Judul: Pemetaan Sebaran Mangrove Di Pantai Lontar, Provinsi Banten
Abstrak: Terancamnya keberadaan mangrove akibat tingginya tingkat abrasi di Pantai Lontar, Kecamatan Tirtayasa, Serang, Banten memerlukan pemantauan secara berkala agar tidak terjadi degradasi terhadap keberadaan ekosistem mangrove disana. Penelitian ini bertujuan untuk memetakan sebaran kerapatan serta luasan mangrove di Pantai Lontar menggunakan Citra Sentinel-2A. Algoritma indeks kerusakan mangrove (IM) dan mangrove vegetation index (MVI) digunakan untuk membedakan antara vegetasi mangrove dan bukan mangrove, sedangkan algoritma normalized difference vegetation index (NDVI) digunakan untuk memetakan sebaran kerapatan mangrove. Klasifikasi yang dilakukan dengan menggunakan maximum likelihood menghasilkan 3 kelas kerapatan yaitu rapat (13,16 ha), sedang (8,69 ha), dan jarang (7,51 ha), dengan dominansi kelas rapat pada wilayah kajian. Luas total mangrove di Pantai Lontar pada tahun 2022 sebesar 29,41 ha. Uji akurasi menggunakan confusion matrix terhadap kerapatan mangrove menghasilkan akurasi sebesar 93,33% dengan koefisien kappa sebesar 0,900. Uji korelasi persentase tutupan kanopi mangrove antara nilai NDVI dan data lapang menghasilkan koefisien korelasi (r) sebesar 0,90 serta koefisien determinasi (R2) sebesar 0,81., The threat to the existence of mangroves due to the high level of abrasion on Lontar Beach, Tirtayasa District, Serang, Banten requires regular monitoring so that there is no degradation of the existence of the mangrove ecosystem. This study aimed to map the distribution of mangrove density and area on Lontar Beach using Sentinel-2A Imagery. The mangrove damage index (IM) and mangrove vegetation index (MVI) algorithms are used to distinguish between mangrove and non-mangrove vegetation, while the normalized difference vegetation index (NDVI) algorithm is used to map the distribution of mangrove density. Classification carried out using maximum likelihood resulted in 3 density classes, namely dense (13,16 ha), medium (8,69 ha), and rare (7,51 ha), with the dominance of dense classes in the study area. The total area of mangroves in Lontar Beach in 2022 is 29.41 ha. The accuracy test using the confusion matrix on mangrove density resulted in an accuracy of 93.33% with a kappa coefficient of 0.900. A Correlation test of the percentage of mangrove canopy cover between NDVI values and field data resulted in a correlation coefficient (r) of 0.90 and a coefficient of determination (R2) of 0.81.
Keyword: Kerapatan Mangrove, NDVI, Pantai Lontar, Penginderaan Jauh, Sentinel-2A, Lontar Beach, Remote Sensing, Mangrove Density

Judul: Analysis of Distribution and Density of Mangrove Forests from Sentinel-2B Image Processing in Kurau Region, Bangka Belitung
Abstrak: Kawasan hutan mangrove memiliki manfaat dalam aspek sosial, ekonomi, maupun ekologi. Fungsi fisik hutan mangrove sebagai pengendali intrusi air laut terhadap daratan, kawasan penyanga, dan pelindung garis pantai. Algoritma Maximum Likelihood Classification (MLC) digunakan untuk memetakan sebaran mangrove. Analisis Normalized Difference Vegetation Index (NDVI) digunakan untuk mengetahui indeks kerapatan vegetasi suatu daerah. Dari hasil perhitungan Hutan mangrove di Kurau, Bangka Belitung pada 2021 didapatkan luas total 695.94 Ha yang terbagi menjadi tiga kelas kerapatan, yaitu mangrove padat (446.36 Ha), mangrove sedang (110.43 Ha), dan mangrove jarang (80.58 Ha). Uji akurasi dengan confusion matrix mendapatkan akurasi sebaran sebesar 94.02% dengan kappa coefficient 0.88, dan akurasi kerapatan sebesar 90% dengan kappa coefficient sebesar 0.81. Pengujian korelasi antara data survei kerapatan mangrove dengan NDVI didapatkan nilai korelasi (r) sebesar 0.8299 dan nilai determinasi (R2) sebesar 0.6887., Mangrove areas have benefits in social, economy, and ecology aspects. The physical function of mangrove forests is to control sea water intrusion into land area, buffer areas, and coastlines protector. Maximum Likelihood Classification (MLC) algorithm is used to map mangrove coverage distribution. Normalized Difference Vegetation Index (NDVI) analysis is used to determined the vegetation density index of an area. The results of computations obtained mangrove forest area in Kurau region, Bangka Belitung in 2021 has a total area of 695.45 Ha which is divided into three density classes, namely dense mangrove (446.36 Ha), moderate mangrove (110.43 Ha), and rare mangrove (80.58 Ha). The accuracy test using confusion matrix got a distribution accuracy of 94.02% with a kappa coefficient of 0.88, and a density accuracy of 90% with kappa coefficient of 0.81. The correlation test between mangrove density survey data and NDVI obtained a correlation value (r) of 0.829 and determination value (R2) of 0.6887.
Keyword: Kurau, Sentinel-2B, Area, Density, Mangrove, NDVI, Remote Sensing

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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