In Section B, one of the vulnerability dimensions identified was a dyn terjemahan - In Section B, one of the vulnerability dimensions identified was a dyn Bahasa Indonesia Bagaimana mengatakan

In Section B, one of the vulnerabil

In Section B, one of the vulnerability dimensions identified was a dynamic-systemic situation that should be
reflected in the interactions among factors, adaptations and indicators. Therefore, the interaction cannot be as in
Figure 2, but it should reflect dynamic and systemic situation as illustrated in Figure 5 below. In responding to these
community characteristics, a dynamic system analysis can be utilized to model or simplify the community dynamic
and represent systemic relationships among factors, adaptations and indicators (Sterman 2001). Moreover, in
predicting levels of vulnerability, the analysis can also run certain models (based on some scenarios of adaptation) to
produce various future vulnerability levels.







Since there are then some predicted levels for future vulnerability, comparison among them responds to the fifth
gap, the need for assessments to evaluate the effectiveness of adaptations. The quantitative approach in dynamic
system analysis could give a ranking system based on these comparisons. The rank will sort the future levels from
highest to the lowest. Therefore, the most effective adaptation can be distinguished from the lowest future
vulnerability level after applying certain scenarios through the modelling process. This selection process can provide
a rationale for policy-making.
The number of victims, damage losses and the period of time for recovery can be utilized to respond to the last
(sixth) gap around the need for measurable vulnerability indicators. Number of victims and damage losses indicators
can be seen as various applications of impact assessment post hazard events. Those two kinds of valuation can also
represent the vulnerability level based on the assumption of the hazards as a given variable (constant). Moreover, the
period of time is drawn from the concept of resilience (the ability of community to “bounce back” (recover) after an
event as in Mileti & Peek 2002; Paton et al. 2003 cited in Ronan & Johnston 2005). Those three kinds of
measurements can also be set as major step to prepare a community facing negative events, as suggested by Ronan
Vulnerability
Factors
(Independent
variables)
Vulnerability
Level
(Dependent
variables)
Vulnerability
Factors
(Intermediate
independent
variables)
Vulnerability
Level
(Dependent
variables)
Vulnerability
Factors
(Independent
variables)
164 Adjie Pamungkas et al. / Procedia - Social and Behavioral Sciences 135 ( 2014 ) 159 – 166
and Johnston (2005). Preparation itself can be made by taking adaptations to reduce the possibility of fatalities,
damage losses and a long period of recovery.
In Summary, some points for a proposed vulnerability research framework are set out in Table 3 below. These
points can provide a rational basis for proposing vulnerability modelling using a system dynamic analysis.






3.4. Conclusion
This paper identifiesgaps in the vulnerability literature andpresents an approach to respond to these gaps,
specifically from the perspective of improving systematic assessment processes. Since the vulnerability concept
draws from a range of disciplines and there are diverse definitions, the dimensions of vulnerability were clarified
first, then utilized as one of the criteria for analysing the gaps in the literature. A wide range of literature within and
beyond vulnerability was then reviewed, particularly that which engages with concepts of resilience, adaptation and
community in the context of vulnerability to disasters. The major gaps identified in the literature provide a basis for
framing a future research agenda.
Based on these gaps, the following three main areasare proposed for future research in vulnerability modelling:
.. The modelling should consider all community layers (individual, groups of people and social networks)
and shouldfocus on community case studies where vulnerability dimensions can be characterised at the
community scale. It is a reflectionof vulnerability dimensions.
.. The context specificdimension of vulnerability modelling outlined in the first point is particularly important
for selecting relevant factors and identifying interactions among them. The selection process should reflect
the layers of community and be context specific in terms of hazard type, while the interaction should reflect
Adjie Pamungkas et al. / Procedia - Social and Behavioral Sciences 135 ( 2014 ) 159 – 166 165
the dynamic and systemic nature of the community. The end result of modelling should go beyond
assessment of existing vulnerability levelsto develop predictive capacity. This requiresa capacity to
evaluate scenarios of adaptation to provide a predictive tool for reducing the level of future vulnerability.
.. In responding to the last group of gaps on further developing vulnerability research, a dynamic system
analysis can accommodate the issues raised in this group as well as the first and second points above. A
quantitative evaluation process using dynamic system analysis can simulate several adaptation scenarios
through a modelling process. By comparing the output of vulnerability modelling (future vulnerability
levels) for the different adaptation scenarios the most effective adaptation scenario to reduce future
vulnerability can be determined.
Acknowledgments: This article is part of PhD materials by Adjie Pamungkas on Finding a Framework of
Vulnerability Assessment and Modelling for Disaster Risk Management, conducted at RMIT University, Australia.
The author expresses his gratitude to Dr. David Mitchell, Prof. John Handmer and Dr. Joshua Whittaker from Centre
for Risk and Community Safety –RMIT University. Some of the materials have also been presented in 4th Annual
international Workshop and Expo on Sumatra Tsunami Disaster and Recovery, 2009 on 23-25 November 2009.
0/5000
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Ke: -
Hasil (Bahasa Indonesia) 1: [Salinan]
Disalin!
In Section B, one of the vulnerability dimensions identified was a dynamic-systemic situation that should be
reflected in the interactions among factors, adaptations and indicators. Therefore, the interaction cannot be as in
Figure 2, but it should reflect dynamic and systemic situation as illustrated in Figure 5 below. In responding to these
community characteristics, a dynamic system analysis can be utilized to model or simplify the community dynamic
and represent systemic relationships among factors, adaptations and indicators (Sterman 2001). Moreover, in
predicting levels of vulnerability, the analysis can also run certain models (based on some scenarios of adaptation) to
produce various future vulnerability levels.







