Judul: Pengembangan Metodologi Downscaling Menggunakan Jaringan Saraf Tiruan (Studi Kasus Curah Hujan Indramayu)
Abstrak: Peristiwa iklim ekstrim terkait El Niño-Southern Oscillation (ENSO) telah menimbulkan dampak yang signifikan pada sektor pertanian di Indonesia. Pada peristiwa ENSO hangat atau biasa dikenal dengan istilah El Niño, biasanya terjadi penundaan awal musim hujan dan bertambah panjangnya musim kemarau. Peristiwa El Niño seringkali dikaitkan dengan meluasnya kekeringan di berbagai daerah yang menyebabkan turunnya produksi pertanian karena kurangnya persediaan air. Sebaliknya, pada periode ENSO dingin, atau dikenal dengan istilah La Niña, kejadian hujan meningkat melebihi kondisi rata-rata sehingga meningkatkan peluang terjadinya banjir di berbagai daerah di Indonesia. Implikasinya pada sektor pertanian ialah semakin meningkatnya wilayah persawahan yang tergenang banjir dan merusak tanaman, sehingga menyebabkan meningkatnya kemungkinan gagal panen.Perubahan iklim regional/lokal ini bisa bersifat spesifik antar daerah sehingga membutuhkan data dengan resolusi cukup tinggi yang tidak bisa didapat dari data luaran general circulation model (GCM) yang memiliki resolusi horizontal yang sangat rendah. Oleh karena itu untuk mendapatkan informasi dengan resolusi yang cukup tinggi, diperlukan pemanfaatan teknik downscaling. Metode downscaling dengan pendekatan dinamik dapat dilakukan dengan menggunakan simulasi model iklim regional (regional climate model, RCM) yang disimulasi berdasarkan informasi data GCM. ..dst
Keyword: penurunan skala statistik, model sirkulasi umum, jaringan syaraf tiruan, backpropagation, K-fold Cross Validation, jarak euclid

Judul: Downscaling Methodology Development Using Arftificial Neyral Network (Case Study of Indramayu’s Rainfall).
Abstrak: The unexpected climate change results a long period wet and dry so it makes many agricultural areas damage. This is cause the farmers failed to harvest so the production of food will be decreased. To solve this problem, we need a model that can estimate the climate and rainfall so it can determine the suitable cropping pattern with the climate. Statistical downscaling modeling commonly used to model the changes of the weather. This study develops a statistical downscaling model that use artificial neural network with some of GCM data as an input in training. In this study, the GCM data is input for training. Before training this data is grouped into 5 groups. The results obtained indicate that some of the GCM can approach the average of monthly rainfall. However, in some extreme point, the result have not managed to reach and close to results of observation. The best results obtained with the correlation Bondan station at 0.4976 and the worst results obtained Krangkeng station with a correlation of 0.3704. In this study also can be seen that the estimators can be influenced by the location of the observation. The location is adjacent to the sea tend to have the results of a relatively lower correlation compared with the location that far from the sea.
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Judul: In vitro analysis of biphasic calcium phosphate and hydroxyapatite as bone implants
Abstrak: The minimum requirement of synthetic biomaterial is must be biocompatible. Biocompatibility means that material is nontoxic and able to interact with the host cells. The analysis of 3-(4,5-dimethylthiazol-2-yl)-2,5- diphenyltetrazolium bromide (MTT) assay was used to detect toxicity. Interaction between cells and bone implants were characterized by Scanning Electron Microscopy (SEM). Previously, hydroxyapatite is well known as biocompatible implantable materials. But, the dense hydroxyapatite is almost non-resorbable when used as bone implant. In the other hand, the porous β-tricalcium phosphate (TCP) displays affinity for high speed biological degradation. TCP was later identified as Biphasic Calcium Phosphate (BCP) consisting of hydroxyapatite (HA) and tricalcium phosphate (β-TCP).
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