Klasifikasi Preferensi Mahasiswa dalam Pemilihan Laptop Menggunakan Analisis Diskriminan Kernel Gaussian

Meilan Sigar; Lailany Yahya; Salmun K. Nasib; Nisky Imansyah Yahya; Djihad Wungguli

doi:10.62383/bilangan.v3i5.804

Authors

Meilan Sigar Universitas Negeri Gorontalo
Lailany Yahya Universitas Negeri Gorontalo
Salmun K. Nasib Universitas Negeri Gorontalo
Nisky Imansyah Yahya Universitas Negeri Gorontalo
Djihad Wungguli Universitas Negeri Gorontalo

DOI:

https://doi.org/10.62383/bilangan.v3i5.804

Keywords:

Classification, Gaussian, Kernel Discriminant Analysis, Laptop, Preferences

Abstract

Rapid developments in information technology have made laptops an essential device for students, especially those in their final year of study. Choosing the right laptop plays an important role in supporting academic productivity, such as writing theses, analyzing data, and developing software. This study aims to classify the preferences of mathematics students at Gorontalo State University in choosing laptops based on usage characteristics and factors that influence purchasing decisions. The method used is Kernel Discriminant Analysis (KDA) with a Gaussian kernel function and an optimal bandwidth of 0.8. The research data involved 268 respondents divided into training and testing data. The analysis results show that the KDA model has an accuracy rate of 60% on the training data and 52% on the testing data, which indicates the model's ability to recognize student preference patterns despite a decrease in accuracy on new data. Based on the kernel density estimation results, Acer is the most widely used laptop brand, while Zyrex and Apple are rarely chosen. The most influential factor in purchasing decisions is processor specifications, with a contribution of 35.739%, followed by brand, warranty, and price. These findings indicate that hardware characteristics are the main consideration in laptop selection, with most students choosing laptops with Intel Core i5 processors, a minimum of 8GB of RAM, and SSD storage. The results of this study can also be used by universities to provide recommendations for selecting laptops that suit students' academic needs.

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References

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.

Erikasari, V., Zulaeha, Z., Mulqiya, W. Z., Maharani, T. F., & Anshor, A. H. (2025). Optimalisasi preferensi mahasiswa dalam pemilihan laptop menggunakan metode TOPSIS. Jurnal Teknologi dan Sistem Informasi Bisnis, 7(1), 119–125. https://doi.org/10.47233/jteksis.v7i1.1726

Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x

Fitri, F. H., Vionanda, D., Kurniawati, Y., & Mukhti, T. O. (2024). Comparison of linear discriminant analysis with robust linear discriminant analysis. UNP Journal of Statistics and Data Science, 2(3), 353–359. https://doi.org/10.24036/ujsds/vol2-iss3/206

Jain, A. K., Duin, R. P. W., & Mao, J. (2021). Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 4–37. https://doi.org/10.1109/34.824819

Johnson, T., & B. L., S. R. (2022). Trends in student preferences for personal computers: An analytical approach. Journal of Consumer Research, 49(3), 235–248.

Marsaoly, T., & Haviani, R. (2023). Comparison of SAW methods with WP method laptop recommendations. Jurnal Mandiri IT, 12(2), 106–115.

Nurhikmah. (2020). Analisis preferensi mahasiswa dalam memilih laptop menggunakan analisis konjoin. Jurnal Humanika, 30(2), 45–56. https://doi.org/10.1234/humanika.v30i2.61

Park, S. Y., Cha, S. B., Joo, M. H., & Na, H. (2022). A multivariate discriminant analysis of university students' career decisions based on career adaptability, social support, academic major relevance, and university life satisfaction. International Journal for Educational and Vocational Guidance, 22(1), 191–206. https://doi.org/10.1007/s10775-021-09480-5

Permatasari, S. P., & Setyowibowo, F. (2024). Analisis diskriminan budaya, gaya hidup, e-WOM, dan FOMO terhadap keputusan pembelian online berdasarkan online buying characters pada mahasiswa FKIP UNS. Journal of Social Science Research, 4(4), 10335–10344.

Putra, A. R., & Wijaya, B. (2024). Preferensi mahasiswa IT dalam pemilihan rekomendasi laptop terbaik dengan metode weighted product berbasis web. Jurnal Sistem Informasi, 15(3), 89–102. https://doi.org/10.9101/jsi.v15i3.4518

Samahwati, S., Rohayu, S., & Hidayat, S. (2025). Analisis harga laptop di kalangan mahasiswa. Jurnal Penelitian Ekonomi Manajemen dan Bisnis, 4(1), 19–29. https://doi.org/10.55606/jekombis.v4i1.4708

Sari, M., & Kurniawan, R. (2022). Sistem pendukung keputusan pemilihan laptop bagi mahasiswa menggunakan metode Analytic Hierarchy Process (AHP). Jurnal Teknologi Informasi dan Komputer, 9(2), 112–120. https://doi.org/10.33369/jtik.v9i2.18476

Shawe-Taylor, J., & Cristianini, N. (2004). Kernel methods for pattern analysis. Cambridge University Press. https://doi.org/10.1017/CBO9780511809682

Soraya, A., Gabriel, D., & Kurnia, I. D. (2023). Faktor-faktor yang mempengaruhi preferensi mahasiswa dalam belanja online (Studi survei pada mahasiswa Ilmu Komunikasi Unesa 2023B). Prosiding Seminar Nasional Ilmu Ilmu Sosial (SNIIS), 39–51.

Tjahaya, M. S., Raupong, S., & Tinungki, G. M. (2022). Analisis diskriminan linear robust dengan metode Winsorized Modified One-Step M Estimator. Journal of Statistics and Its Application, 3(1), 1–13. https://doi.org/10.20956/ejsa.vi.11302