Since there are then some predicted levels for future vulnerability, comparison among them responds to the fifth
gap, the need for assessments to evaluate the effectiveness of adaptations. The quantitative approach in dynamic
system analysis could give a ranking system based on these comparisons. The rank will sort the future levels from
highest to the lowest. Therefore, the most effective adaptation can be distinguished from the lowest future
vulnerability level after applying certain scenarios through the modelling process. This selection process can provide
a rationale for policy-making.
The number of victims, damage losses and the period of time for recovery can be utilized to respond to the last
(sixth) gap around the need for measurable vulnerability indicators. Number of victims and damage losses indicators
can be seen as various applications of impact assessment post hazard events. Those two kinds of valuation can also
represent the vulnerability level based on the assumption of the hazards as a given variable (constant). Moreover, the
period of time is drawn from the concept of resilience (the ability of community to “bounce back” (recover) after an
event as in Mileti & Peek 2002; Paton et al. 2003 cited in Ronan & Johnston 2005). Those three kinds of
measurements can also be set as major step to prepare a community facing negative events, as suggested by Ronan
Vulnerability
Factors
(Independent
variables)
Vulnerability
Level
(Dependent
variables)
Vulnerability
Factors
(Intermediate
independent
variables)
Vulnerability
Level
(Dependent
variables)
Vulnerability
Factors
(Independent
variables)
164 Adjie Pamungkas et al. / Procedia - Social and Behavioral Sciences 135 ( 2014 ) 159 – 166
and Johnston (2005). Preparation itself can be made by taking adaptations to reduce the possibility of fatalities,
damage losses and a long period of recovery.
In Summary, some points for a proposed vulnerability research framework are set out in Table 3 below. These
points can provide a rational basis for proposing vulnerability modelling using a system dynamic analysis.






