Model Kapasitas Masyarakat Dalam Menghadapi Bencana Menggunakan Analisis Regresi Logistik Ordinal

Indonesia secara geografis merupakan sebuah negara yang memiliki potensi bencana alam yang tinggi untuk berbagai jenis bencana seperti banjir, gempa, tanah longsor, kekeringan dan gunung berapi. Pengurangan resiko bencana sebuah daerah dapat dilakukan dengan meningkatkan kapasitas pemerintah dan masyarakat dalam melakukan mitigasi bencana. Pada makalah ini dibahas penyusunan model kapasitas masyarakat dalam menghadapi bencana menggunakan analisis regresi logistik ordinal. Model regresi disusun dengan menggunakan tiga variabel dependen yaitu (i) pengetahuan umum yang dimiliki tentang pengurangan resiko bencana alam yang disimbolkan dengan Y1 (ii) pengetahuan umum yang dimiliki tentang bagaimana menyelamatkan keluarga ketika terjadi bencana alam yang disimbolkan dengan Y2 (iii) upaya peningkatan kewaspadaan warga menghadapi bencana alam oleh pihak terkait disimbolkan dengan Y3. Variabel dependen Y1 dan Y2 dipengaruhi oleh Faktor Pengetahuan dan Faktor Rencana Aksi. Sedangkan variabel dependen Y3 dipengaruhi oleh Faktor kepemimpinan dan program, dan Faktor Fasilitas

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Model of Community Capacity in Facing Disaster Using Ordinal Logistic Regression Analysis

Indonesia is geographically a country with potential natural disasters which is high for various types of disasters such as floods, earthquakes, landslides, drought and volcanoes. A regional disaster risk reduction can be done with increase the capacity of government and communities in disaster mitigation. On this paper discusses the formulation of community capacity models in the face of disaster using ordinal logistic regression analysis. The regression model was prepared using three dependent variables are (i) general knowledge of risk reduction natural disaster symbolized by Y1 (ii) general knowledge possessed about how to save the family when a natural disaster is symbolized by Y2 (iii) efforts to increase citizens' awareness of natural disasters by related parties symbolized by Y3. The dependent variables Y1 and Y2 are influenced by the Knowledge Factor and Factor Plan of Action. While the dependent variable Y3 is influenced by Factor leadership and programs, and Facility Factors

MANAJEMEN KEUANGAN INTERNASIONAL

Tren globalisasi bisnis telah terdokumentasi dengan baik yang terlihat dalam laporan-laporan keuangan dari korporasi-korporasi besar dan kecil. Tren ke arah globalisasi tersebut didorong oleh penurunan hambatan batas negara. Oleh sebab itu pemahaman atas manajemen keuangan internasional menjadi sangat penting bagi keberhasilan perusahaan. Pemahaman dan pengertian yang berkaitan dengan pentingnya pasar keuangan internasional, penentu exchange rate, investasi asing langsung, penganggaran modal multinasional, pendanaan jangka panjang bagi perusahaan multinasional, analisa risiko negara serta pendanaan perdagangan internasional. Manajemen Keuangan Internasional merupakan suatu perencanaan, pengorganisasian, dan pengendalian keuangan pada suatu Perusahaan Multinasional (Multinational Corporation yang sering disebut MNC) yang merupakan perusahaan yang beroperasi di seluruh dunia. Mereka adalah perusahaan besar yang dimiliki oleh kaum kapitalis Global yang pasarnya di Kanada, USA, Jepang, Jerman, Italia, Perancis dan Inggris. Perusahaan-perusahaan tersebut disebut konglomerat Global atau kapitalis Global yang ingin menguasai ekonomi dunia dan ekonomi negara-negara sedang berkembang dengan tujuan utama adalah mencari keuntungan. Perusahaan internasional berarti perusahaan yang beroperasi lebih dari satu negara. Perusahaan tersebut telah melakukan transaksi transnasional yang melewati batas-batas negara secara geografis. Pada masa ini, pertumbuhan perusahaan multinasional disebabkan karena perkembangan teknologi komunikasi dan transportasi. Perusahaan multinasional ini dipengaruhi oleh sosial, politik, dan ekonomi dunia. Modal mengalir begitu cepat dari berbagai negara ke berbagai negara. Perusahaan yang mampu mengembangkan usahanya di tingkat multinasional akan mampu menunjang keunggulan komperatif yang lebih tinggi dibandingkan perusahaan yang beroperasi dalam satu negara

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press