Klasifikasi Risiko Diabetes Mellitus Menggunakan K-Nearest Neighbors dengan Peningkatan Performa Melalui Teknik Oversampling ADASYN
-
Muhammad Bagir
*
Sekolah Tinggi Teknologi Informasi NIIT, Jakarta,
Indonesia
- Hendra Mayatopani Universitas Pradita, Tangerang, Indonesia
- Umbar Riyanto Universitas Muhammadiyah Tangerang, Tangerang, Indonesia
- Dedy Alamsyah Universitas Muhammadiyah Tangerang, Tangerang, Indonesia (*) Corresponding Author
Abstract
Diabetes mellitus is a chronic metabolic disease with a continuously increasing global prevalence. Early detection of diabetes risk is crucial to reduce long-term health complications and the associated healthcare costs. However, a major challenge in applying machine learning models to medical data is the issue of class imbalance, which can lead to model bias toward the majority class. This study aims to develop a diabetes risk classification model by integrating the K-Nearest Neighbors (KNN) algorithm with the Adaptive Synthetic Sampling (ADASYN) technique to address the class imbalance problem. The dataset used was obtained from the Kaggle platform, containing 2,000 patient samples with nine predictive features. Data preprocessing was performed through missing value imputation, outlier handling using winsorizing, and feature normalization using StandardScaler. ADASYN was applied to generate adaptive synthetic samples for the minority class, and the KNN model was trained and evaluated using confusion matrix, precision, recall, F1-Score, accuracy, and ROC-AUC metrics. The results indicate that the implementation of ADASYN improved the ROC-AUC Score by 5.48% (from 91.34% to 96.82%) and the overall accuracy by 2.50% (from 81.50% to 84.00%). The F1-Score for the Diabetes class also increased by 0.40%. The integration of KNN and ADASYN has proven effective in enhancing model performance for detecting high-risk diabetes patients and improving sensitivity toward the minority class.
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References
D. Priyantini, N. A. Sari, and P. A. Hanggitriana, “Indeks Massa Tubuh Pada Penderita Diabetes Melitus Dengan Nilai Ankle Brachial Index,” J. Ilm. Keperawatan Stikes Hang Tuah Surabaya, vol. 17, no. 2, pp. 144–149, 2022.
A. Aminuddin, Yenny Sima, Nurril Cholifatul Izza, Nur Syamsi Norma Lalla, and Darmi Arda, “Edukasi Kesehatan Tentang Penyakit Diabetes Melitus bagi Masyarakat,” Abdimas Polsaka, pp. 7–12, 2023, doi: 10.35816/abdimaspolsaka.v2i1.25.
M. A. Sembiring, H. Saputra, R. A. Yusda, S. Sutarman, and E. B. Nababan, “Performance of Robust Support Vector Machine Classification Model on Balanced, Imbalanced and Outliers Datasets,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 10, no. 1, pp. 208–215, 2024, doi: 10.33480/jitk.v10i1.5272.
N. Maulidah, R. Supriyadi, D. Y. Utami, F. N. Hasan, A. Fauzi, and A. Christian, “Prediksi Penyakit Diabetes Melitus Menggunakan Metode Support Vector Machine dan Naive Bayes,” Indones. J. Softw. Eng., vol. 7, no. 1, pp. 63–68, 2021, doi: 10.31294/ijse.v7i1.10279.
G. Abdurrahman, “Klasifikasi Penyakit Diabetes Melitus Menggunakan Adaboost Classifier,” JUSTINDO (Jurnal Sist. Teknol. Inf. Indones., vol. 7, no. 1, pp. 59–66, 2022.
L. Safitri and Z. Fatah, “Implementasi Prediksi Penyakit Diabetes Menggunakan Metode Decision Tree,” JUSIFOR J. Sist. Inf. dan Inform., vol. 2, no. 2, pp. 125–132, 2023, doi: 10.70609/jusifor.v3i2.5788 .
S. P. Nainggolan and A. Sinaga, “Comparative Analysis of Accuracy of Random Forest and Gradient Boosting Classifier Algorithm for Diabetes Classification,” Sebatik, vol. 27, no. 1, pp. 97–102, 2023, doi: 10.46984/sebatik.v27i1.2157.
P. R. Putri and R. Alit, “Klasifikasi Penyakit Diabetes menggunakan Metode Support Vector Machine,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 3, pp. 740–746, 2024.
A. Yogianto, A. Homaidi, and Z. Fatah, “Implementasi Metode K-Nearest Neighbors (KNN) untuk Klasifikasi Penyakit Jantung,” G-Tech J. Teknol. Terap., vol. 8, no. 3, pp. 1720–1728, 2024, doi: 10.33379/gtech.v8i3.4495.
S. G. Barus, “Klasifikasi Sentimen Data Tidak Seimbang Menggunakan Algoritma SMOTE dan K-Nearest Neighbor Pada Ulasan Pengguna Aplikasi Pedulilindungi,” in Seminar Nasional Mahasiswa Ilmu Komputer dan Aplikasinya (SENAMIKA), 2022, pp. 162–173.
