| dc.contributor.advisor | JUSMAN, YESSI | |
| dc.contributor.advisor | ARDIYANTO, YUDHI | |
| dc.contributor.author | PIKRIANSAH, PIKRIANSAH | |
| dc.date.accessioned | 2020-05-16T02:26:26Z | |
| dc.date.available | 2020-05-16T02:26:26Z | |
| dc.date.issued | 2020-04-20 | |
| dc.identifier.uri | http://repository.umy.ac.id/handle/123456789/33575 | |
| dc.description | Malaria merupakan penyakit infeksi yang disebabkan oleh parasit plasmodium yang ditularkan oleh nyamuk anopheles betina. Penyakit malaria memiliki 4 jenis parasite yaitu plasmodium falciparum, plasmodium vivax, plasmodium malariae dan plasmodium ovale. Metode standard diagnosis malaria yaitu dengan mikroskopis atau uji laboratorium dan Rapid Diagnostic Test (RDT). Uji laboratorium memiliki resiko tinggi human error sedangkan RDT memiliki kelemahan terhadap sensitivitas suhu, variasi genetik dan ketahanan antigen dalam aliran darah. Penelitian ini menawarkan suatu sistem klasifikasi penyakit malaria dengan metode hu moment dan support vector machine (SVM) dengan 3 jenis objek parasit malaria yaitu falciparum, malariae dan vivax. Sistem klasifikasi menggunakan 3 model SVM yaitu linier SVM, polynomial SVM dan gaussian SVM dengan kelas falciparum sebagai data positif serta malariae dan vivax sebagai data negatif. Hasil klasifikasi terbaik terdapat pada model gaussian SVM dengan sensitivitas 96.67% dan spesifisitas 90%. Rata-rata akurasi model gaussian SVM dengan metode 5-fold cross validation 90 sampel citra yang terbagi kedalam 3 kelas didapatkan hasil 86.66%. | en_US |
| dc.description.abstract | Malaria is an infectious disease caused by a plasmodium parasite transmitted by the female Anopheles mosquito. Malaria disease has four types of parasites, namely Plasmodium falciparum, Plasmodium vivax, Plasmodium malariae and Plasmodium ovale. The standard method of malaria diagnosis is by conducting microscopic examination or laboratory test and Rapid Diagnostic Test (RDT). Laboratory tests have a high risk of human error whereas RDT has weaknesses in temperature sensitivity, genetic variation and antigen resistance in the bloodstream. This research offers a classification system of malaria disease by applying the Hu moment and Support vector Machine (SVM) method with 3 types of malaria parasitic objects, namely falciparum, Malariae and vivax. The classification system uses 3 SVM models, namely linear SVM, polynomial SVM and Gaussian SVM with the Falciparum class as a positive data and malariae and vivax as negative data. The best classification outcome is on the Gaussian SVM model with 96.67% sensitivity and 90% specificity. The mean accuracy of the Gaussian SVM model with a 5-fold cross Validation 90 image sample which is divided into 3 classes is 86.66%. | en_US |
| dc.publisher | FAKULTAS TEKNIK UNIVERSITAS MUHAMMADIYAH YOGYAKARTA | en_US |
| dc.subject | Malaria, hu moment, linear SVM, polynomial SVM, gaussian SVM. | en_US |
| dc.title | SISTEM KLASIFIKASI PENYAKIT MALARIA DENGAN METODE HU MOMENT DAN SUPPORT VECTOR MACHINE | en_US |
| dc.type | Thesis SKR FT 586 | en_US |
