Judul: Prediksi Awal Musim Hujan di Provinsi Sulawesi Selatan Menggunakan Data Anomali Suhu Muka Laut
Abstrak: The Province of South Sulawesi is one of the centers for national paddy production with 7% contribution from total paddy production in national level. The increasing of paddy production depends on the water availability. Moreover, the water availability is always associated with the onset of rainy seasons, because if an area has the forward or backward of onset of rainy season, it will influence the water availability and crop calendar. In order to onset of rainy seasons, to reduce the farmer’s loss and to protect the food stability level in Indonesia, it needs to identified the areas which have the sensitivity for the onset of rainy season’s variability to climate control factors like ENSO and IOD. This research has been conducted to study the correlation between onset of rainy seasons (AMH) in South Sulawesi and sea surface temperature anomaly (AnoSML) in Pacific Ocean and Sulawesi Sea, to determine the domain of sea surface temperature predictor which has influence the onset of rainy seasons in South Sulawesi and to develop the onset of rainy season model prediction in South Sulawesi based on the sea surface temperature anomaly in Pacific Ocean and Sulawesi Sea. This research has been conducted using daily rainfall data during 30 years (1980-2010) from rainfall stations in South Sulawesi, sea surface temperature anomaly data for 4 domains i.e. Nino 3 region, Nino 3.4, Nino 4 and Sulawesi Sea on June, July, August and September (JJAS) during 30 years. The first analysis of data was conducted with the examination of observation daily rainfall data and selection of rainfall stations which are used for the development of the rainy season onset prediction model. Further analysis was conducted to determine the onset of rainy seasons using Moron’s method (five days of daily rainfall data/pentad analysis). The result of pentad analysis was used to calculate Principle Component Analysis (PCA) which was used to determine the cluster of rainfall stations. The correlation analysis was then calculated between the onset rainy seasons on each cluster and AnoSML data on JJAS. The last analysis was conducted for development of onset rainfall season model prediction and verification of the model. Based on the checking of observation daily rainfall data with the data availability >70%, there were 45 chosen stations for onset of the rainy season analysis. The result of AMH analysis showed that the average of AMH for whole stations was on the 348th Julian Date (JD) or on 14th December. The highest AMH was obtained at Stasiun BPP. Panincong (PNCG) in Soppeng District with 473th Julian Date or on 18th April. Otherwise, the lowest AMH was obtained at Stasiun BPP. Keera (KERA) in Wajo District with 278th Julian date or on 5th October. The result of PCA analysis showed that there were six PCs which were explained variability of data ± 80%. Based on the PC clustering, there were 3 clusters of rainfall stations. Cluster 1 has 13 rainfall stations which are spread in 9 districts i.e. Bantaeng District, Bulukumba, Enrekang, Gowa, Luwu, Maros, Pinrang, Soppeng and Wajo District. On Cluster 2, there are 10 rainfall stations
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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: Sintesis dan Pencirian Kompleks Fe(III)-Kuersetin
Abstrak: Quercetin belongs to flavonols which is able to chelate metal ions. Quercetin may chelate metal ions at 3’,4’-catechol, 3- or 5-hydroxycromone sites. In this study, quercetin was complexed with Fe(III). The complex with Fe(III) ions from FeCl3·6H2O was more stable than the complex derived from Fe(NO3)3·9H2O. The quercetin was yellow in color and showed maximum wavelengths (λ􀭫􀭟􀭶) at 250 and 370 nm. The Fe(III)-quercetin complex from Fe(NO3)3·9H2O was yellowish green with 􀟣􀭫􀭟􀭶 at 280 and 420 nm, whereas that from FeCl3·6H2O was dark green with 􀟣􀭫􀭟􀭶 at 280 and 430 nm. The 􀟣􀭫􀭟􀭶 shift indicated that the complex was formed at 3-hydroxychromone site. The Fe(III)- quercetin complex was formed with 1:1 metal-ligand mole ratio based on atomic absorption spectrophotometry analysis and the X-ray diffractogram showed that the crystallinity of the complex was 37%.
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