Judul: Pengidentifikasian Penyakit Erythemato-Squamous  Menggunakan Probabilistic Neural Network
Abstrak: Diagnosa untuk membedakan penyakit-penyakit erythemato-squamous merupakan masalah dalam ilmu tentang kesehatan kulit (dermatologi). Penyakit-penyakit yang termasuk ke dalam kelompok tersebut adalah psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, cronic dermatitis dan pityriasis rubra pilaris. Pada pengamatan awal, penyakit-penyakit tersebut memiliki ciri-ciri klinis erythema dan scaling dengan perbedaan-perbedaan yang sangat kecil (Merz dan Murphy dalam Kustiyo 2004). Tujuan dari penelitian ini adalah mengetahui ketepatan hasil pengklasifikasian penyakit erythemato-squamous menggunakan Probabilistic Neural Network (PNN) dengan pengelompokan data family history serta selang umur, sedangkan nilai smoothing parameter yang digunakan adalah 0.25, 0.5, 0.75 dan 1. Rata-rata keakuratan dega algoritma PNN untuk data family history 1 dan 0 masing-masing sebesar 87.68% dan 42.38%, sedangkan untuk pengelompokan umur, selang dewasa (18-40) menghasilkan ketepatan tertinggi, maka algoritma PNN seperti yang digunakan dalam penelitian ini dapat menjadi salah satu alternatif untuk indentifikasi penyakit erythemato-squamous pada data kelompok family history 1 dan selang dewasa.
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Judul: Identifikasi penyakit tanaman menggunakan fast fourier transform dan local binary pattern dengan probabilistic neural network (studi kasus tanaman Anthurium dan padi)
Abstrak: Plant diseases can result in death and decreased quality and quantity of agricultural products that are economically significant to cause losses for farmers. This research proposes a new system to identify plant disease automatically based on plant leaf image. Fast Fourier Transform (FFT) and Local Binary Patterns (LBP) are used to extract the features. There are nine features had been resulted by FFT i.e. mean, variance, different value of maximum and minimum levels, different value of maximum and mean levels, standard deviation, skewness, kurtosis, entropy, and the highest pixel value. Then these features are combined and classified using Probabilistic Neural Network. The result shows us that these features can be used to identify plants disease, best accuracy to indentify plants disease is 85.56%. The proposed system is promising since it is capable to identify disease plants species efficiently and accurately.
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Judul: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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