Judul: Pemetaan Sebaran dan Kerapatan Mangrove di Teluk Pangpang, Banyuwangi Menggunakan Maximum Likelihood dan Artificial Neural Network
Abstrak: Mangrove menjadi ekosistem pesisir terbesar kedua di Indonesia serta memiliki peranan penting dalam siklus hidup biota laut sebagai nursery ground, spawning ground dan feeding ground. Penelitian ini bertujuan untuk memetakan sebaran dan kerapatan mangrove di Teluk Pangpang menggunakan citra Sentinel 2B. Metode Hemispherical Photography digunakan untuk memperoleh data tutupan kanopi mangrove serta analisis Normalized Difference Vegetation Index digunakan untuk memetakan sebaran kerapatan mangrove. Hasil uji akurasi 2 klasifikasi menunjukkan bahwa metode artificial neural network menghasilkan nilai Overall accuracy 92,73% (kappa 0,84) dan lebih baik daripada metode maximum likelihood dengan nilai Overall accuracy 89,09% (kappa 0,76). Analisis Normalized Difference Vegetation Index (NDVI) menghasilkan 3 kelas kerapatan mangrove. Luas yang dihasilkan adalah 925,8 ha dengan mangrove rapat 672,99 ha, mangrove sedang 61,83 ha, dan mangrove jarang 178,75 ha. Pengujian korelasi data survei tutupan kanopi dengan nilai NDVI didapatkan nilai korelasi (r) sebesar 0,893287 dan nilai determinasi (R2) sebesar 0,797961., The mangrove ecosystem is the second largest coastal ecosystem in Indonesia, playing a crucial role in the life cycle of marine biota. It serves as a nursery ground, spawning ground, and feeding ground, making it an essential component of the marine ecology. This study aimed to map the distribution and density of the mangroves in Pangpang Bay, utilizing Sentinel 2B imagery. To gather data on mangrove canopy cover, the Hemispherical Photography method was employed, while the Normalized Difference Vegetation Index analysis was utilized to map the mangrove density distribution. The results of the classification accuracy tests revealed that the artificial neural network method produced an overall accuracy value of 92.73% (kappa 0.84), surpassing the maximum likelihood method, which scored an overall accuracy value of 89.09% (kappa 0.76). The Normalized Difference Vegetation Index (NDVI) analysis yielded 3 classes of mangrove densities, resulting in a total area of 925.8 ha, with 672.99 ha of dense mangroves, 61.83 ha of medium mangroves, and 178.75 ha of sparse mangroves. A correlation analysis of the canopy cover survey data with the NDVI value produced a correlation value (r) of 0.893287 and a determination value (R2) of 0.797961.
Keyword: Teluk Pangpang, Sentinel 2B, Kerapatan, Artificial Neural Network, Maximum Likelihood, Mangrove, NDVI

Judul: Mapping of Mangrove Distribution and Density Using Sentinel-2A in Bedono Village, Demak Regency
Abstrak: Mangrove merupakan salah satu ekosistem pesisir yang memiliki peranan penting untuk menjaga kestabilan garis pantai dan sebagai tempat habitat bagi biota laut. Penelitian ini bertujuan untuk mengetahui sebaran dan kerapatan mangrove di Desa Bedono, Kabupaten Demak menggunakan Sentinel-2A. Algoritma Maximum Likelihood digunakan untuk mengatahui sebaran mangrove di daerah penelitian. Analisis kerapatan mangrove dihasilkan menggunakan perbandingan hasil uji regresi linear terhadap 3 indeks vegetasi yang berbeda yaitu NDVI, EVI, dan mRE-SR. Klasifikasi mangrove menghasilkan 3 kerapatan yaitu mangrove rapat (20,96 ha), mangrove sedang (23,52 ha), dan mangrove jarang (6,70 ha). Luas total mangrove di Desa Bedono sebesar 51,18 ha. Uji akurasi menggunakan confusian matrix menghasilkan nilai sebesar 89,80% dan nilai kappa coefficient sebesar 0,69. Hasil uji regresi linear sederhana antara data tutupan kanopi mangrove berdasarkan survei lapang dengan EVI menghasilkan nilai koefisien korelasi (r) sebesar 0,85 dan koefisien determinasi (R2) sebesar 0,724., Mangrove is one of the coastal ecosystems that has an important role in coastal stability and as a habitat for marine life. The research aims to determine the distribution and density of mangroves in Bedono Village, Demak Regency, using Sentinel-2A. The Maximum Likelihood algorithm is used to identify mangrove spread in the research area. An analysis of the density of the mangrove was obtained using a linear regression test comparison of three different vegetation indices: NDVI, EVI, and mRE-SR. The classification of mangroves yields three densities: tight mangrove (20,96 ha), medium mangrove (23,52 ha), and rare mangrove (6,70 ha). The total mangrove area in Bedono Village is 51,18 ha. The accuracy test using the confusion matrix yielded a value of 89,80% and the kappa coefficient of 0,69. The result of a simple linear regression test between mangrove canopy cover data based on a field survey with EVI yielded a correlation coefficient value (r) of 0,85 and a determination factor (R2) of 0,724.
Keyword: mangrove, remote sensing, sentinel-2A, Bedono, EVI

Judul: Sintesis dan Pencirian Kompleks Fe(III)-Kuersetin
Abstrak: Quercetin belongs to flavonols which is able to chelate metal ions. Quercetin may chelate metal ions at 3’,4’-catechol, 3- or 5-hydroxycromone sites. In this study, quercetin was complexed with Fe(III). The complex with Fe(III) ions from FeCl3·6H2O was more stable than the complex derived from Fe(NO3)3·9H2O. The quercetin was yellow in color and showed maximum wavelengths (λ􀭫􀭟􀭶) at 250 and 370 nm. The Fe(III)-quercetin complex from Fe(NO3)3·9H2O was yellowish green with 􀟣􀭫􀭟􀭶 at 280 and 420 nm, whereas that from FeCl3·6H2O was dark green with 􀟣􀭫􀭟􀭶 at 280 and 430 nm. The 􀟣􀭫􀭟􀭶 shift indicated that the complex was formed at 3-hydroxychromone site. The Fe(III)- quercetin complex was formed with 1:1 metal-ligand mole ratio based on atomic absorption spectrophotometry analysis and the X-ray diffractogram showed that the crystallinity of the complex was 37%.
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