Judul: Prediksi Awal Musim Hujan Menggunakan Adaptive Neuro-Fuzzy Inference System pada Studi Kasus Kabupaten Indramayu
Abstrak: Climate anomalies cause the onset of the rainy season changes in Indonesia. In agriculture, the change complicates precise determination of cultivation. Incorrect determination increases crop failure due to droughtness. As one of the national granaries, rice production in Indramayu highly depends on the climate. This research aims to predict the onset of the rainy season in Indramayu using adaptive neuro-fuzzy inference system (ANFIS). The data used in this study are southern oscillation index (SOI) from Mei to August as predictor and the onset of the rainy season from 1971 to 2010. SOI data was reduced to 7 intervals. There are 5 rain regions in Indaramayu, and the average rain region was predicted using ANFIS model for each region. The prediction result was evaluated using the root mean squared error and the squared correlation coefficient. The best prediction result was obtained at interval [-7,7] with an average of the root mean squared error of 19.5 days and a squared coefficient correlation of 0.33.
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Judul: Peramalan Panjang Musim Hujan Menggunakan Adaptive Fuzzy Inference System
Abstrak: Climate is a component of the ecosystem and an important natural factor which is dynamic and difficult to be controlled. The dynamic and diverse characteristics of climate need more accurate analysis to make climatic information be more useful in the agriculture sector. Uncertain climatic condition could be a limiting factor for crops productivity. Technically, in crops cultivation all climatic elements almost affect crops productivity and management. One of the solutions for this problem is by predicting the length of the rainy season to minimize the level of crops failure in agricultural product by using adaptive fuzzy inference system (ANFIS) method. This research used the Southern Oscillation Index (SOI) data and the onset data with the length of the rainy season of the previous year as the predictors to predict length of the rainy season of the current year. This research is focused at the Indramayu district. As the predictive model, we use adaptive neuro fuzzy system. Five models were trained for each of the five regions used, as well as one model for the average data of the five regions. The best prediction was obtained from the model on the fifth region. On this region, the correlation coefficient between the observed values and the predicted values is 0.69 and the RMSE value is 4.09 (measured in tens of days). From this result, we conclude that the performances of the models were still not satisfactory.
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Judul: Sifat optik dan sifat listrik diode schottky film Barium Titanat (BaTiO3) berstruktur emas - BaTiO3 (Metal – ferroelektrik)
Abstrak: Diode Schottky (emas - BaTiO3) berhasil dibuat dengan ditumbuhkan di atas permukaan interdigital electrode berbahan emas(Au) menggunakan metode Chemical Solution Deposition (CSD) . Film BaTiO3 dibuat dengan proses annealing pada suhu 400oC dengan variasi holding time annealing 4 jam dan 10 jam. Karakterisasi sifat optik diode Schottky dilakukan menggunakan UV-Vis Spektrofotometer untuk memperoleh nilai energi gap Film BaTiO3. Nilai energi gap BaTiO3 pada dua perlakuan holding time annealing 4 jam dan 10 jam sebesar 3.27 eV dan 3.21 eV dan nilai energi gap tersebut termasuk dalam kategori semikonduktor. Karakterisasi sifat listrik diode Schottky dilakukan menggunakan LCR Meter yang didapatkan nilai konduktivitas listrik pada Holding time annealing 10 jam lebih besar daripada 4 jam. Kemudian, didapatkan nilai impedansi yang menunjukkan bahwa pada holding time annealing 4 jam lebih besar daripada holding time 10 jam. Didapatkan pula nilai konstanta dielektrik yang menunjukkan pada waktu holding time annealing 4 jam lebih besar daripada 10 jam.
Keyword: Barium titanat, diode schotty, ferroelektrik, sifat optik, sifat listrik, emas, metal