IDENTIFIKASI JENIS JERAWAT DENGAN WAVELET HAAR DAN JARINGAN SYARAF TIRUAN PROPAGASI BALIK - Nava Muzdalifah; Kusworo Adi (Youngster Physics Journal, 2016)
Acne is one of the most common diseases that often occur in adolescence. Acne is caused by several factors, namely genetic, hormonal, stress, Propionibacterium acnes microorganisms, environmental, and cosmetics. The existence of these factors can cause many types of acne, such as blackheads, whiteheads, papules, pustules, and nodules. Various efforts that have been done to resolve the problem of identifying the types of acne still has some flaws that required identification system type of acne that is cheap, effective, efficient, and accurate. Innovation identification of the type of acne is designed with the help of digital microscope camera, and a computer model in which applications are developed is based on wavelet transform and neural networks to identify types of acne automatically. Results of a system can identify the type of acne automatically using wavelet transform with the decomposition at level 3 and the coefficients is horizontal and backpropagation neural network with a network architecture that consists of various layers are input layer, hidden layer and output layer. Accuracy of identification the system is 84,6% with instructional time by 8 seconds. Identified the type of acnes are blackheads, cysts, nodules, papules, pustules and whiteheads. The range accuracy for acnes identification on the network is 72% until 92%. The best pixel resolution is 8 MP.