Judul: Desain dan implementasi komputasi paralel dengan algoritma genetik untuk prapemrosesan probabilistic neural network
Abstrak: Probabilistic Neural Network (disingkat PNN) merupakan salah satu jaringan saraf tiruan yang banyak dikembangkan bagi kepentingan manusia. PNN memiliki tingkat akurasi klasifikasi yang cukup tinggi dan waktu pelatihan yang cukup singkat. Salah satu faktor yang mempengaruhi akurasi klasifikasi PNN adalah parameter penghalus (o). Proses pencarian nilai o yang optimum merupakan salah satu prapemrosesan yang harus dilakukan sebelum masuk ke PNN. Nilai parameter o tidak dapat ditentukan secara langsung. Oleh sebab itu algoritma genetik dapat digunakan untuk mencari nilai o yang optimum. Pada umumnya solusi dari algoritma genetik semakin baik apabila jumlah populasi yang dibangkitkan cukup besar dan proses alamiah evolusi sering terjadi. Hal ini tentunya membutuhkan banyak iterasi dan sumberdaya komputasi yang cukup besar. Permasalahan ini dapat diatasi dengan menggunakan komputasi paralel yang menerapkan metode pelemparan pesan. Dengan mengkombinasikan metode algoritma genetik dan pemrosesan paralel untuk menduga parameter penghalus (σ), maka diharapkan akan diperoleh model PNN yang optimum. Penelitian ini bertujuan untuk menguji dan membandingkan kinerja dari algoritma genetik sekuensial, algoritma genetik paralel mode vektor baris, dan algoritma genetik mode vektor kolom. Penelitian menggunakan basis data bunga iris yang dikembangkan oleh R.A. Fisher. Berbagai kriteria yang digunakan untuk uji analisis adalah akurasi klasifikasi PNN, waktu eksekusi total, rasio kebergantungan mesin, peningkatan kecepatan, dan efisiensi. ...
Keyword: 

Judul: Optimasi jaringan syaraf tiruan menggunakan algoritme genetika
Abstrak: Neural network is one of common computational model to recognize pattern. Backpropagation algorithm can be used as method to train the networks weights. One of the drawbacks of backpropagation training is the initial weights generated randomly. It causes the instability of neural network training performance. Because of this, many experiment and training must be conducted to obtain the desired model. This research combine genetic algorithm and neural network to overcome random generation problem. Genetic Algorithm (GA) is used to find the optimal initial weights based on learning using Mean Square Error as objective function. The initial weights using GA is expected to be stable and better than those generated. The experimental research showed that genetic algorithm can be used to optimize the performance of backpropagation neural network. The optimal initial weights from genetic algorithm is more stable than the initial weights from regular method. The classification accuracy of genetic algortihm and neural network is also higher than regular method. This indicated GA method is promising to increase the neural network performance and can be applied in various real problem.
Keyword: neural network, genetic algorithm, optimation, backpropagation, classification

Judul: Elektrodeposisi Film Tipis Semikonduktor Cu2ZnSnS4 (CZTS) pada Substrat Kaca ITO
Abstrak: Semiconductor Cu2ZnSnS4 (CZTS) thin film that can be used as absorber layers on solar cell module was made via electrodeposition method. CZTS thin film was electrodeposited on the surface of a silicate glass coated with a thin layer of indium tin oxide (ITO). Deposition process was done using potentiostatic mode at room temperature with a potential of 1.05 V for 30, 45, and 60 minutes, followed with annealing the film at 2 different temperatures, 500 ºC using furnace and 180 ºC using hot plate, both treatments for 45 minutes in N2/H2S atmosphere. The X-ray difractograms were analyzed using Match 2 program and showed that the CZTS thin films had kesterit crystalline structure, with a low percentage of CZTS phase. The thin film also contained secondary phases such as Cu2S, ZnS, and SnS. The obtained CZTS thin films had a thickness between 1.039 and 1.676 μm. Absorption study showed that the band gap energy of CZTS thin films which was annealed using furnace ranged from 1.50 to 1.52 eV, whereas that annealed using the hot plate was not observed. The band gap energy value showed that CZTS thin films can be used as sunlight absorber layers on solar cell. The formation of CZTS crystal film on this investigation was effectively performed using furnace annealing process at a temperature of 500 ºC with deposition time of 45 minutes.
Keyword: Thin film, Electrodeposition, Cu2ZnSnS4