2015 · Article · Open Access
Early detection of normality pregnancy is one of the ways to prevent more serious disorders in pregnancy. This thesis study the implementation of Adaptive Neuro Fuzzy Inference System (ANFIS) to determine the normality of pregnancy. The period of pregnancy and complaints during pregnancy are used as inputs and the normality of pregnancy as output. Data were analyzed using ANFIS method and using Sugeno FIS rules. The program simulation results show that the performance of ANFIS can be implemented to determine the normality of pregnancy. The learning results on different training with the highest level of accuracy of 77,5% can recognize the symptoms and 97.5% could identify the diagnosis to determine the normality of pregnancy. The system can provide the necessary information about the normality of pregnancy. The results show that ANFIS can be used to determine the normality of pregnancy.
2015 · Article · Open Access
Early detection of normality pregnancy is one of the ways to prevent more serious disorders in pregnancy. This thesis study the implementation of Adaptive Neuro Fuzzy Inference System (ANFIS) to determine the normality of pregnancy. The period of pregnancy and complaints during pregnancy are used as inputs and the normality of pregnancy as output. Data were analyzed using ANFIS method and using Sugeno FIS rules. The program simulation results show that the performance of ANFIS can be implemented to determine the normality of pregnancy. The learning results on different training with the highest level of accuracy of 77,5% can recognize the symptoms and 97.5% could identify the diagnosis to determine the normality of pregnancy. The system can provide the necessary information about the normality of pregnancy. The results show that ANFIS can be used to determine the normality of pregnancy.
2014 · Article · Open Access
Processing characteristics of digital objects as the basis identification of the digital image, involves knowledge as the interpretation of visual information by using related methods to be implemented. Diseases in plants commonly shows symptoms by the presence of the spot or not, and discoloration. The similarity between the image colors can occur with one another, then the texture identifier could used in these study. The system constructed in this study as a solution for the identification of disease in citrus plants through image processing, involved methods and concepts. Stages of the process undertaken were preprocessing, feature extraction, and identification. Preprocessing method to resize, clipping, texture with usharp mask sharpening filter with kernel Gausian used and conversion of RGB to gray. Feature Extraction with Fast Fourier Transform (FFT) and Local Binary Pattern (LBP) uniform rotation invariant. FFT is a fast extraction of the Fourier transformation, while Karakteristik pengolahan objek digital merupakan dasar dari identifikasi citra digital, melibatkan knowledge sebagai interprestasi informasi secara visual dengan menggunakan metode-metode yang berkaitan agar dapat diimplementasikan. Penyakit pada tanaman umumnya menunjukkan gejala dengan adanya spot ataupun tidak, dan perubahan warna. Adanya kemiripan warna dapat terjadi antara citra satu dengan yang lain, maka penciri tekstur digunakan sebagai dasar dalam penelitian ini. Sistem yang dibangun dalam penelitian ini diharapkan sebagai solusi untuk identifikasi penyakit pada tanaman jeruk melalui pengolahan citra, dengan melibatkan metode dan konsep. Tahapan proses yang dilakukan adalah preprocessing, feature extraction, dan identification. Metode preprocessing dengan resize, clipping, penajaman tekstur dengan usharp mask filter dengan kernel Gausian dan konversi RGB ke gray. Feature Extraction dengan metode Fast Fourier Transform (FFT) dan Local Binary Pattern (LBP) uniform rotation invarian. FFT merupakan ekstraksi cepat pada transformasi fourier, sedangkan LBPP,Rriu2 merupakan ekstraksi ciri dengan diskripsi pola pada citra gray. Proses identifikasi dengan metode Probabilistic Neural Network (PNN) digunakan untuk menentukan hasil akurasi. Hasil pengujian dengan PNN dengan jumlah data 233 terbagi menjadi 157 sebagai data latih, dan 76 sebagai data uji. Hasil akurasi tertinggi terhadap batang, daun, dan buah latih menunjukan nilai rata-rata 100%. Akurasi pada batang uji diperoleh 52,94% dengan ekstraksi ciri FFT, dan akurasi daun uji 34,29% pada ketiga ekstraksi ciri yaitu FFT, LBPP,Rriu2, serta gabungan keduanya. Akurasi pada buah uji 58,33% dengan ekstraksi ciri FFT.
