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: 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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