Judul: Identifikasi Penyebab Banjir di Pulau Jawa
Abstrak: Bencana banjir menjadi masalah serius dan kompleks yang sering terjadi di berbagai daerah di Pulau Jawa setiap tahunnya. Oleh karena itu perlu mengkaji faktor penyebab terjadinya bencana banjir di Pulau Jawa menggunakan data remote sensing. Proses identifikasi menggunakan metode deskriptif dari data yang telah diolah menggunakan aplikasi ArcGIS. Faktor penyebab banjir yang diidentifikasi yaitu curah hujan, topografi, penggunaan lahan, daerah aliran sungai, dan jenis tanah. Curah hujan yang mendominasi di Pulau Jawa sebesar >3000 mm/tahun dengan topografi yang sering dijumpai berupa dataran rendah dan landai. Penggunaan lahan yang mendominasi berupa sawah, perubahan penggunaan lahan paling besar ada pada pemukiman yang mengalami peningkatan, jenis tanah yang sering ditemukan berupa Dystric Fluvisols. Curah hujan yang paling banyak menyebabkan banjir di tahun 2019 yaitu 1501-2000 mm/tahun dan 2501-3000 mm /tahun pada tahun 2022. Kemiringan lahan yang paling banyak mengalami banjir ada di kelas kemiringan 0-8% dengan elevasi <250 mdpl. Perubahan lahan dari tambak menjadi sawah dan perkebunan menjadi pemukiman meningkatkan resiko terjadinya banjir. Jenis tanah Pellic Vertisols yang memiliki kecepatan infiltrasi rendah menyebabkan tingginya kejadian banjir., Flooding is a serious and complex problem that often occurs in various regions on the island of Java every year. Therefore, it is necessary to assess the causes of flooding in Java using remote sensing data. The identification process uses a descriptive method of data that has been processed using ArcGIS applications. The factors that cause flooding identified are rainfall, topography, land use, watershed, and soil type. Rainfall that dominates in Java is >3000 mm / year with topography that is often found in the form of lowlands and ramps. Land use dominates in the form of rice fields, the largest land use change is in settlements that have increased, the type of soil that is often found in the form of Dystric Fluvisols. The rainfall that caused the most flooding in 2019 was 1501-2000 mm/year and 2501-3000 mm/year in 2022. The most flooded land slopes are in slope class 0-8% with elevation <250 MASL. Changes in land from ponds to rice fields and plantations to settlements increase the risk of flooding. The type of Pellic Vertisols soil that has a low infiltration rate causes high flood events.
Keyword: Causes of Floods, Floods, Java Island

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: Pemurnian Rekombinan Eritropoietin Manusia (rhEPO) Menggunakan Kromatografi Afinitas Ni-NTA dan Co-Talon
Abstrak: Suitable and efficient techniques for purification of recombinant human erythropoietin (rhEPO) are required to obtain pure rhEPO which can be continued to preclinical trials step. One of these techniques is immobilized metal affinity chromatography (IMAC), which has advantages such as high selectivity, easy to elute, and simple procedure for column regeneration. At least, there are 2 kinds of commercial IMAC resin using nitriloacetic acid (NTA) matrix as chelating agent, namely nickel-based (Ni-NTA) and cobalt-based IMAC resin (Co-NTA), the latter is well known as Talon. This research studied the influence of metal ions Ni2+ and Co2+ in rhEPO purification. The results indicated that Ni- NTA had higher affinity in binding rhEPO protein compared with Co-Talon. Average total protein by Ni-NTA was 226.72 μg/mL and Co-Talon was 60.52 μg/mL, but the protein purity with Co-Talon was better than Ni-NTA. Base on protein yield that can be separated, Ni-NTA is more suitable to obtain high yield of rhEPO protein.
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