M. T. T. B. Sirait, N. S. Fathonah, and M. N. Fauzan, “Pemanfaatan Algoritma ADASYN dan Support Vector Machine Dalam Meningkatkan Akurasi Prediksi Kanker Paru-Paru,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 5, pp. 8773–8778, 2024.
M. I. Anugrah, J. Zeniarja, and D. S. Setiawan, “Peningkatan Performa Model Hard Voting Classifier dengan Teknik Oversampling ADASYN pada Penyakit Diabetes,” Edumatic J. Pendidik. Inform., vol. 8, no. 1, pp. 290–299, 2024, doi: 10.29408/edumatic.v8i1.25838.
R. I. Borman, R. Napianto, N. Nugroho, D. Pasha, Y. Rahmanto, and Y. E. P. Yudoutomo, “Implementation of PCA and KNN Algorithms in the Classification of Indonesian Medicinal Plants,” in International Conference on Computer Science, Information Technology and Electrical Engineering (ICOMITEE), IEEE, 2021, pp. 46–50.
J. Dasilva, “Diabetes Dataset,” Kaggle. [Online]. Available: https://www.kaggle.com/datasets/johndasilva/diabetes
I. Ahmad, A. Tri Prastowo, R. Indra Borman, M. Tonggiroh, and Y. Jusman, “Lung Cancer Classification from CT Scan Images Using the LVQ Algorithm and GLCM Feature Extraction with Spatial Filters,” in International Conference on Information Technology and Computing (ICITCOM), IEEE, 2024, pp. 99–104.
I. O. Muraina, “Ideal Dataset Splitting Ratios in Machine Learning Algorithms: General Concerns for Data Scientists and Data Analysts,” in International Mardin Artuklu Scientific Researches Conference, 2022, pp. 496–505.
L. Muflikhah, F. A. Bachtiar, D. E. Ratnawati, and R. Darmawan, “Improving Performance for Diabetic Nephropathy Detection Using Adaptive Synthetic Sampling Data in Ensemble Method of Machine Learning Algorithms,” J. Ilm. Tek. Elektro Komput. dan Inform., vol. 10, no. 1, p. 123, 2024, doi: 10.26555/jiteki.v10i1.28107.
I. P. Putri, “Analisis Performa Metode K- Nearest Neighbor (KNN) dan Crossvalidation pada Data Penyakit Cardiovascular,” Indones. J. Data Sci., vol. 2, no. 1, pp. 21–28, 2021, doi: 10.33096/ijodas.v2i1.25.
Z. Abidin, R. I. Borman, F. B. Ananda, P. Prasetyawan, F. Rossi, and Y. Jusman, “Classification of Indonesian Traditional Snacks Based on Image Using Convolutional Neural Network (CNN) Algorithm,” in International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), IEEE, 2022, pp. 18–23.
Y. Liu, Y. Li, and D. Xie, “Implications of imbalanced datasets for empirical ROC-AUC estimation in binary classification tasks,” J. Stat. Comput. Simul., vol. 94, no. 1, pp. 183–203, Jan. 2024, doi: 10.1080/00949655.2023.2238235.
I. Pratama, A. Y. Chandra, and P. T. Presetyaningrum, “Seleksi Fitur dan Penanganan Imbalanced Data menggunakan RFECV dan ADASYN,” J. Eksplora Inform., vol. 11, no. 1, pp. 38–49, 2022, doi: 10.30864/eksplora.v11i1.578.
R. A. Nurdian, Mujib Ridwan, and Ahmad Yusuf, “Komparasi Metode SMOTE dan ADASYN dalam Meningkatkan Performa Klasifikasi Herregistrasi Mahasiswa Baru,” J. Tek. Inform. dan Sist. Inf., vol. 8, no. 1, pp. 24–32, 2022, doi: 10.28932/jutisi.v8i1.4004.
S. Sumarlinda and W. Lestari, “Aplikasi K-Nearest Neighbor (KNN) untuk Klasifikasi Penyakit Kardiovaskuler,” in Sumarlinda, Sri Lestari, Wiji, 2022, pp. 259–262.
M. N. Maskuri, K. Sukerti, and R. M. Herdian Bhakti, “Penerapan Algoritma K-Nearest Neighbor (KNN) untuk Memprediksi Penyakit Stroke Stroke Desease Predict Using KNN Algorithm,” J. Ilm. Intech Inf. Technol. J. UMUS, vol. 4, no. 1, pp. 130–140, 2022.
A. Razaki, Y. H. Chrisnanto, and M. Melina, “Penanganan Outlier Pada Metode Algoritma K- Nearest Neighbors (KNN) Dengan Metode Kernel Density Estimation Pada Kasus Penyakit Diabetes,” INTECOMS J. Inf. Technol. Comput. Sci., vol. 7, no. 4, pp. 1177–1188, 2024, doi: 10.31539/intecoms.v7i4.10866.
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