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All Journal International Journal of Advances in Applied Sciences Jurnal Edukasi dan Penelitian Informatika (JEPIN) CogITo Smart Journal Journal of Information Systems and Informatics Infotech: Journal of Technology Information Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Konstelasi: Konvergensi Teknologi dan Sistem Informasi Riwayat: Educational Journal of History and Humanities
Kamila, Ahya Radiatul
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics Social Sciences

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Journal : CogITo Smart Journal

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Analysis Comparison of K-Nearest Neighbor, Multi-Layer Perceptron, and Decision Tree Algorithms in Diamond Price Prediction Kamila, Ahya Radiatul; Andry, Johanes Fernandes; Kusuma, Adi Wahyu Candra; Prasetyo, Eko Wahyu; Derhass, Gerry Hudera
CogITo Smart Journal Vol. 10 No. 2 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i2.532.298-311

Abstract

Diamond price predictions are essential due to the high demand for these gemstones, valued as investments and jewelry. Diamonds are expensive due to their rarity and extraction process. Their prices vary depending on key factors like the diamond's inherent value and secondary factors such as marketing costs, brand names, and market trends. These variations often confuse customers, potentially leading to investment losses. This research aims to help investors determine the true price of diamonds based solely on their intrinsic value, excluding secondary factors. A machine learning approach was utilized to predict diamond prices, focusing on primary determinants. Three models such as Multi-Layer Perceptron (MLP), Decision Tree, and K-Nearest Neighbor (KNN) were compared with manual hyperparameter tuning to identify the best performing algorithm. Model performance was evaluated using Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (MSE). Among the models, KNN demonstrated the best results, achieving MAPE, MAE, and MSE values of 1.1%, 0.00038, and 〖2.687 x 10〗^(-6) respectively. This study offers valuable insights for investors by accurately predicting diamond prices based on fundamental attributes, minimizing the impact of secondary factors.
Co-Authors Adikara, Fransiskus Ariq Cahya Wardhana Budiyanto, Very Christy, Vania Derhass, Gerry Hudera EKO WAHYU PRASETYO Francka Sakti Lee Geasela, Yemima Monica Herdian, Cevi Johanes Fernandes Andry Kusuma, Adi Wahyu Candra Meyliana, Sintia Revaldo, Danny Samuel Winata Surianto Surianto Sutrisno, Sutrisno Tampinongkol, Felliks F. Tampinongkol, Felliks Feiters