3.4. Conclusion
This paper identifiesgaps in the vulnerability literature andpresents an approach to respond to these gaps,
specifically from the perspective of improving systematic assessment processes. Since the vulnerability concept
draws from a range of disciplines and there are diverse definitions, the dimensions of vulnerability were clarified
first, then utilized as one of the criteria for analysing the gaps in the literature. A wide range of literature within and
beyond vulnerability was then reviewed, particularly that which engages with concepts of resilience, adaptation and
community in the context of vulnerability to disasters. The major gaps identified in the literature provide a basis for
framing a future research agenda.
Based on these gaps, the following three main areasare proposed for future research in vulnerability modelling:
.. The modelling should consider all community layers (individual, groups of people and social networks)
and shouldfocus on community case studies where vulnerability dimensions can be characterised at the
community scale. It is a reflectionof vulnerability dimensions.
.. The context specificdimension of vulnerability modelling outlined in the first point is particularly important
for selecting relevant factors and identifying interactions among them. The selection process should reflect
the layers of community and be context specific in terms of hazard type, while the interaction should reflect
Adjie Pamungkas et al. / Procedia - Social and Behavioral Sciences 135 ( 2014 ) 159 – 166 165
the dynamic and systemic nature of the community. The end result of modelling should go beyond
assessment of existing vulnerability levelsto develop predictive capacity. This requiresa capacity to
evaluate scenarios of adaptation to provide a predictive tool for reducing the level of future vulnerability.
.. In responding to the last group of gaps on further developing vulnerability research, a dynamic system
analysis can accommodate the issues raised in this group as well as the first and second points above. A
quantitative evaluation process using dynamic system analysis can simulate several adaptation scenarios
through a modelling process. By comparing the output of vulnerability modelling (future vulnerability
levels) for the different adaptation scenarios the most effective adaptation scenario to reduce future
vulnerability can be determined.
Acknowledgments: This article is part of PhD materials by Adjie Pamungkas on Finding a Framework of
Vulnerability Assessment and Modelling for Disaster Risk Management, conducted at RMIT University, Australia.
The author expresses his gratitude to Dr. David Mitchell, Prof. John Handmer and Dr. Joshua Whittaker from Centre
for Risk and Community Safety –RMIT University. Some of the materials have also been presented in 4th Annual
international Workshop and Expo on Sumatra Tsunami Disaster and Recovery, 2009 on 23-25 November 2009.
Sedang diterjemahkan, harap tunggu..
Hasil (Bahasa Indonesia) 2:[Salinan]
Disalin!
Dalam Bagian B, salah satu dimensi kerentanan diidentifikasi adalah situasi yang dinamis-sistemik yang harus
tercermin dalam interaksi antara faktor-faktor, adaptasi dan indikator. Oleh karena itu, interaksi tidak bisa seperti pada
Gambar 2, tetapi harus mencerminkan situasi yang dinamis dan sistemik seperti yang diilustrasikan pada Gambar 5 di bawah ini. Dalam menanggapi ini
karakteristik masyarakat, analisis sistem dinamis dapat digunakan untuk model atau menyederhanakan masyarakat yang dinamis
dan mewakili hubungan antara faktor-faktor sistemik, adaptasi dan indikator (Sterman 2001). Selain itu, dalam
memprediksi tingkat kerentanan, analisis dapat juga menjalankan model-model tertentu (berdasarkan beberapa skenario adaptasi) untuk
