Judul: Heterogeneous Correlation Map between Regional and Local Climate Estimation Data on the Burned Areas in Indonesia
Abstrak: Indonesia merupakan negara dengan hutan hujan tropis terbesar ketiga di dunia, namun setengahnya sudah terdegradasi. Salah satu penyebabnya yaitu kebakaran hutan. Kebakaran hutan bisa disebabkan oleh dua faktor, yaitu faktor manusia dan alam. Faktor alam salah satunya menurunnya curah hujan. Fenomena iklim regional (ENSO dan IOD) dan lokal (selat Karimata, selat Makassar, dan laut Arafura) merupakan salah satu penyebab menurunnya curah hujan di Indonesia. Penelitian ini bertujuan melihat keterkaitan data estimasi iklim regional terhadap data luas area terbakar di Indonesia dan melihat keterkaitan data estimasi iklim lokal terhadap luas area terbakar di wilayah sekitarnya. Penelitian ini menggunakan metode Combined Empirical Orthogonal Function (CEOF) berbasis Singular Value Decomposition (SVD). Hasil penelitian menunjukkan bahwa ENSO lebih memiliki korelasi terhadap luas area terbakar di Merauke, Sulawesi dan Kalimantan. Sementara itu, IOD lebih memiliki korelasi terhadap luas area terbakar di Sumatra bagian Selatan. Luas area terbakar di Riau berkorelasi sangat rendah terhadap iklim regional, baik ENSO maupun IOD. Luas area terbakar di Sumatra selain dipengaruhi oleh ENSO dan IOD, ternyata juga dipengaruhi oleh iklim lokal di selat Karimata. Sama halnya dengan luas area terbakar di Kalimantan dipengaruhi oleh iklim lokal di selat Makassar dan luas area terbakar di Merauke dipengaruhi oleh iklim lokal di laut Arafura., Indonesia is a country with the third largest tropical rainforest in the world, but half of the forest has already been degraded, one of the causes is forest fires. Forest fires can be caused by two factors, namely human and natural factors. One of the natural factors is the lack of rainfall. Regional (ENSO and IOD) and local climate phenomena (Karimata straits, Makassar straits, and Arafura seas) are some of the causes of the lack of rainfall in Indonesia. This study aims to analyze the correlation of regional climate estimation data with data of burned areas in Indonesia and to analyze the correlation of local climate estimation data with data of burned areas in the surrounding area. This study uses the Combined Empirical Orthogonal Function (CEOF) method based on Singular Value Decomposition (SVD). The result shows that ENSO has a better correlation with the burned area in Merauke, Sulawesi and Kalimantan. Meanwhile, IOD has a better correlation with the burned area in southern Sumatra. The burned area in Riau has a very low correlation with the regional climate, both ENSO and IOD. The burned area in Sumatra is not only influenced by ENSO and IOD, but it is also influenced by the local climate in the Karimata strait. Similarly, the burned area in Kalimantan is influenced by the local climate in the Makassar Strait and the burned area in Merauke is influenced by the local climate in the Arafura Sea.
Keyword: ENSO, IOD, burned area, CEOF, SVD

Judul: Karakterisasi Indikator Global terhadap Pola Bersama Luas Area Terbakar dan Curah Hujan di Indonesia
Abstrak: Tujuan dari penelitian ini adalah menerapkan metode combined EOF berbasis Singular Value Decomposition (SVD) dengan menganalisis data luas area terbakar, indikator global (Indian Ocean Dipole, El Nino dan La Nina), dan curah hujan di Indonesia. Metode yang digunakan dalam penelitian ini adalah Empirical Orthogonal Function (EOF) berbasis Singular Value Decomposition (SVD) untuk mereduksi matriks data ke dalam program komputer menggunakan software. Metode ini bertujuan untuk menentukan pola–pola dominan pada data luas kebakaran dan curah hujan baik secara spasial maupun temporal. Hasil dari penelitian ini yaitu didapatkan sinyal dominan yang mempengaruhi kebakaran di setiap wilayah. Kata kunci: Curah hujan, Dekomposisi nilai singular, Empirical orthogonal function (EOF), ENSO dan IOD, Kebakaran hutan, Forest fires in Indonesia are influenced by several indicators, including the area burned, rainfall, and global indicators (ENSO and IOD). The purpose of this study is to apply the combined EOF method based on Singular Value Decomposition (SVD) by analyzing data on the burned area, global indicators (Indian Ocean Dipole, El Nino, and La Nina), and rainfall in Indonesia. The method used in this research is Empirical Orthogonal Function (EOF) based on Singular Value Decomposition (SVD) to reduce the data matrix into a computer program using software. This method aims to determine the dominant patterns in the data of fire area and rainfall both spatially and temporally. The results of this study shows the dominant signals which affect fires in each regions.
Keyword: Empirical orthogonal function (EOF), ENSO and IOD, Forest fires, Rainfall, Singular value decomposition

Judul: Synthesis of Apatite - Chitosan Composite Using Duck Eggshell and Chitosan with Temperature and pH Control
Abstrak: Composite of apatite-chitosan was prepared by using chitosan as polymer matrix. Calcium phosphate was prepared from CaO and (NH4)2HPO4, which the ratio Ca : P had to be 1.67. Chitosan is produced by deacetilase from biopolymer chitin. Duck eggshell (95% CaCO3) as starting material was converted to be calcium oxide (CaO) through calcinations with temperature 1000oC for 5 hours. In-situ and ex-situ methods were prepared from apatite and chitosan (dissolved with acetate acid 2%). Calcium phosphate prepared for bone implantation, but it is too fragile and brittle to be implant into the body. So addition of chitosan can decrease crystalinity and increase mechanic characteristic (tougher). Composite apatite-chitosan was resulted by fast precipitation. Fast precipitation was held in a breaker glass with nitrogen atmosphere, under controlled temperature at 37oC and pH 7, in order to increase amount of hydroxyapatite. Precipitation with CaO which was dropped by (NH4)2HPO4. Hydroxyapatite and chitosan were appearing at samples with in-situ and ex-situ methods which indicating by peak matching of XRD (closed lattice parameter, crystal size), overlapping at FTIR spectra and supporting by SEM picture (homogenous and agglomerated particle). However, it still needs further research to examine biocompatibility osteoconductive degree of sample by in-vivo and in-vitro test.
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