Judul: Identification of Agricultural Drought Mapping
Based on TVDI and SMI Algorithms in Indramayu Regency
Abstrak: bupaten Indramayu sebesar 79,8 %. Salah satu solusi memitigasi kekeringan adalah membuat peta kekeringan pertanian. Penelitian ini bertujuan untuk mengidentifikasi wilayah persebaran yang mengalami kekeringan pertanian menggunakan metode Temperature Vegetation Dryness Index (TVDI) dan Soil Moisture Index (SMI). Metode TVDI dan SMI dalam mengindikasikan kekeringan berfokus pada perbedaan suhu permukaan tanah dan vegetasi. Kedua metode ini berbasis parameter empirik. Identifikasi sebaran kekeringan lahan pertanian baik menggunakan metode SMI maupun TVDI menghasilkan sebaran kekeringan lahan yang mirip. Terjadinya kekeringan tidak dipengaruhi secara langsung oleh curah hujan. Hal ini dikarenakan sebagian besar lahan sawah di Kabupaten Indramayu merupakan lahan sawah irigasi. Sawah irigasi mampu mengantisipasi kekeringan dengan pengairan melalui sungai terdekat. Sedangkan sawah tadah hujan, mampu diantisipasi dengan metode pompanisasi. Secara visual, sebaran kekeringan mengalami peningkatan dari tahun 2019 hingga tahun 2023 pada bulan Juni hingga Agustus sebesar 129.880 ha. Kekeringan yang terjadi ditandai juga dengan penurunan luas panen di tahun 2019 dan 2023, sebesar 20.341 ha dan 13.975 ha. Upaya awal menentukan daerah prioritas penanganan kekeringan untuk meminimalisir kerugian adalah dengan cara pemetaan kekeringan pertanian., Drought is the main cause of crop failure in Indramayu Regency with 79.8%. One solution to mitigate drought is to create an agricultural drought map. This research aims to identify the distribution areas that experience agricultural drought using the Temperature Vegetation Dryness Index (TVDI) and Soil Moisture Index (SMI) methods. The TVDI and SMI methods of indicating drought focus on differences in soil surface temperature and vegetation. Both methods are based on empirical parameters. Identifying the distribution of agricultural land drought using both the SMI and TVDI methods produces a similar distribution of land drought. The occurrence of drought is not directly influenced by rainfall. It’s because most of the rice fields in Indramayu Regency are irrigated rice fields. Irrigated rice fields can anticipate drought by irrigating through the nearest river. Meanwhile, rain-fed rice fields can be anticipated with the pompanization method. Visually, the distribution of drought has increased from 2019 to 2023 in June to August by 129.880 ha. Drought is also characterized by a decrease in harvest areas in 2019 and 2023, amounting to 20.341 ha and 13.975 ha, respectively. Initial effort to determine priority areas for drought management to minimize losses is by mapping agricultural drought.
Keyword: penginderaan jauh, padi, petani, kelembapan tanah, sawah

Judul: Identifikasi lahan pertanian rawan kekeringan dengan metode sistem informasi geografis
Abstrak: Drought is an event where rainfall is very low for long time period. Drought is a common natural phenomenon in Indonesia. It affects a wide variety of events, one of which is crop failure. This study aims to determine the drought prone of an area in Indramayu using GIS methods based on several bio-physic parameters, to determine the distribution and characteristics of drought prone area in Indramayu district, and to see the difference in the distribution of drought prone area by using different or the equal weights of parameter in annual rainfall or monthly rainfall (dry season). The study consists of several phases namely preparation, collection of base maps and create thematic map, satellite imagery interpretation, analysis and making drought-vulnerable, and presentation of research results. The bio-physic parameters used in this study were rainfall, landforms, land cover, drainage, slope, and stream buffers. All parameters are combined in a GIS analysis using different weighting and equal weighting with annual rainfall and dry season rainfall. The most influential parameter will get the highest value of weight and parameters with small influence will be given a low weight value. Modeling bio-physic parameter with different weights to the annual rainfall resulted in two vulnerable classes, less vulnerable 77.24%, and vulnerable 22.76%, while for the dry season rainfall resulted three classes including not vulnerable 0.74%, less vulnerable 85.20%, and vulnerable 14.06%. Maps with the equal weighting parameters for the annual rainfall resulted in four classes including not vulnerable 0.07%, less vulnerable 76.26%, vulnerable 23.56% and very vulnerable 0.11%. For the dry season rainfall also resulted in four classes: not vulnerable 0.47%, less vulnerable 84.12%, and vulnerable 15.28%, and the very vulnerable classes 0.12%. The subdistrict that potentially effected by drought prone area are Trisi subdistrict and Gantar subdistrict.
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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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