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: Prediksi Awal Musim Hujan Menggunakan Data Southern Oscillation Index dengan Support Vector Regression
Abstrak: Climate is one of the important aspects in human life. The information about prediction and forecasting is required, especially in agricultural sector. However, the current prediction for rainy season onset using linear regression method is not very good.The main objective of this research is to develop a model using support vector regression to predict the onset of the rainy season, in order to get the accuracy of climate information. Rainy season onset prediction has been attempted at 1 weather stations in Indramayu. The data used in this study is the Southern Oscillation Index (SOI) from June to August and the onset of the rainy season (AMH) from 1979-2008. The domain of the SOI was selected based on the correlation. Prediction result was evaluated using the root mean squared error and squared correlation coefficient. The average squared correlation coefficient value obtained was 0.7 and the root mean squared error was 2.3 using the RBF kernel function.
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Judul: Reduksi Mikroplastik pada Kerang Darah (Tegillarca granosa) dengan Difusi dan Elutriasi Larutan Garam NaCl
Abstrak: Partikel plastik terdiri atas berbagai ukuran, salah satunya partikel plastik berukuran kurang dari 1 mikrometer hingga 5 milimeter disebut mikroplastik. Mikroplastik tersebut dapat mengkontaminasi daging kerang darah (Tegillarca granosa) sehingga berbahaya untuk dikonsumsi. Tujuan penelitian ini adalah menentukan konsentrasi garam yang optimal untuk menurunkan jumlah mikroplastik dan pengaruhnya terhadap karakteristik daging serta bentuk dan jenis mikroplastik pada kerang darah. Perlakuan konsentrasi garam memengaruhi karakteristik warna kerang yang dihasilkan. Peningkatan konsentrasi garam yang digunakan berakibat pada meningkatnya pH, kadar garam, susut bobot, daya ikat air dan bobot residu pada daging kerang darah. Analisis visual menunjukkan keseluruhan mikroplastik yang ditemukan berbentuk fiber dan fragmen berwarna hitam dan bening/transparan. Aanalisis FTIR menunjukkan bahwa mikroplastik yang ditemukan adalah polyethylene terephthalate (PET). Penggunaan larutan garam NaCl konsentrasi 6.25%, 12.5% dan 25% menghasilkan kelimpahan mikroplastik lebih rendah dibandingkan tanpa garam NaCl, yaitu masing-masing 56.39%, 69.17% dan 73.68% lebih rendah dibandingkan perlakuan garam 0%.
Keyword: kerang darah, larutan garam, mikroplastik, polietilen tereftalat (PET)