Klasifikasi Data Malaria Menggunakan Metode Support Vector Machine
DOI:
https://doi.org/10.30865/mib.v5i4.3347Keywords:
Malaria, Classification, Support Vector Machine, Min-Max, K Cross ValidationAbstract
Malaria is a life-threatening disease, caused by a parasite that is transmitted to humans through the bite of an infected female Anopheles mosquito. In 2019, there were an estimated 229 million cases of malaria worldwide and the death toll reached 409,000. The area most frequently affected by malaria, according to WHO, is the African region. Malaria can be detected beforehand by using the information inpatient data and applying machine learning techniques. This study aims to detect and classify severe malaria based on the history of examining patient data using the Support Vector Machine (SVM) method with a normalization technique using min-max on the dataset and a cross-validation technique with several experiments on the K value of the results. This study also compares the Support Vector Machine method with Naïve Bayes (NB) where the accuracy of the SVM model is superior to Nave Bayes with an average accuracy gap of 25%. The accuracy generated by the application of the proposed method is 92.3%.
References
WHO, 2021. “Key Fact Malariaâ€. Tersedia [https://www.who.int/news-room/fact-sheets/detail/malaria] diakses 8 Agustus 2021.
Irmanita, Rachmadania, Sri Suryani Prasetiyowati, and Yuliant Sibaroni. "Classification of Malaria Complication Using CART (Classification and Regression Tree) and Naïve Bayes." Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi) 5.1 (2021): 10-16.
Parveen, Rahila, et al. "Prediction of malaria using artificial neural network." Int J Comput Sci Netw Secur 17.12 (2017): 79-86.
Lee, You Won, Jae Woo Choi, and Eun-Hee Shin. "Machine learning model for predicting malaria using clinical information." Computers in Biology and Medicine 129 (2021): 104151.
P.J. Cock, T. Antao, J.T. Chang, B.A. Chapman, C.J. Cox, A. Dalke, I. Friedberg, T. Hamelryck, F. Kauff, B. Wilczynski, M.J. de Hoon, Biopython: freely available Python tools for computational molecular biology and bioinformatics, Bioinformatics, 25 (2009) 1422-1423
C.f.D.C.a. Prevention, DPDx - Laboratory Identification of Parasites of Public Health Concern 2020.
Nkiruka, Odu, Rajesh Prasad, and Onime Clement. "Prediction of malaria incidence using climate variability and machine learning." Informatics in Medicine Unlocked 22 (2021): 100508.
Cleary, Eimear, et al. "Spatial prediction of malaria prevalence in Papua New Guinea: a comparison of Bayesian decision network and multivariate regression modelling approaches for improved accuracy in prevalence prediction." Malaria Journal 20.1 (2021): 1-16.
Mohapatra, Pallavi, et al. "Determining suitable machine learning classifier technique for prediction of malaria incidents attributed to climate of Odisha." International Journal of Environmental Health Research (2021): 1-17.
Awotunde, Joseph Bamidele, et al. "Prediction of malaria fever using long-short-term memory and big data." International Conference on Information and Communication Technology and Applications. Springer, Cham, 2020.
Santosh, Thakur, Dharavath Ramesh, and Damodar Reddy. "LSTM based prediction of malaria abundances using big data." Computers in Biology and Medicine 124 (2020): 103859.
Adeboye, Nureni Olawale, Olawale Victor Abimbola, and Sakinat Oluwabukola Folorunso. "Malaria patients in Nigeria: Data exploration approach." Data in brief 28 (2020): 104997.
Shalabi, L.A., Z. Shaaban and B. Kasasbeh, Data Mining: A Preprocessing Engine, J. Comput. Sci., 2: 735-739, 2006
Larose, Daniel T., and Chantal D. Larose. Discovering knowledge in data: an introduction to data mining. Vol. 4. John Wiley & Sons, 2014.
Wijayanti, Ratna Ayu, Muh Tanzil Furqon, and Sigit Adinugroho. "Penerapan Algoritme Support Vector Machine Terhadap Klasifikasi Tingkat Risiko Pasien Gagal Ginjal." Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN 2548 (2018): 964X.
Wong, Tzu-Tsung. "Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation." Pattern Recognition 48.9 (2015): 2839-2846.
Wu, Qiang, and Ding-Xuan Zhou. "Analysis of support vector machine classification." Journal of Computational Analysis & Applications 8.2 (2006).
Sari, Esa Anindika, et al. "Klasifikasi Kabupaten Tertinggal di Kawasan Timur Indonesia dengan Support Vector Machine." JIKO (Jurnal Informatika dan Komputer) 3.3 (2020): 188-195.
Feta, Neneng Rachmalia, and Asep Rahmat Ginanjar. "Komparasi Fungsi Kernel Metode Support Vector Machine Untuk Pemodelan Klasifikasi Terhadap Penyakit Tanaman Kedelai." BRITech, Jurnal Ilmiah Ilmu Komputer, Sains dan Teknologi Terapan 1.1 (2019): 33-39.
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