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 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: 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)