Tinjauan Literatur: Deteksi Kanker Serviks Dengan Pendekatan Machine Learning
DOI:
https://doi.org/10.26418/justin.v13i4.95910Keywords:
Kanker Serviks, Deteksi Dini, Machine Learning, Systematic Literature Review, Seleksi FiturAbstract
Kanker serviks merupakan salah satu jenis kanker yang masih menjadi penyebab utama kematian pada wanita di seluruh dunia. Penyakit ini berkembang secara perlahan dan sering kali tidak menunjukkan gejala pada tahap awal, sehingga deteksi dini menjadi kunci utama dalam upaya pencegahan dan pengobatan. Saat ini, metode deteksi seperti Pap smear dan tes HPV telah banyak digunakan. Namun keterbatasan sumber daya medis dan tantangan dalam akurasi diagnosis masih menjadi hambatan. Dengan kemajuan teknologi, algoritma machine learning mulai dimanfaatkan untuk mendukung proses deteksi kanker serviks secara cepat dan akurat. Penelitian ini bertujuan untuk meninjau secara sistematis penerapan algoritma machine learning dalam deteksi kanker serviks melalui pendekatan Systematic Literature Review (SLR). Sebanyak 243 artikel diidentifikasi dan 42 artikel dipilih untuk dianalisis menggunakan metode PRISMA yang mencakup tahapan identifikasi, penyaringan, evaluasi kelayakan, dan inklusi akhir. Analisis dilakukan terhadap algoritma yang digunakan, variabel prediktor, teknik seleksi fitur, serta jenis dan ukuran dataset. Hasil tinjauan menunjukkan bahwa dataset UCI Cervical Cancer, SEER Database, dan Herlev Dataset merupakan yang paling sering digunakan dengan ukuran bervariasi dari 92 hingga lebih dari 381.000 data. Variabel usia, penggunaan kontrasepsi, dan jumlah pasangan seksual merupakan indikator yang paling sering muncul. Model yang paling banyak diterapkan adalah Random Forest, Decision Tree, Support Vector Machine (SVM), XGBoost, dan Multilayer Perceptron (MLP) dengan akurasi berkisar 82% - 100% yang menunjukkan performa tinggi terutama setelah dilakukan tuning. Selain itu, metode seleksi fitur seperti Chi-Square, LASSO, dan Principal Component Analysis (PCA) berkontribusi dalam meningkatkan akurasi model. Walaupun hasilnya menunjukkan potensi yang baik, penelitian yang ditinjau masih terbatas, seperti ketidakseimbangan kelas, kurangnya validasi eksternal, dan perbedaan metode evaluasi yang memengaruhi kemampuan generalisasi model. Penelitian selanjutnya disarankan untuk memanfaatkan dataset yang lebih beragam, menerapkan metode penyeimbangan data yang lebih baik, serta memperluas validasi pada populasi yang berbeda guna meningkatkan keandalan deteksi dini kanker serviks berbasis machine learning.References
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