menghasilkan berbagai tingkat kerentanan di masa depan. Karena ada maka beberapa tingkat diprediksi untuk kerentanan di masa depan, perbandingan antara mereka merespon kelima gap, kebutuhan untuk penilaian untuk mengevaluasi efektivitas adaptasi. Pendekatan kuantitatif dalam dinamika analisis sistem dapat memberikan sistem peringkat berdasarkan perbandingan ini. Peringkat tersebut akan mengurutkan tingkat masa depan dari yang tertinggi sampai yang terendah. Oleh karena itu, adaptasi yang paling efektif dapat dibedakan dari yang terendah ke depan tingkat kerentanan setelah menerapkan skenario tertentu melalui proses modeling. Proses seleksi ini dapat memberikan alasan untuk pembuatan kebijakan. Jumlah korban, kerugian kerusakan dan jangka waktu untuk pemulihan dapat dimanfaatkan untuk menanggapi terakhir (keenam) gap sekitar kebutuhan indikator kerentanan terukur. Jumlah korban dan kerugian kerusakan indikator dapat dilihat sebagai berbagai aplikasi penilaian dampak peristiwa pasca bahaya. Kedua jenis penilaian juga dapat mewakili tingkat kerentanan berdasarkan asumsi bahaya sebagai variabel tertentu (konstan). Selain itu, jangka waktu diambil dari konsep ketahanan (kemampuan masyarakat untuk "bangkit kembali" (sembuh) setelah peristiwa seperti di Mileti & Peek 2002;. Paton et al 2003 dikutip dalam Ronan & Johnston 2005). Ketiga jenis pengukuran juga dapat diatur sebagai langkah besar untuk menyiapkan masyarakat menghadapi peristiwa negatif, seperti yang disarankan oleh Adjie Pamungkas dkk. / Procedia - Sosial dan Ilmu Perilaku 135 (2014) 159 - 166 dan Johnston (2005). Persiapan itu sendiri dapat dibuat dengan mengambil adaptasi untuk mengurangi kemungkinan kematian, kerugian kerusakan dan periode panjang pemulihan. Dalam ringkasan, beberapa poin untuk kerangka penelitian kerentanan yang diusulkan diatur dalam Tabel 3 di bawah ini. Ini poin dapat memberikan dasar yang rasional untuk mengusulkan model kerentanan menggunakan sistem analisis dinamik. 3.4. Kesimpulan Makalah ini identifiesgaps dalam literatur kerentanan andpresents pendekatan untuk menanggapi kesenjangan ini, khususnya dari perspektif meningkatkan proses penilaian yang sistematis. Karena konsep kerentanan menarik dari berbagai disiplin ilmu dan ada definisi yang beragam, dimensi kerentanan diklarifikasi terlebih dahulu, kemudian digunakan sebagai salah satu kriteria untuk menganalisis kesenjangan dalam literatur. Berbagai literatur dalam dan luar kerentanan kemudian ditinjau, terutama yang terlibat dengan konsep ketahanan, adaptasi dan masyarakat dalam konteks kerentanan terhadap bencana. Kesenjangan utama yang diidentifikasi dalam literatur memberikan dasar untuk . membingkai agenda penelitian mendatang Berdasarkan kesenjangan tersebut, tiga areasare utama berikut diusulkan untuk penelitian masa depan dalam pemodelan kerentanan: .. pemodelan harus mempertimbangkan semua lapisan masyarakat (individu, kelompok orang dan jaringan sosial) dan shouldfocus pada studi kasus masyarakat di mana dimensi kerentanan dapat dicirikan pada skala komunitas. Ini adalah dimensi reflectionof kerentanan. .. Konteks specificdimension pemodelan kerentanan yang digariskan dalam poin pertama sangat penting untuk memilih faktor yang relevan dan mengidentifikasi interaksi di antara mereka. Proses seleksi harus mencerminkan lapisan masyarakat dan menjadi konteks tertentu dalam hal jenis bahaya, sementara interaksi harus mencerminkan Adjie Pamungkas dkk. / Procedia - Sosial dan Ilmu Perilaku 135 (2014) 159 - 166 165 sifat dinamis dan sistemik masyarakat. Hasil akhir dari pemodelan harus melampaui penilaian kerentanan yang ada levelsto mengembangkan kemampuan prediktif. Kapasitas requiresa ini untuk mengevaluasi skenario adaptasi untuk menyediakan alat prediktif untuk mengurangi tingkat kerentanan di masa depan. .. Dalam menanggapi kelompok terakhir kesenjangan pada pengembangan penelitian kerentanan, sistem dinamis analisis dapat mengakomodasi isu yang diangkat dalam kelompok ini sebagai serta pertama dan kedua poin di atas. Sebuah proses evaluasi kuantitatif dengan menggunakan analisis sistem dinamik dapat mensimulasikan beberapa skenario adaptasi melalui proses modeling. Dengan membandingkan output dari model kerentanan (kerentanan masa depan tingkat) untuk skenario adaptasi yang berbeda skenario adaptasi yang paling efektif untuk mengurangi masa depan kerentanan dapat ditentukan. Ucapan Terima Kasih: Artikel ini adalah bagian dari bahan PhD oleh Adjie Pamungkas pada Menemukan Rangka Penilaian Kerentanan dan Pemodelan Manajemen Risiko Bencana, yang dilakukan di RMIT University, Australia. Penulis mengungkapkan rasa terima kasihnya kepada Dr. David Mitchell, Prof. John Handmer dan Dr Joshua Whittaker dari Pusat untuk Risiko dan Keselamatan Masyarakat -RMIT University. Beberapa bahan juga telah disajikan dalam 4 Tahunan Lokakarya internasional dan Expo di Sumatera Tsunami Bencana dan Pemulihan, 2009 pada November 23-25 ​​2009.

















































































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