PREDIKSI KANKER SERVIKS MENGGUNAKAN METODE STACKING ENSEMBLE TABNET, RANDOM FOREST, DAN LIGHT GRADIENT BOOSTING MACHINE

PREDIKSI KANKER SERVIKS MENGGUNAKAN METODE STACKING ENSEMBLE TABNET, RANDOM FOREST, DAN LIGHT GRADIENT BOOSTING MACHINE

• By Mahendra;
• Tesis
• 24
• 1-37

Penelitian ini mengevaluasi akurasi dari beberapa algoritma klasifikasi dalam mendeteksi dini kanker serviks, dengan menjelaskan bahwa Random Fores (RF) menunjukkan kinerja terbaik, dengan mencapai skor akurasi tinggi sebesar 0.978 untuk metode tes skrining deteksi dini kanker serviks Hinselmann dan 0.969 untuk metode tes skrining Biopsy. Praproses dataset dilakukan sebelum penerapan algoritma klasifikasi dilakukan yang meliputi penanganan data yang hilang menggunakan metode Mice Forest Iterative Imputer, serta penanganan ketidakseimbangan kelas dengan teknik Adaptive Synthetic Sampling (ADASYN). Metode seperti Support Vector Machine (SVM) dan K-Nearest Neighbors (KNN) memperlihatkan kinerja yang cukup baik, akan tetapi teknik Ensemble secara significant mampu meningkatkan kemampuan prediksi. Penerapan metode Ensemble Stacking (Tabnet, RF, dan LightGBM) yang mencapai akurasi 1 pada Hinselmann dan Biopsy, ini menunjukkan keefektifitasan dari kombinasi beberapa algoritma. Dengan hasil tersebut, peneliti telah berhasil menunjukkan pentingnya untuk memilih metode klasifikasi yang tepat dalam mendeteksi dini kanker serviks, serta menunjukkan bahwa model Ensemble yang canggih dapat mengarah pada peningkatan prediksi deteksi dini yang lebih baik.

• Katakunci Kanker serviks, Pembelajaran Mesin, Model Prediksi, Data Mining, Metode Ensemble
• URL http://
• Penerbit Universitas Nusa Mandiri,

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