Klasifikasi Penyakit TBC Menggunakan Metode UMAP dan K-NN
DOI:
https://doi.org/10.32877/bt.v7i3.2227
Keywords:
Confusion Matrix , K-Nearest Neighbors, Metode Elbow, Tuberkulosis , UMAP
Abstract
Tuberkulosis (TBC) adalah penyakit infeksi yang disebabkan oleh bakteri Mycobacterium tuberculosis, yang dapat menyebar dengan cepat melalui udara. Deteksi dini yang akurat sangat penting dalam penanganan penyakit ini untuk mencegah penyebaran lebih lanjut serta meningkatkan efektivitas pengobatan. Diagnosis yang tidak tepat dapat menyebabkan keterlambatan dalam pengobatan, sehingga meningkatkan risiko komplikasi serius bagi pasien. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan sistem klasifikasi TBC menggunakan metode Uniform Manifold Approximation and Projection (UMAP) dan K-Nearest Neighbors (K-NN) di Puskesmas Prabumulih Timur. Dataset yang digunakan terdiri dari 278 data pasien dengan berbagai atribut klinis terkait gejala TBC. Proses analisis diawali dengan tahap pra-pemrosesan data, termasuk penghapusan data duplikat, encoding data kategorikal, serta penanganan nilai yang hilang. Untuk meningkatkan akurasi klasifikasi, metode Elbow diterapkan guna menentukan nilai K optimal, dengan hasil terbaik pada K=3. Data kemudian dibagi menjadi 80% data pelatihan dan 20% data uji guna menghindari overfitting dan meningkatkan reliabilitas model. Pengujian dilakukan dengan membandingkan dua skenario, yaitu K-NN tanpa UMAP dan K-NN dengan UMAP. Hasil evaluasi menggunakan Confusion Matrix menunjukkan bahwa penerapan UMAP meningkatkan accuracy dari 93,48% menjadi 100%, dengan precision dan recall juga mencapai nilai maksimal. Penelitian ini berkontribusi dalam pengembangan sistem klasifikasi berbasis machine learning yang lebih akurat dan efisien untuk membantu tenaga medis dalam mendiagnosis TBC secara cepat, tepat, dan optimal dalam sistem layanan kesehatan.
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