Comprehensive Overview of Principal Component Analysis Applications and Developments

Faris Alaudin Shalih; Rafli Akbar Ramadhan; Najlaa Syalaisa

doi:10.17509/jem.v13i1.75863

Comprehensive Overview of Principal Component Analysis Applications and Developments

Faris Alaudin Shalih, Rafli Akbar Ramadhan, Najlaa Syalaisa

Abstract

Principal Component Analysis (PCA) is a popular statistical method for reducing data dimensionality in complex data analysis. Developed since the early 20th century by Pearson and Hotelling, PCA has become an important tool in various disciplines. This article reviews the application of PCA in various industries to explain the working principles and key steps in this method, such as data normalization, covariance matrix calculation, eigenvalue decomposition, and data projection. The review also includes previous studies applying PCA in healthcare, remote sensing, finance, agriculture, education, and geology. The results show that PCA effectively reduces data dimensionality, improves accuracy, and identifies significant features in each application. PCA also contributes to accurate data-driven decision-making. To date, PCA remains applicable and has wide applicability in various fields, proving its effectiveness in facing the growing challenges of modern data.

Keywords: Analysis (PCA), Dimensionality Reduction, PCA Applications, PCA Methodology, PCA Timeline, Principal Component.

Abstrak

Principal Component Analysis (PCA) adalah metode statistik yang populer untuk mereduksi dimensi data dalam analisis data kompleks. Dikembangkan sejak awal abad ke-20 oleh Pearson dan Hotelling, PCA telah menjadi alat penting di berbagai disiplin ilmu. Artikel ini mengulas penerapan PCA di berbagai industri untuk menjelaskan prinsip kerja dan langkah-langkah utama dalam metode ini, seperti normalisasi data, perhitungan matriks kovarians, dekomposisi nilai eigen, dan proyeksi data. Ulasan juga mencakup studi-studi terdahulu yang menerapkan PCA di bidang kesehatan, penginderaan jauh, finansial, pertanian, pendidikan, dan geologi. Hasilnya menunjukkan bahwa PCA efektif dalam mengurangi dimensi data, meningkatkan akurasi, dan mengidentifikasi fitur signifikan pada setiap aplikasi. PCA juga berkontribusi pada pengambilan keputusan berbasis data yang akurat. Hingga saat ini, PCA tetap aplikatif dan memiliki penerapan yang luas di berbagai bidang, membuktikan efektivitasnya pada data modern yang terus berkembang.

Keywords

Aplikasi PCA, Linimasa PCA, Metodologi PCA, Principal Component Analysis (PCA), Reduksi Dimensi.

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DOI: https://doi.org/10.17509/jem.v13i1.75863