Skin disease is one of disease that is often found in tropical countries, such as Indonesia. People who suffered from skin disease in Indonesia were still relatively high, the prevalence could range between 20%-80%. Therefore, the help of computer technology was expected to detect the disease earlier that attacked the skin in the human's body and it could reduce the possibility of the occurrence for other dangerous diseases. This study proposed the making of an application of identification image for skin disease by using one of the machines learning method, called Support Vector Machine (SVM) which was done by processing the image and machine learning processes that could perform early detection of skin diseases. This study aimed to determine the classification of skin diseases in humans into four classes, such as the class Benign Keratosis, Melanoma, Nevus, and Vascular. The segmentation method used was K-Means Clustering, while the feature extraction method that used was feature extraction of the Discrete Wavelet Transform (DWT) and Color Moments. Based on the results of the test that had conducted, the sensitivity was 95%, the specificity was 97.9% and the accuracy was 97.1% by using SVM parameters, that was kernel Radial Basis Function (RBF), Box Constraint = 1.5, RBF_Sigma (σ) = 1, and iterations = 1000.

Kanker serviks merupakan salah satu penyakit berbahaya yang biasanya menyerang pada wanita. Kanker serviks dapat dicegah dengan melakukan deteksi dini, yaitu melalui tes pap smear untuk mengenali sel nukleus abnormal. Penyakit serviks secara teratur terbentuk dari perubahan prakanker lebih dari 10 hingga 20 tahun. Penelitian ini mengusulkan pembuatan aplikasi klasifikasi sel nukleus pap smear untuk mempermudah deteksi dini kanker serviks dengan menggabungkan teknik machine learning dan pengolahan citra digital. Aplikasi berfungsi mempermudah para patologi untuk mendeteksi sel nukleus pap smear normal dan abnormal. Tahap yang dilalui untuk memperoleh hasil klasifikasi, yaitu preprocessing, segmentasi, ekstraksi ciri dan klasifikasi. Dua jenis kelas diklasifikasikan pada penelitian ini, yaitu Sel Abnormal dan Sel Normal. Akurasi yang dihasilkan dari proses uji coba, yaitu sebesar 88.8% dan error rate sebesar 11.2%.Kata Kunci : Neural Network, K-Means Clustering, Regionprops, GLCM, Pap Smear

Skin disease is one of disease that is often found in tropical countries, such as Indonesia. People who suffered from skin disease in Indonesia were still relatively high, the prevalence could range between 20%-80%. Therefore, the help of computer technology was expected to detect the disease earlier that attacked the skin in the human's body and it could reduce the possibility of the occurrence for other dangerous diseases. This study proposed the making of an application of identification image for skin disease by using one of the machines learning method, called Support Vector Machine (SVM) which was done by processing the image and machine learning processes that could perform early detection of skin diseases. This study aimed to determine the classification of skin diseases in humans into four classes, such as the class Benign Keratosis, Melanoma, Nevus, and Vascular. The segmentation method used was K-Means Clustering, while the feature extraction method that used was feature extraction of the Discrete Wavelet Transform (DWT) and Color Moments. Based on the results of the test that had conducted, the sensitivity was 95%, the specificity was 97.9% and the accuracy was 97.1% by using SVM parameters, that was kernel Radial Basis Function (RBF), Box Constraint = 1.5, RBF_Sigma (σ) = 1, and iterations = 1000.