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Implementasi Sistem Prediksi Harga Motor Bekas Menggunakan Algoritma Categorical Boosting

Fathia Wardah S. Djawas(1*),Muhammad Najamuddin Dwi Miharja(2),Nanang Tedi K(3)
(1) Universitas Pelita Bangsa
(2) Universitas Pelita Bangsa
(3) Universitas Pelita Bangsa
(*) Corresponding Author
DOI : 10.35889/jutisi.v15i3.3730

Abstract

This study is intended to design a web-based system for predicting used motorcycle prices by employing the Categorical Boosting (CatBoost) algorithm. The research problem stems from the subjective process of setting used motorcycle prices, which is affected by multiple factors and frequently results in inaccurate price estimates. The research methodology covers data collection from the OLX Indonesia platform, followed by data preprocessing, feature engineering, data partitioning with a train–test split approach, and model construction using the CatBoost Regressor algorithm. The experimental results indicate that the prediction model attains a coefficient of determination of 0.9166 on the training set and 0.9149 on the test set. These findings suggest that the model performs well, accounting for more than 91% of the variance in used motorcycle prices and producing stable price predictions without notable overfitting. The system is deployed using Streamlit, enabling users to obtain used motorcycle price predictions in a faster, interactive, and more objective manner.

Keywords: Used Motorcycle; Machine Learning; CatBoost; Streamlit

 

Abstrak

Penelitian ini dimaksudkan untuk merancang sebuah sistem prediksi harga motor bekas berbasis web dengan memanfaatkan algoritma Categorical Boosting (CatBoost). Permasalahan penelitian berangkat dari praktik penentuan harga motor bekas yang masih bersifat subjektif dan dipengaruhi beragam faktor sehingga kerap menghasilkan estimasi harga yang kurang akurat. Metode yang digunakan mencakup pengumpulan data dari platform OLX Indonesia, preprocessing data, feature engineering, pembagian data menggunakan skema train–test split, serta pemodelan dengan algoritma CatBoost Regressor. Hasil pengujian memperlihatkan bahwa model prediksi memperoleh nilai koefisien determinasi (R²) sebesar 0,9166 pada data latih dan 0,9149 pada data uji. Temuan ini mengindikasikan bahwa model memiliki kinerja yang baik, karena mampu menjelaskan lebih dari 91% variasi harga motor bekas serta menghasilkan prediksi yang konsisten tanpa gejala overfitting yang berarti. Sistem kemudian diimplementasikan menggunakan Streamlit sehingga pengguna dapat memperoleh estimasi harga motor bekas dengan lebih cepat, interaktif, dan bersifat lebih objektif.

 

Keywords


Prediksi Harga; Motor Bekas; Machine Learning; CatBoost; Streamlit

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