Judul: Mangrove Area Change Detection using Sentinel 2A and 2b in Sabira Island, Kepulauan Seribu, DKI Jakarta
Abstrak: Hutan di Indonesia, termasuk hutan mangrove, mengalami penurunan luas. Untuk memantau perubahan tersebut, penginderaan jauh dari satelit Sentinel-2A dan 2B digunakan dengan metode Maximum Likelihood di Pulau Sabira, Kepulauan Seribu, DKI Jakarta. Pengamatan lapangan dilakukan pada Agustus 2019. Hasilnya menunjukkan bahwa luas tutupan mangrove di Pulau Sabira mengalami penurunan sebesar 0,81 hektar dari tahun 2019 hingga 2022 akibat abrasi di pesisir pantai. tahun 2022 nilai indeks vegetasi NDVI berkisar 0,45 hingga 0,8. Band yang digunakan dalam penggabungan false color pada citra Sentinel 2 yaitu band 4-8-11 (R-NIR-SWIR) dapat mengidentifikasi area vegetasi mangrove. Penurunan luasan yang cukup signifikan terjadi pada kelas kerapatan mangrove rapat, sedangkan kelas kerapatan mangrove sedang dan mangrove jarang. Uji akurasi menunjukkan nilai Producer Accuracy sebesar 64,7% - 66,67%, User Accuracy sebesar 68,7% - 62,5%, dan Overall Accuracy sebesar 81,25%. Secara keseluruhan, data citra satelit Sentinel-2A dan 2B dapat digunakan untuk mendeteksi tutupan area mangrove dan perubahannya dari waktu ke waktu., Forests in Indonesia, including mangrove forests, are decreasing in size. To monitor these changes, remote sensing from the Sentinel-2A and 2B satellites was used with the Maximum Likelihood method on Sabira Island, Kepulauan Seribu, DKI Jakarta. Field observations were conducted in August 2019. The results showed that the mangrove cover area on Sabira Island decreased by 0.81 hectares from 2019 to 2022. in 2022 the NDVI vegetation index value ranged from 0.45 to 0.8. The band used in the incorporation of false color in Sentinel 2 imagery is band 4-8-11 (RNirSWIR) can identify areas of mangrove vegetation. A significant decrease in area occurs in the dense mangrove density class, while the moderate mangrove density class and sparse mangroves. The accuracy test shows the Producer Accuracy value of 64.7% - 66.67%, User Accuracy of 68.7% - 62.5%, and Overall Accuracy of 81.25%. Overall, Sentinel-2A and 2B satellite image data can be used to detect mangrove area cover and changes over time.
Keyword: Mangrove, Maximum Likelyhood, Pulau Sabira, QGIS, Remote Sensing

Judul: Mangrove Area Change Detection using Sentinel 2A and 2b in Sabira Island, Kepulauan Seribu, DKI Jakarta
Abstrak: Hutan di Indonesia, termasuk hutan mangrove, mengalami penurunan luas. Untuk memantau perubahan tersebut, penginderaan jauh dari satelit Sentinel-2A dan 2B digunakan dengan metode Maximum Likelihood di Pulau Sabira, Kepulauan Seribu, DKI Jakarta. Pengamatan lapangan dilakukan pada Agustus 2019. Hasilnya menunjukkan bahwa luas tutupan mangrove di Pulau Sabira mengalami penurunan sebesar 0,81 hektar dari tahun 2019 hingga 2022 akibat abrasi di pesisir pantai. tahun 2022 nilai indeks vegetasi NDVI berkisar 0,45 hingga 0,8. Band yang digunakan dalam penggabungan false color pada citra Sentinel 2 yaitu band 4-8-11 (R-NIR-SWIR) dapat mengidentifikasi area vegetasi mangrove. Penurunan luasan yang cukup signifikan terjadi pada kelas kerapatan mangrove rapat, sedangkan kelas kerapatan mangrove sedang dan mangrove jarang. Uji akurasi menunjukkan nilai Producer Accuracy sebesar 64,7% - 66,67%, User Accuracy sebesar 68,7% - 62,5%, dan Overall Accuracy sebesar 81,25%. Secara keseluruhan, data citra satelit Sentinel-2A dan 2B dapat digunakan untuk mendeteksi tutupan area mangrove dan perubahannya dari waktu ke waktu., Forests in Indonesia, including mangrove forests, are decreasing in size. To monitor these changes, remote sensing from the Sentinel-2A and 2B satellites was used with the Maximum Likelihood method on Sabira Island, Seribu Islands, DKI Jakarta. Field observations were conducted in August 2019. The results showed that the mangrove cover area on Sabira Island decreased by 0.81 hectares from 2019 to 2022. in 2022 the NDVI vegetation index value ranged from 0.45 to 0.8. The band used in the incorporation of false color in Sentinel 2 imagery is band 4-8-11 (RNirSWIR) can identify areas of mangrove vegetation. A significant decrease in area occurs in the dense mangrove density class, while the moderate mangrove density class and sparse mangroves. The accuracy test shows the Producer Accuracy value of 64.7% - 66.67%, User Accuracy of 68.7% - 62.5%, and Overall Accuracy of 81.25%. Overall, Sentinel-2A and 2B satellite image data can be used to detect mangrove area cover and changes over time.
Keyword: Mangrove, Maximum Likelyhood, QGIS, Remote Sensing, Pulau Sabira

Judul: Karakteristik Organoleptik dan Kimia Biskuit Berbahan Baku Tepung Whey untuk Penderita Autis
Abstrak: People with autism have an allergic reaction to foods containing gluten and casein. Food processing innovation is needed. Whey powder, the byproduct of cheese, have a high protein but can be consumed by people with autism safely. The objectives of this research to evaluate organoleptic and chemical characteristics of different whey powder-based biscuits to obtain the best formulation as a special food for people with autism. Biscuit formulations with the different addition of whey powder 5%, 10%, 15%, and 20% was observed organoleptic and chemical characteristics. Casein and gluten protein of the best formulation biscuit was analyzed by SDS-PAGE method. The results of this research showed that the addition of whey powder had a significant difference to organoleptic characteristics, the content of ash, protein, and free fatty acid. The SDS-PAGE analysis showed that protein fraction of casein and gluten were not seen significantly with band protein standard on 5% whey powder-based biscuit.
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