Judul: Sensitivitas Ukuran Amatan Model Autoregresi Fuzzy
Abstrak: The conventional time series modelling method is Autoregressive Integrated Moving Average (ARIMA). ARIMA method is more effective if it is applied in long time periode of observations that at least 50 observations. Tseng on 2001, introduced Fuzzy ARIMA method to deal time series modelling on short time periode of observations. Fuzzy ARIMA combines the advantages of fuzzy logic, ARIMA and fuzzy regression. Considering Tseng’s study the aim of this study is tracing the optimal observation range size when fuzzy ARIMA method is applied at the curent condition. The result of this study indicated that fuzzy ARIMA when the condition of time series data AR(2) with μ=50 and parameter estimation 1=0.600 and 2=0.300 was more sensitive when the observation size less than 20.
Keyword: fuzzy regression, fuzzy logic, fuzzy ARIMA

Judul: Peramalan Nilai Tukar Rupiah terhadap Dolar Menggunakan Model ARIMA dan Metode Fuzzy Time Series
Abstrak: Peramalan merupakan sesuatu yang belum pasti dan sulit untuk diduga. Setiap negara memiliki mata uang sebagai alat tukar, pergerakan nilai tukarnya dapat mempengaruhi perekonomian suatu negara. Tujuan penelitian ini adalah meramalkan nilai tukar rupiah terhadap dolar menggunakan metode peramalan konvensional ARIMA dan metode peramalan fuzzy time series serta membandingkan kedua metode tersebut dengan melihat ukuran kesalahan peramalan menggunakan Mean Absolute Percentage Error (MAPE). Model ARIMA yang menggunakan metode Box Jenkins adalah metode konvesional yang menggunakan himpunan tegas dalam proses peramalannya sementara metode fuzzy time series adalah metode peramalan menggunakan himpunan fuzzy dalam prose peramalannya. Kedua metode ini menggunakan sejumlah data untuk meramalkan nilai tukar rupiah terhadap US dolar di masa yang akan datang. Nilai MAPE yang diperoleh untuk validasi peramalan menggunakan ARIMA adalah 2.47%, metode fuzzy time series 25 periode sebesar 1.42% dan metode fuzzy time series 60 periode sebesar 1.80%, sehingga dapat disimpulkan bahwa metode fuzzy time series menghasilkan peramalan yang lebih baik dibandingkan ARIMA.
Keyword: ARIMA, fuzzy time series, nilai tukar, peramalan.

Judul: Manajemen Pemeliharaan Kuda (Equus caballus) Untuk Upacara Kenegaraan Dan Sarana Kesenjataan Di Detasemen Kavaleri Berkuda (DenKavkud) TNI-AD Parongpong
Abstrak: Cavalry Horse Detachement (Denkavkud) Indonesian National Army-Cavalary Parongpong is one of the Indonesian military units that utilize the horse and train it as a means of purpose of formal state ceremony and armed forces. Skills possessed by the horse cavalry was the result of care management and specialized training provided by the trainer. Aspects of care management are breeding, feeding, and management. Horses that are kept in the Denkavkud, Parongpong are stallions, mares, foals, and military horses. Total populations of horses at Denkavkud are 183 horses. Besides commited horse training (remonte), Denkavkud also did breeding program to produce horses that can be trained and used as a military horse. There are some issues contained in Denkavkud, such as some of the nutrients needed by the horses not being met from feed given. One of these is crude protein and lysine. Lactation mares was crude protein and lysine deficiency. The number of death in 2010 is higher than previous years. Based on the number of horses that died from January until June 2010 is 11 horses. The average mortality was caused by colic. Colic is caused by excessive eating, excessive drinking during hot, moldy food, and even by investment roundworms. Animal welfare in Denkavkud can be enhanced by improvement of feed given and improving the health of livestock through improved sanitation.
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