Pembelajaran Vektor Untuk Klasifikasi Data Pada Bidang
DOI:
https://doi.org/10.35706/sjme.v4i2.3515Abstract
Tujuan penelitian ini adalah penyusunan hyperplane untuk
memisahkan data yang mempunyai 2 kelas dan bersifat linear pada
bidang datar sebagai pembelajaran vektor untuk klasifikasi data.
Adapun metode yang digunakan adalah pre-Support Vector Machine
(SVM). Metode ini mencari garis (hyperplane) terbaik yang
memisahkan data dan memberi ruang antar 2 kelas data dimana ruang
pemisah tersebut tidak boleh memuat data serta ruang tersebut
merupakan margin maksimal. Langkah awal adalah menduga garis
pemisah (hyperplane) awal melalui titik O. Dengan mengambil salah
satu titik data yang menjadi titik referensi, disusun vektor dari O
terhadap titik referensi dan garis melalui titik referensi sebagai batas
pertama margin. Kemudian dibentuk vektor arah dari titik O yang
tegak lulus terhadap garis awal (hyperplane). Selanjutnya vektor
proyeksi dibentuk dari titik referensi terhadap vektor arah sehingga
vektor arah dan vektor proyeksi berhimpit (searah). Penyusunan
margin diperoleh dengan menyusun garis yang pararel terhadap garis
awal sebagai hyperplane serta berjarak 2 kali dengan panjang vektor
proyeksi tersebut. Hyperplane terbaik diperoleh secara manual dengan
mengatur batas kedua dari margin yang diperoleh dengan menggambar
garis melalui suatu titik data pada kelas ke-2 dengan jarak terdekat dan
pararel terhadap garis yang melalui titik referensi dari data kelas ke-1.
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References
Boulet, J., & Roger, J. (2018). A review of orthogonal projections for calibration. Journal of Chemometric. https://doi.org/https://doi.org/10.1002/cem.3045
Claudia Orozco, Rodríguez Erla M. Morales, M. F., & Gonçalves da Silva Cordeiro, M. (2015). Learning Objects and Geometric Representation for Teaching “Definition and Applications of Geometric Vector.” Journal of Cases on Information Technology (JCIT), 17(1). https://doi.org/10.4018/JCIT.2015010102
Demidova, L., Nikulchev, E., & Sokolova, Y. (2016). Big Data Classification Using the SVM Classifiers with the Modified Particle Swarm Optimization and the SVM Ensembles. International Journal of Advanced Computer Science and Applications, 7(5), 294–312. https://doi.org/10.14569/ijacsa.2016.070541
Boulet, J., & Roger, J. (2018). A review of orthogonal projections for calibration. Journal of Chemometric. https://doi.org/https://doi.org/10.1002/cem.3045
Claudia Orozco, Rodríguez Erla M. Morales, M. F., & Gonçalves da Silva Cordeiro, M. (2015). Learning Objects and Geometric Representation for Teaching “Definition and Applications of Geometric Vector.” Journal of Cases on Information Technology (JCIT), 17(1). https://doi.org/10.4018/JCIT.2015010102
Demidova, L., Nikulchev, E., & Sokolova, Y. (2016). Big Data Classification Using the SVM Classifiers with the Modified Particle Swarm Optimization and the SVM Ensembles. International Journal of Advanced Computer Science and Applications, 7(5), 294–312. https://doi.org/10.14569/ijacsa.2016.070541.
Fatma, F., Abdallah, Hany, W., & Darwish, I. A. (2019). Orthogonal projection to latent structures and first derivative for manipulation of PLSR and SVR chemometric models’ prediction: A case study. Plos One. https://doi.org/https://doi.org/10.1371/journal.pone.0222197.
Hakan, C. (2016). Best Fitting Hyperplanes for Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1076–1088. https://doi.org/10.1109/TPAMI.2016.2587647.
Kowalczyk, A. (2017). Support Vector Machines Succintctly. www.syncfusion.com.
Kwanghee, K., & Takis, S. (2014). Orthogonal projection of points in CAD/CAM applications: an overview. Journal of Computational Design and Engineering, Volume 1,(2), 116–127. https://doi.org/https://doi.org/10.7315/JCDE.2014.012.
Min-Wei, H., Chih-Wen, C., Wei-Chao, L., Shih-Wen, K., & Chih-Fong, T. (2018). SVM and SVM Ensembles in Breast Cancer Prediction. Plos One, 12. https://doi.org/10.1371/journal.pone.0161501
Moh. Yamin Darsyah. (2013). Menakar Tingkat Akurasi Support Vector Machine Study Kasus Kanker Payudara. Statistika Universitas Muhammadiyah, 1(1), 15–20.
Poluakan, & Runtuwene, J. (2018). Students’ difficulties regarding vector representations in free-body system. Journal of Physics Conference Series, 1120(1).
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