Judul: Identifikasi motif berbasis citra menggunakan wavelet dengan klasifikasi probabilistic neural network (Studi kasus: Direktorat Jenderal Hak Kekayaan Intelektual)
Abstrak: Motif is one creation that should be respected and protected. The protection must be on target so nobody is aggrieved. The Directorate General of Intellectual Property Rights (DGIP) is a government institution in charge of implementing that protection. A process or step taken is examining the registered creation. The examination is still done manually, so DGIP spends a lot of time. This research aims to create a design and implementation of a system to identify if the motifs have been registered at DGIP with respectively digital image as the input. Detection process begins with the extraction process using the Haar wavelet to get the characteristics of the image and classification process by using the Probabilistic Neural Network (PNN). This study uses 450 training images divided into 50 classes. Testing uses 200 images consisting of 4 images for each class. The accuracies of the motif identification system are 65% and 64% for Haar wavelet level 2 and level 3, respectively.
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Judul: Signature Identification Using Probabilistic Neural Networks (PNN) With Wavelet Transformation Praprocessing.
Abstrak: Signature identification is a process for identifying a person who has the signature. Nowadays, there are many signature forgeries. A system that can recognize signature patterns is required to overcome these problems. Identification system which is implemented in this research uses Probabilistic Neural Networks (PNN). It works faster than other neural network approaches such as backpropagation because PNN approach only needs one iteration in training process. This research uses 200 images of signatures which consist of 20 signatures of each 10 respondents. This research is divided into two parts which are images without Wavelet decomposition and iamges with Wavelet decomposition. The results of this research are the system using Wavelet decomposition level 2 and level 3 in 4-fold cross validation has 98% accuracy. Different training data causes different test results. The greater the number of training data tends to increase the accuracy. Computation time is faster with the Wavelet decomposition and even faster with the addition of decomposition level. Keyword : Probabilistic Neural Networks (PNN), signature, Wavelet.
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Judul: Performa ayam broiler yang diberi zeolit dalam ransum dan litternya
Abstrak: Tingginya permintaan daging ayam menyebabkan meningkatnya perkembangan peternakan ayam broiler. Hal ini akan menimbulkan masalah seperti harga ransum yang mahal dan juga jumlah limbah peternakan juga meningkat. Zeolit yang bersifat higroskopis dapat membantu dalam penyerapan nutrien dan mengurangi gas NH3 dan H2S yang dapat mengganggu kesehatan ayam broiler sehingga performa ayam broiler dapat ditingkatkan. Penelitian ini bertujuan untuk mengetahui pengaruh pemberian zeolit (Aclinop) dalam ransum dan zeolit pada litter, serta kemungkinan interaksi kedua faktor ini terhadap performa ayam broiler. Pemberian Aclinop dalam ransum bertujuan untuk meningkatkan bobot badan, efisiensi penggunaan ransum dan laju pertumbuhan ayam broiler karena sifatnya yang mampu meningkatkan penyerapan nutrien ransum. Pemberian zeolit pada litter bertujuan untuk mengurangi kadar NH3 dan H2S yang dapat mempengaruhi kesehatan ayam broiler.
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