Judul: Prediksi Awal Musim di Kabupaten Kapuas Menggunakan Data Curah Hujan Musiman Hasil Prediksi Model Iklim Global
Abstrak: Predictions of the rainy and dry season onsets are very important in climate risk management processes, especially for the development of early warning system of land and forest fires in Kalimantan. This research aimed to predict the rainy and dry season onsets in Kapuas District, Central Kalimantan. The prediction models used to predict the onsets were developed by using seasonal rainfall data on September-October-November (SON) periods as predicted by five Global Climate Models (GCMs). The models developed in this study relied on Canonical Correlation Analysis (CCA) prediction methods available in the Climate Predictability Tool (CPT) software developed by the International Research Institute for Climate and Society (IRI), Columbia University. This research showed that HMC and POAMA models produce better canonical correlations (r = 0.72 and 0.89, respectively) in creating the models for predicting the wet onsets compared to BCC (r=0.46), CWB (r=0.62), and GDAPS_F (r=0.67) models. In the development of models for predicting the dry season onsets, CWB and POAMA models indicated better canonical correlation results (r = 0.73 and 0.76, respectively) compared to BCC (r=0.53), GDAPS_F (r=0.64), and HMC (r=0.46) models. In general, the model validations showed that CWB, GDAPS_F, and POAMA models have better predictive skills than BCC and HMC models in predicting onsets of the rainy and dry seasons (with Pearson correlations (r) ranging between 0.30 and 0.75). Experiments on those five models for the predictions of wet season onset in 2013 showed that the predicted onsets occurred on the range of 8 September to 22 October in Cluster 1 and on 3 to 7 October in Cluster 2. For the predictions of the dry season onsets in 2014, the models predicted the occurrences from 6 to 25 May in Cluster 1 and from 21 to 25 March in Cluster 2.
Keyword: onset prediction, GCM, CPT, CCA

Judul: Model Prediksi Awal Musim Kemarau dan Awal Musim Hujan (Studi Kasus: Kabupaten Malang).
Abstrak: Informasi prediksi awal musim dapat menjadi dasar dalam pengurangan tingkat risiko iklim dan perencanaan kegiatan pada sektor pertanian. Informasi tersebut diperlukan di wilayah Kabupaten Malang karena wilayah ini memiliki potensi rawan bencana dan memiliki lahan pertanian yang cukup luas. Penelitian ini bertujuan membangun model prediksi awal musim di Kabupaten Malang. Penentuan awal musim di Kabupaten Malang menggunakan metode akumulasi anomali curah hujan harian. Awal Musim Kemarau (AMK) rata-rata terjadi pada 1 Mei (Julian Day ke-122), sedangkan Awal Musim Hujan (AMH) rata-rata terjadi pada 3 November (Julian Day ke-308). Model prediksi AMK dan AMH menggunakan prediktor tekanan permukaan laut rata-rata, Outgoing Longwave Radiation (OLR), serta angin zonal 200 hPa dan 925 hPa dengan menggunakan metode Principle Component Regression (PCR). Model prediksi AMK paling baik menggunakan prediktor pada bulan Februari yang memiliki koefisien determinasi (R2) sebesar 80.1%, RMSE sebesar 10.71 hari, dan koefisien korelasi mencapai 0.89. Model prediksi AMH paling baik menggunakan prediktor pada bulan Agustus yang memiliki koefisien determinasi (R2) sebesar 90.8%, RMSE sebesar 5.53 hari, dan koefisien korelasi mencapai 0.95.
Keyword: awal musim hujan, awal musim kemarau, model prediksi, PCR

Judul: Karakteritsik kimia dan organoleptik tablet effervescent putih telur bersitarasa lemon dengan konsentrasi effervescent mix yang berbeda
Abstrak: Egg is an animal origin food product which has high nutritional value especially protein. Cholesterol content of eggs is only found in egg yolk, on the other hand albumen does not countain cholesterol at all but high content of protein, so it can be used as a free cholesterol-protein sources. The application of albumen as egg instant drink effervescent tablet can be an alternative of protein source for people who need free-cholesterol diet. It can also become an egg product diversification.
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