@article{MAKHILLJEAS2017122315263,
    title = {Sustainable Reclamation Model: Study Case of Makassar Waterfront Area},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {23},
    pages = {7286-7292},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.7286.7292},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.7286.7292},
    author = {Andi,Slamet and},
    keywords = {Reclamation,structural equation model,reclamation sustainability index,relationship,influential,infrastructure},
    abstract = {Although, many studies have been done using index sustainability research for developing
sustainability index, especially for reclamation area is still lacking. Similarly, a model of sustainable waterfront
area has been studied by many scholars, yet a model of sustainable improvement area is also still lacking. The
aim of this study is to develop a model of sustainable improvement using Structural Equation Model (SEM).
The SEM is used to analyze the relationship between indices which contain three indicators from physical
aspects of sustainable development. The indicators are waterfront resource, building and coastal infrastructure.
The study provides a novel method for evaluating the sustainability of reclamation using Reclamation
Sustainability Index (RSI) by Analysis of Moment Program Structures (AMOS). The research approach is
quantitative and the survey is conducted to obtain quantitative data from questionnaires to find information
and perceptions of the community about sustainable reclamation. The study area is in Makassar Waterfront
City, Indonesia. SEM used with some steps such as model specification, identification, estimation and eligibility
test. It found out that the most influential variables in observed are open space, water conservation, protected
area, density, the condition of main road and distance from the residential area. The model will be a useful tool
to evaluate and monitor the reclamation development. The information generated will be used to predict urban
coastal zone scenarios for sustainable development.}
    }