Judul: Land Cover/Use Change Analysis in Landsat 8
with Machine Learning at Sumedang Regency, West Java
Abstrak: Kabupaten Sumedang merupakan salah satu kabupaten terpilih dalam pelaksanaan proyek strategis nasional, berupa pembangunan Waduk Jatigede dan Jalan Tol Cisumdawu. Pembangunan infrastruktur berdampak negatif bagi sektor pertanian karena menyebabkan alih fungsi lahan pertanian, sehingga dibutuhkan pemantauan perubahan penutupan/penggunaan lahan secara terukur. Penelitian ini bertujuan mengidentifikasi pola nilai reflektan setiap penutupan/penggunaan lahan, membandingkan pendekatan RF dan SVM dalam klasifikasi penutupan/penggunaan lahan, dan menganalisis tren perubahan penutupan/penggunaan lahan di Kabupaten Sumedang pada tahun 2015 dan 2023. Pola nilai reflektan setiap penutupan/penggunaan lahan bersifat khas, tetapi penggunaan lahan sawah dan ladang/tegalan memiliki pola yang mirip dan lebih rentan terhadap misklasifikasi. Pendekatan RF dan SVM menghasilkan akurasi klasifikasi yang tinggi, yaitu sebesar 92,2% dan 93,6% tahun 2015 dan 2023 untuk RF, serta 97,70% dan 98% tahun 2015 dan 2023 untuk SVM. Perbedaan hasil klasifikasi RF dan SVM tahun 2015 adalah sebesar 38,66%, sedangkan pada tahun 2023 adalah sebesar 34,64%. Perbedaan tersebut terjadi karena adanya perbedaan cara kerja klasifikasi. Tren perubahan penutupan/penggunaan lahan dianalisis menggunakan pendekatan SVM karena menghasilkan klasifikasi dengan akurasi terbaik dan di atas 80%. Penutupan/penggunaan lahan berupa hutan, perkebunan, sawah, dan tanah terbuka mengalami penurunan luas lahan, sedangkan penutupan/penggunaan lahan badan air, lahan terbangun, serta ladang/tegalan mengalami peningkatan luas lahan dari tahun 2015 sampai 2023. Kata kunci: Random Forest, Support Vector Machine, proyek strategis nasional, Sumedang Regency is one of the selected districts in implementing national strategic projects, in the form of the construction of the Jatigede Reservoir and the Cisumdawu Toll Road. Infrastructure development has a negative impact on the agricultural sector because it causes conversion of agricultural land, so it is necessary to monitor changes in land cover/use in a measurable manner. This research aims to identify patterns of reflectance values for each land cover/use, compare RF and SVM approaches in classifying land cover/use, and analyze trends in changes in land cover/use in Sumedang Regency in 2015 and 2023. Patterns of reflectance values for each land cover/use is typical, but the use of rice fields and fields/moors has similar patterns and is more susceptible to misclassification. The RF and SVM approaches produce high classification accuracy, namely 92,2% and 93,6% in 2015 and 2023 for RF, and 97,7% and 98% in 2015 and 2023 for SVM. The difference in RF and SVM classification results in 2015 was 38,66%, while in 2023 it was 34,64%. This difference occurs because of differences in the way classification works. Land cover/use change trends were analyzed using the SVM approach because it produces classification with the best accuracy and above 80%. Land cover/use in the form of forests, plantations, rice fields, and open land experienced a decrease in land area, while land cover/use of water bodies, built-up land, and fields/moorlands experienced an increase in land area from 2015 to 2023.
Keyword: random forest, Support Vector Machines (SVM), proyek strategis nasional

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: 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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