Judul: Land cover classification of rural areas using Sentinel-1 and Support Vector Machines
Abstrak: Pemetaan klasifikasi tutupan lahan di wilayah rural atau pedesaan saat ini memiliki nilai penting dalam pembangunan berkelanjutan dan pembuatan kebijakan untuk optimalisasi sumber daya alam. Penggunaan Citra radar Sentinel 1 beroperasi optimal dalam semua kondisi cuaca dan mengurangi dampak dari iklim tropis Indonesia yang sering tertutup awan saat perekaman. Penelitian ini bertujuan untuk menelaah karakteristik data hamburan balik dan fitur dekomposisi polarimetrik pada berbagai tutupan lahan rural dan melakukan pemodelan dengan metode Support Vector Machines (SVM). Penelitian dilakukan pada wilayah perbatasan Kabupaten Pandeglang dan Lebak. Pemodelan dilakukan dengan mencoba berbagai kernel dan penyetelan parameter Cost untuk mendapatkan hasil yang optimal. Kinerja terbaik diperoleh dengan penggunaan kernel Radial Basis Function (RBF) dengan akurasi total sebesar 77,74 %. Penyetelan parameter dengan penyesuaian nilai Cost tidak memberikan perubahan yang signifikan pada setiap kernel yang digunakan akibat kompleksitas tutupan lahan yang tinggi. Penggunaan polarimetri parsial dan dekomposisi Eigenvalue belum sepenuhnya efektif dalam mengklasifikasikan tutupan lahan di wilayah yang kompleks., Land cover classification mapping in rural areas is currently of great importance in sustainable development and policy making for the optimization of natural resources. Sentinel-1 radar imagery operates optimally in all weather conditions and reduces the impact of Indonesia's tropical climate which is often covered by clouds during data acquisition. This study aimed to examine characteristics of backscatter data and polarimetric decomposition features over various rural land covers and to model them using Support Vector Machines (SVM) method. The research was conducted at the border area of Pandeglang and Lebak regencies. Modeling was done by trying various kernels and tuning Cost parameters to get optimal results. The best performance was obtained by using the Radial Basis Function (RBF) kernel with a total accuracy of 77.74%. Parameter tuning by adjusting the Cost value did not provide significant changes in each kernel used due to the high complexity of land cover. The use of partial polarimetry and Eigenvalue decomposition were not fully effective in classifying land cover in complex areas.
Keyword: Land Cover, Rural, Sentinel-1, Support Vector Machines

Judul: Klasifikasi Tutupan Lahan Menggunakan Pendekatan Pemelajaran Mesin pada Data Sentinel-1
Abstrak: Sektor pertanian, khususnya subsektor perkebunan, berkontribusi besar sebagai penghasil devisa negara. Komoditas perkebunan yang menjadi andalan bagi pendapatan nasional dan devisa negara adalah karet dan kelapa sawit. Perencanaan, pengembangan, serta pemantauan lahan perkebunan dan pertanian seringkali memanfaatkan perkembangan teknologi penginderaan jauh untuk mempermudah proses pengambilan keputusan. Pemetaan menggunakan citra optik sering terkendala kondisi iklim Indonesia dengan cuaca yang sering tertutup awan dan kabut yang dapat menghalangi objek saat perekaman. Pada wilayah berbukit dan bergunung, frekuensi liputan awan umumnya lebih tinggi dibandingkan wilayah dataran. Oleh karena itu, penelitian ini mengkaji citra radar Sentinel-1 yang dapat beroperasi baik pada segala kondisi cuaca sehingga meminimalkan efek dari atmosfer. Namun demikian, citra Sentinel-1 belum banyak ditelaah pada wilayah berbukit/bergunung, sehingga dampak terain terhadap iluminasi dan kemampuannya dalam pemisahan kelas tutupan lahan belum banyak diketahui. Klasifikasi tutupan lahan dilakukan dengan pendekatan pemelajaran mesin untuk membandingkan kinerja terbaik dari model klasifikasi Support Vector Machines (SVM) dan Random Forests (RF). Pada model RF, akurasi tertinggi didapatkan dengan persentase sebesar 64.8% pada citra akuisisi musim hujan, dan 63.7% pada citra akuisisi musim kemarau. Sedangkan akurasi terendah didapatkan dengan model SVM kernel Linier dengan persentase 53.2% pada citra akuisisi saat musim hujan, dan 52.7% pada citra akuisisi saat musim kemarau. Penelitian ini menunjukkan bahwa akurasi sangat dipengaruhi oleh pengaturan parameter algoritma pemelajaran mesin. Pemilihan nilai parameter yang tepat dapat menghasilkan akurasi yang lebih baik. Hasil penelitian mengindikasikan bahwa terdapat perbedaan yang cukup signifikan antara hasil klasifikasi pada musim yang berbeda, yang memerlukan pendalaman lebih jauh di masa mendatang., Agricultural sector, especially plantation sub-sector, greatly contributes to Indonesian economy. Plantation commodities commonly cultivated in Indonesia include rubber and oil palm. Planning, development, and monitoring over plantations and agricultural fields often take the advantage of remote sensing technology to facilitate appropriate decision making. Mapping using optical imageries is often hampered by Indonesia's climatic conditions. Clouds and fogs can obstruct objects during data acquisition. In hilly and mountainous areas, the frequency of cloud coverage is generally higher than in plain areas. Therefore, this study examined Sentinel-1 radar which can operate in all weather conditions so as to minimize atmospheric effects. However, Sentinel-1 has yet been widely studied in hilly/mountainous areas, so that terrain impact on illumination and its ability to separate land cover classes are yet to be fully understood. Land cover classification was carried out using machine learning approach to compare the performance of Support Vector Machines (SVM) and Random Forests (RF) classification. Highest accuracy of RF was obtained in rainy season with 64.8% overall accuracy and 63.7% overall accuracy in dry season, while the lowest accuracy of SVM was achieved during dry season with 52.7% overall accuracy, and 53.2% overall accuracy in rainy season. This study showed that the accuracy is strongly influenced by parameter setting of machine learning algorithms. Selection of suited parameter values can yield a better accuracy. The results indicate that accuracy difference is expected when season is taken into account. This warrants further studies in the future.
Keyword: Land cover, random forest, Sentinel-1, support vector machine

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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