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All Journal Indonesian Journal of Health Science Madani: Multidisciplinary Scientific Journal
Sarah, Auta Shinta
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Perbandingan mtode K-Nearest Neighbors (K-NN) dan regresi logistik biner dalam memprediksi kanker Surbakti, Christina Amanda; Sinaga, Albert Samuel; Simorangkir, Agnes Monica; Sarah, Auta Shinta; Harefa, Clara Jocelyn; Dalimunthe, Syairal Fahmy
Indonesian Journal of Health Science Vol 5 No 2 (2025)
Publisher : PT WIM Solusi Prima

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54957/ijhs.v5i2.1456

Abstract

Latar Belakang: Kanker merupakan salah satu penyakit yang memiliki tingkat kematian tinggi, sehingga dibutuhkan metode klasifikasi yang akurat untuk mendukung proses diagnosis. Penelitian ini membandingkan performa metode K-Nearest Neighbors (KNN) dan Regresi Logistik Biner dalam mengklasifikasikan kanker sebagai ganas atau jinak. Metode: Penelitian ini menggunakan dataset sekunder dari Kaggle yang terdiri dari 569 data pasien kanker dengan 11 variabel independen yang mencakup karakteristik tumor. Model dikembangkan dengan menggunakan normalisasi data, pembagian data training dan testing, serta teknik K-Fold Cross Validation untuk optimasi parameter K dalam KNN. Evaluasi model dilakukan berdasarkan akurasi, presisi, recall, serta uji McNemar dan ANOVA untuk menguji signifikansi perbedaan performa model. Hasil: Model KNN dengan K=13 menunjukkan akurasi 95,58%, presisi 95,83%, dan recall 97,18%, sementara Regresi Logistik Biner memiliki akurasi 94,69%, presisi 92,86%, dan recall 92,86%. Hasil uji McNemar menunjukkan bahwa tidak terdapat perbedaan signifikan antara kedua model (p-value = 1), sedangkan hasil ANOVA menunjukkan bahwa semua variabel independen berkontribusi terhadap model. Kesimpulan: Kedua metode menunjukkan performa yang baik dalam klasifikasi kanker, tetapi KNN dengan K=13 memiliki sedikit keunggulan dalam akurasi dan recall dibandingkan Regresi Logistik Biner. Implementasi model ini dapat mendukung sistem pendukung keputusan dalam diagnosis kanker untuk meningkatkan ketepatan hasil klasifikasi.
Perbandingan Metode K-Nearest Neighbors (K-NN) dan Regresi Logistik Biner Dalam Memprediksi Kanker Surbakti, Christina Amanda; Sinaga, Albert Samuel; Simorangkir, Agnes Monica; Sarah, Auta Shinta; Harefa, Clara Jocelyn; Dalimunthe, Syairal Fahmy
Madani: Jurnal Ilmiah Multidisiplin Vol 3, No 2 (2025): March
Publisher : Penerbit Yayasan Daarul Huda Kruengmane

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15074826

Abstract

Background: Cancer is one of the diseases with a high mortality rate, so an accurate classification method is needed to support the diagnosis process. This study compares the performance of the K-Nearest Neighbors (KNN) method and Binary Logistic Regression in classifying cancer as malignant or benign. Methods: This study used a secondary dataset from Kaggle consisting of 569 cancer patient data with 11 independent variables covering tumor characteristics. The model was developed using data normalization, training and testing data division, and the K-Fold Cross Validation technique to optimize the K parameter in KNN. Model evaluation was carried out based on accuracy, precision, recall, and the McNemar and ANOVA tests to test the significance of differences in model performance. Results: The KNN model with K=13 showed an accuracy of 95.58%, a precision of 95.83%, and a recall of 97.18%, while Binary Logistic Regression had an accuracy of 94.69%, a precision of 92.86%, and a recall of 92.86%. The McNemar test results showed that there was no significant difference between the two models (p-value = 1), while the ANOVA results showed that all independent variables contributed to the model. Conclusion: Both methods performed well in cancer classification, but KNN with K=13 had a slight advantage in accuracy and recall compared to Binary Logistic Regression. The implementation of this model can support decision support systems in cancer diagnosis to improve the accuracy of classification results. 
Co-Authors Harefa, Clara Jocelyn Simorangkir, Agnes Monica Sinaga, Albert Samuel Surbakti, Christina Amanda Syairal Fahmy Dalimunthe