Judul: Sentinel-2 Image for Paddy Field Mapping using Object-Based Classification
Abstrak: Kabupaten Tangerang merupakan salah satu kawasan sasaran dalam program LP2B dengan pertimbangan memiliki kawasan sawah yang luas dengan potensi panen yang tinggi dalam menunjang produksi padi skala nasional. Namun demikian, produksi padi di Kabupaten Tangerang terancam menurun setiap tahun akibat alih fungsi lahan pertanian menjadi lahan non pertanian dengan rata-rata penyusutan lahan sawah sebesar 2,4% per tahun. Dengan demikian, pemetaan tutupan lahan secara berkala perlu dilakukan dengan efektif dan efisien. Pemanfaatan penginderaan jauh menggunakan citra Sentinel 2A dengan metode klasifikasi berbasis objek merupakan alternatif metode pemetaan tersebut. Pada penelitian ini, kombinasi data berupa band, indeks (NDVI, NDWI, NDBI), dan transformasi tasseled cap transformation (TCT) menjadi data masukan dalam proses segmentasi. Metode klasifikasi terbimbing yang digunakan setelah proses tersebut melibatkan maximum likelihood (MLC) dan spectral angle mapper (SAM). Algoritma berbasis peluang yaitu MLC menghasilkan akurasi keseluruhan tertinggi pada data-8 (TCT) sebesar 81% dan data-5 (komposit band 11, 8 dan 2) sebesar 78% sedangkan algoritma berbasis spektral (SAM) menghasilkan akurasi keseluruhan tertinggi pada data-7 (kombinasi NDVI, NDBI, dan NDWI dan band panjang gelombang tampak) sebesar 76% dan data-3 (komposit band 11, 4, dan 3) sebesar 71%. Dengan demikian, penggunaan metode berbasis peluang cenderung memiliki kemampuan klasifikasi lebih baik dibandingkan metode berbasis spektra. Kualitas segmentasi pada klasifikasi tutupan lahan sawah berbasis objek dipengaruhi oleh kesesuaian data masukan dan jenis klasifikasi terhadap objek sawah., Tangerang Regency is one of the target areas in the LP2B program considering its large rice areas with high potential harvesting, leading to a support of nation-wide paddy production. Rice production in Tangerang Regency, however, tends to decline annually due to the conversion of agricultural to non-agricultural lands with an average of 2.4% per year. Thus, land cover data, on a regular basis, needs to be developed. Coupling Sentinel 2A imagery with object-based classification approach is an alternative to pixel-wise mapping. In this study, the combination of data in the form of bands, indices (NDVI, NDWI, NDBI), and tasseled cap transformation (TCT) served as data inputs during segmentation process. Supervised classification involved statistical-based maximum likelihood (MLC) and spectral angle mapper (SAM). The MLC produced the highest overall accuracy over data-8 (TCT) of 81% and data-5 (composite bands 11, 8 and 2) of 78% while SAM yielded the highest overall accuracy using data-7 (combination of NDVI, NDBI, and NDWI and visible wavelength bands) by 76% and data-3 (composite bands 11, 4, and 3) by 71%. Thus, the use of probability-based methods appeared to deliver a better classification than spectra-based method. Quality of segmentation in the classification of paddy fields was influenced by data input and classification approach.
Keyword: Paddy field, segmentation, maximum likelihood, spectral angle mapper

Judul: Utilization of Remote Sensing Technology for Land Use Mapping in the Coastal Area Banten Bay
Abstrak: Wilayah pesisir Teluk Banten memiliki potensi sumberdaya alam kelautan dan perikanan. Potensi tersebut suatu saat bisa rusak (degradasi) atau hilang akibat dampak pembangunan yang tidak memperhatikan aspek lingkungan pesisir. Pemantauan penggunaan lahan dapat dilakukan melalui teknologi penginderaan jauh menggunakan Citra Sentinel-2A. Tujuan penelitian ini yaitu mengetahui kemampuan citra Sentinel-2A dalam pemetaan penggunaan lahan di Kawasan Pesisir Teluk Banten menggunakan algoritma Maximum Likelihood (MLH) dan Support Vector Machine (SVM). Hasil klasifikasi penggunaan lahan dari citra sentinel-2A menghasilkan 6 kelas yaitu, badan air, mangrove, pemukiman, sawah, tambak, dan tanah terbuka. Hasil yang didapatkan dari trial and error pada pengolahan data, dengan algoritma Multiresolution Segmentation mendapatkan nilai scale 60 karena dapat memberikan hasil segmentasi yang cukup baik. Klasifikasi berbasis piksel dengan algoritma MLH dan berbasis objek dengan algoritma SVM, mampu memetakan penggunaan lahan dengan tingkat akurasi klasifikasi, masing-masing sebesar 75,3% dengan algoritma MLH dan sebesar 77,4% dengan SVM. Nilai Kappa masing-masing adalah 0,69 dan 0,72. Nilai perbandingan Z-test dari kedua klasifikasi berbasis piksel dengan objek (MLH vs SVM) yaitu 0,48 yang berarti keduanya tidak berbeda signifikan., The coastal area of Banten Bay had the potential for marine and fisheries resources. This potential could one day be damaged (degraded) or lost due to the impact of development that did not pay attention to aspects of the coastal environment. Land use monitoring could be done through remote sensing technology with the use Sentinel-2A Imagery. The purpose of this study was to determine the ability of Sentinel-2A imagery in mapping land use in the Banten Bay Coastal Area using the Maximum Likelihood (MLH) and Support Vector Machine (SVM) algorithms. The land use classification results from sentinel-2A imagery produced six classes, (water bodies, mangroves, residence, rice fields, ponds, and open land). Pixel-based classification with the MLH algorithm and object-based with the SVM algorithm could map land use with an accuracy of 75.3% for MLH algorithm and 77.4% for SVM algorithm. Kappa values were 0.69 (MLH) and 0.72 (SVM). The Z-test comparison value of the two pixel-based classifications with objects (MLH vs SVM) was 0.48, which means that the two were not significantly different.
Keyword: Land Use, MLH, Remote Sensing, SVM

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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