Comparison of LSTM Model Performance with Classical Regression in Predicting Gaming Laptop Prices in Indonesia
Abstract
The demand for gaming laptops has surged in the digital era, appealing to both professional gamers and the general public. Gaming laptops come equipped with advanced features such as powerful graphics, fast processors, and sleek designs, offering a portable solution for gaming enthusiasts. However, the price of gaming laptops varies due to factors like brand, hardware specifications, screen size, and additional features. Accurately predicting these prices can help consumers make informed purchasing decisions and assist manufacturers in setting competitive prices. This research proposes the use of the Long Short-Term Memory (LSTM) algorithm to predict gaming laptop prices, comparing its performance with classic regression algorithms such as Linear Regression and Multi-layer Perceptron. Utilizing a comprehensive dataset of gaming laptop prices and specifications in Indonesia, this study employs robust pre-processing and model optimization techniques. The results show that the LSTM model achieves a Root Mean Squared Error (RMSE) of 0.09011, a Mean Squared Error (MSE) of 0.00812, and an R² Score of 0.90016. In comparison, the Linear Regression model has an RMSE of 0.09075, an MSE of 0.00823, and an R² Score of 0.89873, while the Multi-layer Perceptron model has an RMSE of 0.09891, an MSE of 0.00978, and an R² Score of 0.87971. These results indicate that the Long Short-Term Memory algorithm outperforms other classic regression algorithms in this case. This study highlights the potential of LSTM in developing a robust price prediction model for gaming laptops, particularly in the Indonesian market, providing valuable insights for both consumers and manufacturers.
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References
B. Ayu Saputri, A. Samhudi, E. Zamilah, and U. Islam Kalimantan Muhammad Arsyad Al-Banjari, “Analisis Peningkatan dan Prediksi Penjualan Laptop Pada IT Komp Banjarbaru,” 2021.
Aakarshachug, “What is LSTM – Long Short-Term Memory?” GeeksforGeeks. Accessed: Jun. 23, 2024. [Online]. Available: https://www.geeksforgeeks.org/deep-learning-introduction-to-long-short-term-memory/?utm_source=auth&utm_medium=saved&utm_campaign=articles
E. Eli Lavindi and A. Rohmani, “Aplikasi Hybrid Filtering Dan Naïve Bayes Untuk Sistem Rekomendasi Pembelian Laptop Hybrid Filtering and Naïve Bayes Application for Laptop Purchase Recommendation Systems,” Journal of Information System, vol. 4, no. 1, pp. 54–64, 2019.
M. A. Shaik, M. Varshith, S. Srivyshnavi, N. Sanjana, and R. Sujith, “Laptop Price Prediction using Machine Learning Algorithms,” in 2022 International Conference on Emerging Trends in Engineering and Medical Sciences, ICETEMS 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 226–231. doi: 10.1109/ICETEMS56252.2022.10093357.
M. A. Sharma and M. S. Bhushan, “Laptop Price Prediction System Using Machine Learning,” JOURNAL OF APPLIED OPTICS, 2024.
Fatur Febrianto M, “Gaming Laptop Dataset,” Kaggle. Accessed: Jun. 23, 2024. [Online]. Available: https://www.kaggle.com/datasets/faturfebr/gaming-laptop-dataset
Gupta M, “Regresi Linier dalam Pembelajaran Mesin,” GeeksforGeeks. Accessed: Jun. 23, 2024. [Online]. Available: https://www.geeksforgeeks.org/ml-linear-regression/
S. Sen, D. Sugiarto, and A. Rochman, “Komparasi Metode Multilayer Perceptron (MLP) dan Long Short Term Memory (LSTM) dalam Peramalan Harga Beras,” ULTIMATICS, vol. XII, no. 1, p. 35, 2020.
Anugerah W, “Perbedaan RMSE dan MSE: Mengenal Metode Evaluasi Kinerja Model Regresi,” Localstartupfest. Accessed: Jun. 24, 2024. [Online]. Available: https://www.localstartupfest.id/faq/perbedaan-rmse-dan-mse/
Fernando J, “R-Squared: Definition, Formula, Uses, and Limitations,” Investopedia. Accessed: Jun. 24, 2024. [Online]. Available: https://www.investopedia.com/terms/r/r-squared.asp
T. B. Sianturi, I. Cholissodin, and N. Yudistira, “Penerapan Algoritma Long Short-Term Memory (LSTM) berbasis Multi Fungsi Aktivasi Terbobot dalam Prediksi Harga Ethereum,” 2023. [Online]. Available: http://j-ptiik.ub.ac.id
M. Rizki, S. Basuki, and Y. Azhar, “Implementasi Deep Learning Menggunakan Arsitektur Long Short Term Memory Untuk Prediksi Curah Hujan Kota Malang,” REPOSITOR, vol. 2, no. 3, pp. 331–338, 2020.
A. A. Suryanto, A. Muqtadir, and S. Artikel, “Penerapan Metode Mean Absolute Error (MAE) dalam Algoritma Regresi Linear untuk Prediksi Produksi Padi Info Artikel : ABSTRAK,” no. 1, p. 11, 2019.
Wijaya Mahendra A, “Analisis Pengaruh Brand Characteristics dan Brand Resonance Terhadap Brand Loyalty Laptop ASUS di Soloraya,” 2022.
N. Erliani, K. Suryowati, M. T. Jatipaningrum, and J. Statistika, “Klasifikasi Tingkat Penjualan Laptop di e-Commerce Menggunakan Algoritma Classification and Regression Tree(CART),” Jurnal Statistika Industri dan Komputasi, vol. 08, no. 2, pp. 40–47, 2023, [Online]. Available: https://www.tokopedia.com.
T. O. Hodson, “Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not,” Geoscientific Model Development, vol. 15, no. 14. Copernicus GmbH, pp. 5481–5487, Jul. 19, 2022. doi: 10.5194/gmd-15-5481-2022.
A. Samii, H. Karami, H. Ghazvinian, A. Safari, and Y. D. Ajirlou, “Comparison of DEEP-LSTM and MLP Models in Estimation of Evaporation Pan for Arid Regions,” Journal of Soft Computing in Civil Engineering, vol. 7, no. 2, pp. 155–175, Apr. 2023, doi: 10.22115/SCCE.2023.367948.1550.
C. A. Rahardja and H. Agung, “Rahardja, Implementasi Algoritma K-Nearest Neighbor Pada Website Rekomendasi Laptop 75 Implementasi Algoritma K-Nearest Neighbor Pada Website Rekomendasi Laptop,” 2019.
C. Puspa Tria, A. Nuryaman, A. Faisol, dan Eri Setiawan, J. Soemantri Brojonegoro No, and B. Lampung, “Penerapan Algoritma K-Nearest Neighbor Pada Data Kategorik Untuk Klasifikasi Harga Jual Laptop,” 2023.
Herwinsyah, “Penerapan Fuzzy Inference System (FIS) Dengan Metode Mamdani Pada Sistem Prediksi Penjualan Laptop Implementation of Fuzzy Inference System (FIS) with the Mamdani Method in Laptop Sales Prediction System,” 2019.
H. W. Fondy, M. Fajar, and I. Alwiah Musdar, “Jurnal Ilmu Komputer KHARISMA TECH Implementasi Teori Support Vecto R Machine Untuk Memprediksi Harga Penjualan Laptop ASUS,” 2019.
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