Judul: Prediksi Trafik Data pada Jaringan Telekomunikasi Menggunakan Metode Jaringan Saraf Tiruan Propagasi Balik
Abstrak: The development of communication technology has had a significant impact on the increase of communication service user. As a result, there is a high traffic on UMTS networks. In order to maintain the network’s Quality of Service (QoS), PT Indosat guarantees that its network utilization will not exceed 80%. This research tries to predict the future traffic, so that the company can analize the requirement of additional network capacity. The utilited data is the time series data of packet switching (PS) traffic on node B IPB, from January 2011 until April 2012. The used traffic data are original data, differentiated data, and normalized data. The normalized data are obtained by three functions, namely: logarithmic function, min-max normalization, and z-index normalization. Artifiacial neural network (ANN) back propagations used as the prediction method. The prediction error is calculated using mean absolute persentage error (MAPE). The result of this research is a three-month prediction of traffic data starting from April 2012. The prediction result with the smallest error of 5.14% is obtained from the data that are already transformed with logarithmic function. This small value of MAPE shows that the prediction result is quite accurate.
Keyword: time series, forecasting, backpropagation, artificial neural network

Judul: Peramalan Pasokan Tandan Buah Segar dan Penjualan Minyak Sawit Kasar Menggunakan Jaringan Syaraf Tiruan Propagasi Balik
Abstrak: Crude palm oil sales forecasting and palm oil fresh fruit bunch stock forecasting are common problems for palm agro industry company. The low rate of sales due to wrong prediction may influence company’s expansion plan. Moreover, crude palm oil stock is strongly dependent on palm oil fresh fruit bunch stock in each fields. The objective of this study is to create a model for sales forecasting of crude palm oil by using backpropagation Neural Network. This study uses secondary data which contains historical data of crude palm oil sales dependent on TBS stocks from 3 fields (inti, plasma, luar). The data is obtained from PT. Perkebunan Nusantara (PTPN) XIII term of January 2005 to December 2007. Factors that influence backpropagation neural network model are the number of neurons in the hidden layer and the learning rate. The conclusion obtained based on the result test is that the level of accuracy of the Backpropagation Neural Network in predicting the supply of Palm Fresh Fruit Bunch and Crude Palm Oil sales is more competitive than the accuracy of ARIMA time series data model for Palm Fresh Fruit Bunch and Crude Palm Oil sales with 750.45 for RMSE and 25.67 MAPE.
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Judul: Penanganan koksidiosis hati (Eimeria stiedae) pada kelinci
Abstrak: Koksidiosis merupakan penyakit parasiter yang menyerang hewan yang disebabkan oleh genus Eimeria. Pada kelinci terdapat dua bentuk koksidiosis, yakni koksidiosis intestinal dan koksidiosis hati. Eimeria stiedae adalah protozoa penyebab koksidiosis hati. Menyerang ganas pada kelinci muda (terutama umur 5-8 minggu) sedang hewan dewasa yang terserang cenderung menjadi karier dan mengekalkan penyakit. Hampir di seluruh dunia dilaporkan. Sekalipun kejadiannya jarang, namun menyebabkan kematian dengan mortalitas 50% sampai 100%. Kasus banyak bersifat subklinis. Pada kasus klinis gelaja yang tampak adalah: pembesaran abdomen, nafsu makan menurun, berat badan menurun, ikterus dan mencret. Penularan penyakit melalui makanan dan minuman atau bahan lain yang tercemar sporokista yang terbawa tinja penderita. Ookista ini berbentuk oval, berdinding licin dan double contour, berwarna merah kekuningan, mikrofil jelas, terang dan datar, ukuran (28-40 X 16-25) mikron. Siklus hidup mengalami tiga daur. Daur skizogoni, menghasilkan skizon dan merozoit. Daur gametogoni, menghasilkan gametosit, dan melalui fertilisasi terbentuk zigot yang berubah menjadi ookista dan keluar berturut- turut dari buluh empedu, usus dan tinja. ...
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