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: Perbandingan metode Wavelet Daubechies dan MFCC sebagai ekstraksi ciri pada pengenalan fonem dengan Probabilistic Neural Network (PNN) sebagai Classifier
Abstrak: Nowadays, the development of telecommunication research is rapidly increasing. One of the research is in sound area. Sound is a way for human to interact with computers, known as word recogniser. Word recogniser is a part of voice recogniser that make the computers possible to receive input from word that pronounced. Word that pronounced contain phonemes that arranged into sentence. Voice recognition technology can recognise and understand words that pronounce by digitalising it, and tuning the digital signal with certain pattern that has been saved in a hardware. The result from this word identification will be displayed into printed word. This research will compare between Wavelet Daubechies and MFCCas identity extraction on word recognition with (PNN) as pattern identifier. PNN is a pattern identifier that has high accuracy. The comparison between trained data and tested data in research is 75% : 25%. Tested data that has been used was vary, such as: testing with increase the noise (pure noise) and data with noise increasing from 30dB, 20dB and 10dB. The result from this research is that the identity extraction by using MFCC is much better than with Wavelet Daubechies. From the pure original data (without noise increasing) the accuracy is 92.3% and in data with noise increasing 30dB, noise 20dB and noise 10dB the accuracy is 50.96%, 26.92% and 19.23%.
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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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