Judul: Analisis Gerombol Dampak Bencana Banjir di Indonesia Menggunakan Metode Self Organizing Maps
Abstrak: Terdapat fenomena kecenderungan meningkatnya bencana alam dalam tiga dekade terakhir di Indonesia. Lebih dari 4 miliar orang terkena dampak peristiwa alam yang ekstrem pada tahun 1980-2010. Sebagai negara yang luas, penting untuk membuat pemetaan wilayah sehingga dapat meminimalisir kerugian yang ditimbulkan oleh bencana salah satunya banjir. Data dampak bencana banjir yang bersumber dari BNPB dengan 514 kabupaten/kota di Indonesia digunakan pada penelitian ini. Metode yang dapat digunakan untuk mencapai tujuan ini adalah dengan analisis gerombol, salah satunya dengan metode Self Organizing Maps (SOM). Metode SOM memiliki kelebihan yaitu merupakan perangkat visualisasi dan analisis untuk data berdimensi tinggi dan dapat menangani pencilan peubah ganda. Penggerombolan 514 kabupaten/kota di Indonesia tahun 2021 berdasarkan 10 peubah menghasilkan 7 gerombol. Gerombol 1,4 dan 5 dikategorikan sebagai gerombol yang memiliki dampak bencana banjir terbesar. Dampak bencana pada Gerombol 1 (Banjar) didominasi oleh banyaknya korban dan kerusakan fasilitas. Gerombol 1 (Banjar) didominasi oleh banyaknya korban dan kerusakan infrastruktur dampak bencana banjir. Gerombol 4 (Bandung, Indramayu, Bekasi, Cilacap, Wonogiri, Pekalongan, Kota Pekalongan, Alor, Lembata, Flores Timur) didominasi oleh banyaknya korban dan kerusakan rumah dampak bencana banjir. Gerombol 5 (Bima, Sumba Timur, Kupang, Rote Ndao, Sabu Raijua, Malaka, Kota Kupang) didominasi oleh banyaknya korban dan kerusakan infrastruktur dampak bencana banjir., There has been a trend of increasing natural disasters in Indonesia over the past three decades. More than 4 billion people were affected by extreme natural events from 1980 to 2010. As a large country, it is important to map out regions to minimize the losses caused by disasters, one of which is flooding. Data on the impact of flood disasters sourced from BNPB, covering 514 regencies/cities in Indonesia, was used in this study. A method that can be used to achieve this goal is cluster analysis, one of which is the Self-Organizing Maps (SOM) method. The SOM method has the advantage of being a visualization and analysis tool for high- dimensional data and can handle multiple variable outliers. The clustering of 514 regencies/cities in Indonesia in 2021 based on 10 variables resulted in 7 clusters. Clusters 1, 4, and 5 are categorized as clusters with the greatest impact of flood disasters. The disaster impact in Cluster 1 (Banjar) is dominated by the number of victims and damage to facilities. Cluster 1 (Banjar) is dominated by the number of victims and infrastructure damage due to flood disasters. Cluster 4 (Bandung, Indramayu, Bekasi, Cilacap, Wonogiri, Pekalongan, Pekalongan City, Alor, Lembata, East Flores) is dominated by the number of victims and house damage due to flood disasters. Cluster 5 (Bima, East Sumba, Kupang, Rote Ndao, Sabu Raijua, Malaka, Kupang City) is dominated by the number of victims and infrastructure damage due to flood disasters.
Keyword: cluster,disaster, district/city, floods, SOM

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.
Keyword: