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: Land Cover Classification of Rural Areas By  Coupling Sentinel-2 Imagery and Random Forests
Abstrak: Pembangunan wilayah dan pengelolaan sumberdaya memerlukan data tutupan lahan. Dewasa ini, pembangunan tidak hanya berfokus pada kawasan perkotaan melainkan juga sudah mulai merambah ke kawasan perdesaan dan pinggiran kota. Oleh karena itu, pemantauan dan pemetaan tutupan lahan perdesaan (rural) penting untuk dilakukan. Penelitian ini bertujuan untuk mengkaji karakteristik spektral beberapa tutupan lahan dan melakukan pemodelan tutupan lahan rural dengan metode Random Forests (RF). Penelitian dilaksanakan di perbatasan Kabupaten Pandeglang dan Kabupaten Lebak. Percobaan dilakukan dengan menggunakan data citra Sentinel-2 yang memiliki resolusi tinggi. Metode RF digunakan dalam melakukan klasifikasi berbagai jenis tutupan lahan yang diamati yaitu; lahan terbangun, lahan pertanian semusim, kebun campuran, kebun kelapa sawit, kebun karet monokultur, karet rakyat (jungle rubber), dan tanaman kayu (timber). Berdasarkan percobaan yang telah dilakukan diketahui bahwa kombinasi kanal SWIR (Shortwave Infra Red) cukup mampu memisahkan tutupan lahan yang ada dibandingkan kombinasi kanal lainnya. Selain itu, metode RF cukup efektif untuk klasifikasi tutupan lahan meskipun variasi kepercayaan informasi pada tingkat kelas tutupan lahan masih belum sepenuhnya seragam. Penelitian ini menunjukkan bahwa tidak semua kanal yang digunakan pada citra Sentinel-2 berkontribusi terhadap ekstraksi informasi, khususnya pada tutupan lahan yang dinilai cukup sulit dibedakan seperti karet rakyat. Namun demikian, metode klasifikasi RF yang digunakan cukup efektif dengan akurasi total sebesar 77.7%., Regional development and the management of natural resources require land cover data. The development, nowadays, does not merely set its focus to urban areas, but has been expanded by incorporating rural and suburban areas. Monitoring and mapping of rural land cover is therefore important to accommodate this need. This study aimed to assess spectral characteristics of selected land cover types and to model rural land cover using the Random Forests (RF). The research was conducted over an area bordering Pandeglang and Lebak Regencies. The experiment demonstrated the value of using Sentinel-2 image, which is considerably a high resolution data. The RF method was used in classifying various types of land cover, namely; built-up, seasonal agricultural crops, mixed gardens, oil palm plantations, monoculture rubber plantations, jungle rubber, and timber plants. Based on the experiment, it was discovered that the SWIR (Shortwave Infra Red) channel was quite capable of separating the existing land cover compared to other band combinations. In addition, the RF method is fairly effective for land cover classification although confidence at class level varied. This study showed that not all channels used in Sentinel-2 imagery contributed to the extraction of information, especially on land cover that was considered quite difficult to distinguish such as smallholder rubber. However, the RF classification method was somewhat effective with a total accuracy of 77.7%.
Keyword: klasifikasi, rural, Sentinel-2, tutupan lahan, random forests, karakteristik spektral

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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