FA-KNN: Hybrid Algoritma Untuk Klasifikasi Penyakit Diabetes Melitus
Keywords:
firefly algorithm, knn, machine learning, classification
Abstract
Penanganan yang tepat dan tepat waktu dari Diabetes Melitus menjadi sangat penting karena penyakit ini dapat menyebabkan berbagai komplikasi serius. Komplikasi jangka panjang meliputi gangguan pada mata (retinopati), ginjal (nefropati), saraf (neuropati), jantung dan pembuluh darah (kardiovaskular), serta risiko luka yang sulit sembuh hingga amputasi pada ekstremitas. Penelitian ini bertujuan untuk menerapkan Firefly Algorithm (FA) atau algoritma kunang-kunang dan KNN dalam melakukan klasifikasi terhadap penyakit diabetes melitus, dimana FA akan digunakan untuk melakukan pencarian parameter yang paling optimal untuk KNN. Metode penelitian yang digunakan yaitu metode eksperimen dengan melakukan skenario perubahan pada jumlah populasi kunang-kunang dan juga perubahan nilai k-fold validation untuk melakukan pembagian dataset. Hasil akurasi terbaik didapatkan pada populasi 100 dan 150 dengan nilai k=5 yaitu sebesar 76.3% dengan parameter K pada KNN yang diperoleh yaitu 15 dan P adalah 2
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[10] L. U. Khasanah and Y. N. Nasution, “Klasifikasi Penyakit Diabetes Melitus Menggunakan Algoritma Naïve Bayes Classifier,” 2022.
[11] N. M. Putry, B. N. Sari, and M. Kom, “KOMPARASI ALGORITMA KNN DAN NAÏVE BAYES UNTUK KLASIFIKASI DIAGNOSIS PENYAKIT DIABETES MELITUS,” vol. 10, no. 1, 2022.
[12] H. Apriyani and K. Kurniati, “Perbandingan Metode Naïve Bayes Dan Support Vector Machine Dalam Klasifikasi Penyakit Diabetes Melitus,” J. Inf. Technol. Ampera, vol. 1, no. 3, pp. 133–143, Dec. 2020, doi: 10.51519/journalita.volume1.isssue3.year2020.page133-143.
[13] A. Yahyaoui, A. Jamil, J. Rasheed, and M. Yesiltepe, “A Decision Support System for Diabetes Prediction Using Machine Learning and Deep Learning Techniques,” in 2019 1st International Informatics and Software Engineering Conference (UBMYK), Ankara, Turkey: IEEE, Nov. 2019, pp. 1–4. doi: 10.1109/UBMYK48245.2019.8965556.
[14] G. T. Reddy et al., “An Ensemble based Machine Learning model for Diabetic Retinopathy Classification,” in 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), Vellore, India: IEEE, Feb. 2020, pp. 1–6. doi: 10.1109/ic-ETITE47903.2020.235.
[15] Md. K. Hasan, Md. A. Alam, D. Das, E. Hossain, and M. Hasan, “Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers,” IEEE Access, vol. 8, pp. 76516–76531, 2020, doi: 10.1109/ACCESS.2020.2989857.
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[17] S. Ulya, M. A. Soeleman, and F. Budiman, “Optimasi Parameter K Pada Algoritma K-NN Untuk Klasifikasi Prioritas Bantuan Pembangunan Desa,” Techno.Com, vol. 20, no. 1, pp. 83–96, Feb. 2021, doi: 10.33633/tc.v20i1.4215.
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[20] K. W. Mahardika, Y. A. Sari, and A. Arwan, “Optimasi K-Nearest Neighbour Menggunakan Particle Swarm Optimization pada Sistem Pakar untuk Monitoring Pengendalian Hama pada Tanaman Jeruk”.
[21] M. Pei, E. D. Goodman, and W. F. Punch, “Feature Extraction Using Genetic Algorithms”.
[22] L. Zhang, K. Mistry, C. P. Lim, and S. C. Neoh, “Feature selection using firefly optimization for classification and regression models,” Decis. Support Syst., vol. 106, pp. 64–85, Feb. 2018, doi: 10.1016/j.dss.2017.12.001.
[23] M. A. Kahya, S. A. Altamir, and Z. Y. Algamal, “Improving firefly algorithm-based logistic regression for feature selection,” J. Interdiscip. Math., vol. 22, no. 8, pp. 1577–1581, Nov. 2019, doi: 10.1080/09720502.2019.1706861.
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[28] D. A. Nggego, A. Setyanto, and Sukoco, “Implementation of the Firefly Algorithm in the Case of N-Queens Problem,” J. RESTI Rekayasa Sist. Dan Teknol. Inf., vol. 4, no. 2, pp. 219–227, Apr. 2020, doi: 10.29207/resti.v4i2.1506.
[29] H. Wisnu, M. Afif, and Y. Ruldevyani, “Sentiment analysis on customer satisfaction of digital payment in Indonesia: A comparative study using KNN and Naïve Bayes,” J. Phys. Conf. Ser., vol. 1444, no. 1, p. 012034, Jan. 2020, doi: 10.1088/1742-6596/1444/1/012034.
[30] G. Guo, H. Wang, D. Bell, Y. Bi, and K. Greer, “KNN model-based approach in classification,” in On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE: OTM Confederated International Conferences, CoopIS, DOA, and ODBASE 2003, Catania, Sicily, Italy, November 3-7, 2003. Proceedings, Springer, 2003, pp. 986–996.
