Judul: Random Forest and Gradient Boosting Machine for Mapping Flood Locations in DKI Jakarta with Copernicus Sentinel-1A Data
Abstrak: Indonesia banyak mengalami kejadian bencana. Salah satunya adalah banjir yang terjadi di DKI Jakarta, 1 Januari 2020. Beberapa media massa menyebutkan banjir ini sebagai banjir terparah yang menimpa Jakarta. Pemerintah DKI Jakarta mencatat hal sebaliknya dengan data luas wilayah administrasi terdampak banjir. Perbedaan klaim tentang tingkat keparahan banjir memerlukan pendekatan luas banjir yang lebih akurat. Algoritma machine learning pada data citra satelit hasil penginderaan jauh memungkinkan pendekatan luas wilayah banjir secara lebih cepat dan akurat. Penelitian ini bertujuan untuk membandingkan kebaikan model algoritma Random Forest (RF) dan Gradient Boosting Machine (GBM) untuk melakukan prediksi wilayah banjir di DKI Jakarta sehingga dari hasilnya didapat prediksi luas wilayah DKI Jakarta yang terkena banjir. Prediksi dilakukan pada Copernicus Sentinel-1A Data di dua waktu yang berbeda (puncak musim kemarau dan setelah banjir). Setelah melakukan praproses citra, melalui pengamatan secara visual dilakukan pelabelan permukaan air dan permukaan non-air. Ekstraksi nilai dilakukan pada data citra menghasilkan polarisasi VH, VV, dan label. Label digunakan sebagai peubah target (y), sementara polarisasi VH, VV dan kombinasinya digunakan sebagai peubah prediktor pada algoritma RF dan GBM. Penyeimbangan data dan penggunaan data contoh sebesar 5% digunakan untuk mempercepat proses machine learning. Perbandingan dengan uji sidik ragam antara data contoh (5%) dan seluruh data seimbang (100%) mendapatkan nilai p-value antara 0,141 – 0,924 untuk berbagai kombinasi citra, algoritma dan ukuran kebaikan model. Perbandingan dengan uji sidik ragam antar kombinasi prediktor menunjukkan p-value 0,035 – 1,000. Perbandingan uji sidik ragam antar algoritma menghasilkan p-value antara <2 x 10-16 – 1,93 x 10-12. Fungsi paling sederhana dari berbagai kombinasi y = f(VH,VV) digunakan dengan algoritma GBM karena tidak ada perbedaan yang nyata antar kombinasi prediktor. Hasil prediksi permukaan air didapat 168.317 piksel pada puncak musim kemarau dan 451.760 piksel setelah banjir. Perbandingan antara kedua prediksi tersebut menghasilkan, 153.828 piksel permukaan air permanen dan 297.932 piksel atau setara 2.979,32 ha diduga sebagai wilayah banjir. Algoritma GBM menunjukkan hasil akurasi dan F1 yang lebih baik daripada algoritma RF. Kombinasi berbagai prediktor tidak menunjukkan hasil yang berbeda nyata pada algoritma GBM, namun menunjukkan perbedaan pada algoritma RF pada citra setelah banjir. Penggunaan data contoh (5%) tidak menunjukkan hasil yang berbeda dengan seluruh data seimbang (100%). Hasil verifikasi antara prediksi luas wilayah banjir dengan wilayah adminsitrasi kelurahan terdampak banjir menunjukkan hasil yang cukup akurat (75,68%)., Indonesia has experienced many disasters. One of them is the flood that occurred in DKI Jakarta, January 1st, 2020. Several mass media mentioned it as the worst flood to hit Jakarta. The DKI Jakarta government stated the opposite with data on the administrative areas affected by flooding. Different claims about flood severity require a more accurate approach to flood area. Machine learning algorithms on remote sensing satellite imagery data allow a faster and more accurate approach to the flood area. This study aims to compare the goodness of the Random Forest (RF) and Gradient Boosting Machine (GBM) algorithm models for predicting flood locations in DKI Jakarta so that the results can predict the area of DKI Jakarta affected by flooding. Predictions were made on Copernicus Sentinel-1A data at two different times (peak of the dry
Keyword: banjir, gradient boosting machine, random forest, machine learning, copernicus sentinel-1A

Judul: Evaluasi Potensi Bahaya Banjir di DKI Jakarta  Menggunakan  Analisis  Multi-Kriteria  dan  Satelit  Berbasis  Flood  Hazard Index (FHI)
Abstrak: Floods are one of the most destructive and common natural disasters in the world. Globally, flood disasters have an increasing trend from year to year. In Indonesia istelf, the number of flood disasters from 2011 to 2020 was recorded at 7.574 cases which caused losses in several aspects of life. Losses caused by flooding include death, minor/severe injuries, missing persons, damage to houses, access roads, as well as education and health centers. Various methods have been applied as a preventive action for flood control. One of the methods commonly used to prevent flooding is creating water infiltration channels and cleaning rivers periodically from garbage. However, there is another approach that can be used, namely by making a flood hazard map based on the Flood Hazard Index (FHI). The FHI method is able to combine Geographic Information System (GIS) analysis with Multi Criteria Decision Analysis (MCDA) to obtain flood hazard maps. The MCDA technique used is Analytical Hierarchy Process (AHP) to determine the weight of the influence of factors on flood events. This study aims to obtain the level of flood hazard in DKI Jakarta and the most influential factors in causing flooding. In the analysis process, the seven factors that cause flooding in DKI Jakarta are defined, namely flow accumulation, distance from rivers, density of river networks, slopes, rainfall intensity, land cover, and geology. AHP analysis is used to obtain weighted scores, while GIS analysis is used to obtain scores. The value of weights and scores are combined to obtain FHI. Sensitivity analysis was then carried out to obtain the effective weights used in constructing the flood hazard index. The FHI map, FHI sensitivity (FHIS), and FHI equal weight (FHIW) were validated using the 2013 flood inundation map to find the index that best represents flood events in DKI Jakarta. The results of the analysis show that the FHIS index is best used to represent the flood hazard in DKI Jakarta (59%). Factors that influence in causing flooding are rainfall intensity, followed by land use, and slope. FHIS values have a pattern of high in the north and low in the south. This indicates that the northern region has a higher level of danger than the southern region. This phenomenon occurs due to the high intensity of rain in the northern part of Jakarta due to its location close to the Java Sea. In addition, the northern area of Jakarta is downstream of 13 rivers so it has a higher potential for flooding.
Keyword: AHP, PCI, resample, sensitivity, single-parameter analysis

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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