Pengurangan Dimensi dengan Metode Linear Discriminant Analist (LDA)
DOI:
https://doi.org/10.29408/jit.v6i2.10069Keywords:
Breast Cancer, Classification, LDA, Reduction of variables, Logistic RegressionAbstract
The purpose of this study is to reduce the dimensions of the dataset that affect the prediction of breast cancer. The data used in research is very much data or is called high-dimensional data. The use of classification algorithms has weaknesses when used on high-dimensional data, so an appropriate method is needed to reduce the dimensions or variables used. There are several methods that can be used to reduce dimensions. In this study using the method of linear discriminant analysis (LDA). LDA is a supervised machine learning algorithm that is used to classify data into several classes, using a linear technique to determine the best set of linear variables to unify class data. LDA is used to reduce the dataset variables used by retaining information that is important for the classification process. The method used in this research is using LDA in data processing and then using a logistic regression model for the classification process. The conclusion obtained in this study is that LDA can overcome the problem of multiclass classification. The results obtained were 16 wrong cases out of a total of 455 cases so that the results obtained were 0.035% misclassification.References
S. Ketut, “Kanker payudara: Diagnostik, Faktor Risiko dan Stadium,” Ganesha Med. J., vol. 2, no. 1, pp. 2–7, 2022.
R. Annisa, “Analisis Komparasi Algoritma Klasifikasi Data Mining Untuk Prediksi Penderita Penyakit Jantung,” J. Tek. Inform. Kaputama, vol. 3, no. 1, pp. 22–28, 2019, [Online]. Available: https://jurnal.kaputama.ac.id/index.php/JTIK/article/view/141/156
P. Xu, “Review on Studies of Machine Learning Algorithms,” J. Phys. Conf. Ser., vol. 1187, no. 5, 2019, doi: 10.1088/1742-6596/1187/5/052103.
Erlin, Yulvia Nora Marlim, Junadhi, Laili Suryati, and Nova Agustina, “Deteksi Dini Penyakit Diabetes Menggunakan Machine Learning dengan Algoritma Logistic Regression,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 11, no. 2, pp. 88–96, 2022, doi: 10.22146/jnteti.v11i2.3586.
F. Dewanta and M. Abdillah, “Klasifikasi Beban Listrik dengan Machine Learning Menggunakan Metode K- Nearest Neighbor,” vol. 5, no. 2, pp. 163–172.
D. Nugraha, P. Novantara, and A. Muhammad, “Implementasi Algoritma PCA pada Pengenalan Pola Tanda Tangan dengan Menggunakan Bahasa Pemrograman Phyton,” JEJARING J. Teknol. dan Manaj. Inform., vol. 6, no. 1, pp. 7–12, 2021, doi: 10.25134/jejaring.v6i1.6734.
R. Kosasih, “Penggunaan Metode Linear Discriminant Analysis Untuk Pengenalan Wajah Dengan Membandingkan Banyaknya Data Latih,” J. Ilm. Teknol. dan Rekayasa, vol. 26, no. 1, pp. 25–34, 2021, doi: 10.35760/tr.2021.v26i1.3520.
N. Chintalapudi et al., “LASSO Regression Modeling on Prediction of Medical Terms among Seafarers’ Health Documents Using Tidy Text Mining,” Bioengineering, vol. 9, no. 3, pp. 1–14, 2022, doi: 10.3390/bioengineering9030124.
D. Nurmalasari and W. Nengsih, “Sistem Rekomendasi Referensi Jurnal Ilmiah dengan Metode Frequent Itemset,” vol. 5, no. 2, pp. 176–181, 2021.
A. Dinanti and J. Purwadi, “Analisis Performa Algoritma K-Nearest Neighbor dan Reduksi Dimensi Menggunakan Principal Component Analysis,” vol. 5, no. 1, pp. 155–165, 2023.
F. Fitrianingsih and S. Sugiyarto, “Implementasi Analisa Komponen Utama untuk Mereduksi Variabel yang Mempengaruhi Perbaikan pada Fungsi Ginjal Tikus,” J. Ilm. Mat., vol. 6, no. 2, p. 62, 2019, doi: 10.26555/konvergensi.v6i2.19549.
T. Informasi et al., “Jurnal Pengenalan Wajah menggunakan Principle Component Analysis ( PCA ) dengan Model Algoritma Machine Learning untuk Mengidentifikasi Jenis Kelamin pada Kartu Identitas Mahasiswa Face Recognition using Principle Component Analysis ( PCA ) with Machine L,” vol. 5, pp. 219–224, 2023.
R. Destriana, D. Nurnaningsih, and ..., “Implementasi Metode Linear Discriminant Analysis (LDA) Pada Klasifikasi Tingkat Kematangan Buah Nanas,” … Sci., vol. 3, no. 1, pp. 56–63, 2021, doi: 10.47065/bits.v3i1.1007.
M. S. Tjahaya, Raupong, and G. M. Tinungki, “Analisis Dsikriminan Linear Robust Dengan Metode Winsorized Modified One-Step M-Estimator,” J. Estimasi, vol. 3, no. 1, pp. 1–13, 2022, doi: 10.20956/ejsa.vi.11302.
F. A. D. Aji Prasetya Wibawa, Muhammad Guntur Aji Purnama, Muhammad Fathony Akbar, “Metode-metode Klasifikasi,” Pros. Semin. Ilmu Komput. dan Teknol. Inf., vol. 3, no. 1, p. 134, 2018.
H. Tan, “Machine Learning Algorithm for Classification,” J. Phys. Conf. Ser., vol. 1994, no. 1, 2021, doi: 10.1088/1742-6596/1994/1/012016.
A. Tharwat, T. Gaber, A. Ibrahim, and A. E. Hassanien, “Linear discriminant analysis: A detailed tutorial,” AI Commun., vol. 30, no. 2, pp. 169–190, 2017, doi: 10.3233/AIC-170729.
P. Boedeker and N. T. Kearns, “Linear Discriminant Analysis for Prediction of Group Membership: A User-Friendly Primer,” Adv. Methods Pract. Psychol. Sci., vol. 2, no. 3, pp. 250–263, 2019, doi: 10.1177/2515245919849378.
I. Rodrigues, A. Parayil, T. Shetty, and I. Mirza, “Use of Linear Discriminant Analysis (LDA), K Nearest Neighbours (KNN), Decision Tree (CART), Random Forest (RF), Gaussian Naive Bayes (NB), Support Vector Machines (SVM) to Predict Admission for Post Graduation Courses,” SSRN Electron. J., 2020, doi: 10.2139/ssrn.3683065.
G. O. Yusuf, A. K. Jaya, and N. Ilyas, “Pemodelan Regresi Logistik Menggunakan Metode Momen Diperumum,” ESTIMASI J. Stat. Its Appl., vol. 1, no. 2, p. 74, 2020, doi: 10.20956/ejsa.v1i2.9304.
A. A. T. Fernandes, D. B. F. Filho, E. C. da Rocha, and W. da Silva Nascimento, “Read this paper if you want to learn logistic regression,” Rev. Sociol. e Polit., vol. 28, no. 74, pp. 1/1-19/19, 2020, doi: 10.1590/1678-987320287406EN.
Z. A. A. Al-Bairmani and A. A. Ismael, “Using Logistic Regression Model to Study the Most Important Factors Which Affects Diabetes for the Elderly in the City of Hilla / 2019,” J. Phys. Conf. Ser., vol. 1818, no. 1, 2021, doi: 10.1088/1742-6596/1818/1/012016.
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