yang tepat sesuai dengan fitur-fitur yang ditawarkan. Dalam skripsi ini digunakan 3 metode, yaitu Multiple Linear Regression, Regression Tree, dan Random Forest untuk memprediksi harga mobil bekas. Dari ketiga metode tersebut, didapatkan model yang paling optimal untuk memprediksi harga mobil bekas adalah model yang menggunakan metode Random Forest berdasarkan Mean Squared Error (MSE) yang dihasilkan, yaitu sebesar 1.349,7 untuk data pelatihan dan 2.347,5 untuk data tes. Hasil MSE dari metode Random Forest ini adalah MSE terkecil dari kedua metode lain yang digunakan. / Used car’s price can be affected by a lot of factors, some of them are transmission, mileage, fuel, etc. In this thesis, we will build mathematical models to predict the price of used car using Machine Learning Model. One of the Machine Learning algorithm is linear regression model. Regression linear model has an ability to find relationship between dependent and independent variables which can help seller or buyer to put a right price of a used car based on their features. In this thesis we will discuss three methods, which are Multiple Linear Regression, Regression Tree, and Random Forest. The optimal method to predict the price of used car is Random Forest which is based on the Mean Squared Error (MSE), 1.349,7 for data train and 2.347,5 for data test. The MSE result of Random Forest is the lowest compared to the other methods in this thesis.

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