Judul: Analysis of Flood Prone Areas in the Cimandiri Watershed in Warungkiara District, Sukabumi Regency Based on GIS
Abstrak: Floods can cause material and immaterial damage. The purpose of this study is to map flood-prone areas in the Cimandiri watershed in the Warungkiara sub-district based on GIS and determine priority land cover for rescue from flood disasters. The method used in the analysis of flood susceptibility is the method of scoring and weighting. The flood susceptibility parameters used are slope, land height, land cover, river buffer, rainfall, soil type, and soil texture. The wide distribution of flood susceptibility in Warungkiara District obtained starting from the largest, respectively, namely low level (5,205.37 ha), medium (2,169.66 ha), safe (1,285.55 ha), and high (305.59 ha). Priority land cover that must be saved from flooding are settlements (351.42 ha) and plantations (2,658.66 ha). After the drainage and infiltration wells were built, the low-level (130.06 ha), medium (195.45 ha) and high (25.91 ha) flood susceptibility classes in residential areas became safe classes. Meanwhile, after the terracing was built, the safe level (677.73 ha), low (1,574.20 ha), and medium (406.72 ha) vulnerability classes in the plantation area made the safe level flood vulnerability class to 2,227.37 ha and low to an area of 431.29 ha., Banjir dapat memberikan kerusakan dari segi materiel maupun immateriel. Tujuan dari penelitian ini adalah memetakan daerah rawan banjir pada DAS Cimandiri di Kecamatan Warungkiara berbasis SIG dan menentukan tutupan lahan prioritas penyelamatan dari bencana banjir yang selanjutnya pada tutupan lahan tersebut dilakukan skenario berupa pembuatan prasarana yang dapat mengatasi banjir. Metode yang digunakan dalam analisis kerawanan banjir adalah metode skoring dan pembobotan. Parameter kerawanan banjir yang digunakan yaitu kemiringan lereng, ketinggian lahan, tutupan lahan, buffer sungai, curah hujan, jenis tanah, dan tekstur tanah. Sebaran luas kerawanan banjir di Kecamatan Warungkiara yang diperoleh dimulai dari terbesar secara berturut-turut yaitu tingkat rendah (5.205,37 ha), sedang (2.169,66 ha), aman (1.285,55 ha), dan tinggi (305,59 ha). Tutupan lahan prioritas yang harus diselamatkan dari banjir yaitu permukiman (351,42 ha) dan perkebunan (2.658,66 ha). Setelah dibangun drainase dan sumur resapan, kelas kerawanan banjir tingkat rendah (130,06 ha), sedang (195,45 ha), dan tinggi (25,91 ha) di kawasan permukiman menjadi kelas aman. Sedangkan setelah dibangun terasering, kelas kerawanan tingkat aman (677,73 ha), rendah (1.574,20 ha), dan sedang (406,72 ha) di kawasan perkebunan, membuat kelas kerawanan banjir tingkat aman menjadi seluas 2.227,37 ha dan kelas rendah menjadi seluas 431,29 ha.
Keyword: ArcGIS, flood, flood vulnerability, GIS

Judul: Analysis of Flood Prone Areas in the Krukut Watershed Based on Geographical Information Systems
Abstrak: Indonesia merupakan negara yang sering mengalami bencana alam seperti banjir. Banjir dapat berdampak pada kehidupan sosial, ekonomi, kerusakan material, kerusakan lingkungan dan kerusakan warisan budaya. Penanggulangan banjir perlu dilakukan untuk mengurangi dampak banjir yang begitu merugikan. Penanggulangan banjir dapat dilakukan dengan memetakan daerah rawan banjir dengan kajian Sistem Informasi Geografis (SIG) menggunakan perangkat lunak ArcGIS. Metode penelitian berupa metode deskriptif yang terdiri dari pengumpulan data, pengolahan data awal dan data lanjutan. Proses identifikasi wilayah rawan banjir menggunakan metode skor (scoring). Faktor yang mempengaruhi kerawanan adalah faktor curah hujan, kemiringan lereng, sungai dan tutupan lahan, jenis tanah dan ketinggian lahan. Berdasarkan hasil skor, Jakarta Pusat memiliki kelas kerawanan banjir kategori rendah seluas 846.03 ha (17.89%), kelas kerawanan banjir sedang seluas 3886.17 ha (82.11%). Jakarta Selatan memiliki kelas kerawanan banjir rendah seluas 4920.99 ha (35.67%) dan kelas kerawanan banjir sedang seluas 8874.90 ha (64.33%)., Indonesia is a country that often experiences natural disasters such as floods. Floods can have an impact on social, economic life, material damage, environmental damage, and damage to cultural heritage. Flood countermeasures need to be done to reduce the adverse effects of flooding. Flood countermeasures can be done by mapping flood-prone areas using Geographic Information System (GIS) of ArcGIS software. The research method is a descriptive method consisting of data collection, initial data processing, and advanced data. The process of identifying flood-prone areas uses the scoring method. Factors that influence vulnerability are rainfall, slope, river and land cover, soil type, and land elevation. From scoring result, Central Jakarta has a moderate category of flood hazard category was 846.03 ha (17.89%), and a high flood hazard class was 3886.17 ha (82.11%) . South Jakart has a low flood hazard class in South Jakarta is 4920.99 ha (35.67%), and the moderate flood hazard class is 8874.90 ha (64.33%).
Keyword: Arcgis, food prone, GIS, overlay

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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