Algoritma Supervised Machine Learning Untuk Klasifikasi Diagnosa Penyakit Hipertensi
DOI:
https://doi.org/10.31294/ijcs.v5i1.11141Keywords:
hipertensi, klasifikasi, machine learning, SVMAbstract
Setiap tahun, prevalensi penderita hipertensi mengalami peningkatan signifikan, dan diperkirakan tahun 2025, total pasien hipertensi akan menyentuh 1,5 miliar jiwa. Salah satu metode dalam data mining yang sering digunakan untuk klasifikasi adalah Support Vector Machine (SVM). Metode SVM berupaya menemukan hyperplane atau fungsi batas keputusan terbaik yang mampu memisahkan dua kelas atau lebih dari data dalam ruang masukan. Penelitian ini bertujuan untuk menentukan hasil klasifikasi dan akurasi diagnosis hipertensi menggunakan metode SVM. Sebelas atribut yang digunakan meliputi usia, kebiasaan merokok, aktivitas fisik, mengkonsumsi gula, mengkonsumsi garam, mengkonsumsi lemak, mengkonsumsi alkohol, kurangnya mengkonsumsi buah dan sayur, serta tekanan darah sistolik dan diastolik. Penelitian ini akan memanfaatkan perangkat lunak Jupyter Notebook dan bahasa pemrograman Python sebagai instrumen penelitian. Metode SVM dilatih dengan berbagai atribut kernel dan hyperparameter untuk menghasilkan model terbaik. Hasil penelitian menunjukkan bahwa kernel RBF dengan parameter C=100 dan 𝛾=0,1menghasilkan akurasi sebesar 97,7%, menjadikannya model terbaik dalam mengklasifikasikan hipertensi. Dari hasil ini, dapat disimpulkan bahwa metode SVM mampu menghasilkan klasifikasi diagnosis hipertensi yang sangat baik dan dapat memberikan diagnosis untuk mendeteksi hipertensi secara dini. Lebih jauh lagi, luaran penelitian ini diharapkan berkontribusi dalam menekan angka mortalitas akibat hipertensi dan berpotensi untuk diaplikasikan lebih luas demi kemaslahatan masyarakat.
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