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Erma Suryani
Researcher at Sepuluh Nopember Institute of Technology
Publications - 146
Citations - 698
Erma Suryani is an academic researcher from Sepuluh Nopember Institute of Technology. The author has contributed to research in topics: Computer science & Population. The author has an hindex of 8, co-authored 110 publications receiving 513 citations. Previous affiliations of Erma Suryani include Andalas University & National Taiwan University of Science and Technology.
Papers
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Journal ArticleDOI
Air passenger demand forecasting and passenger terminal capacity expansion: A system dynamics framework
TL;DR: It is found that airfare impact, level of service impact, GDP, population, number of flights per day and dwell time play an important roles in determining the air passenger volume, runway utilization and total additional area needed for passenger terminal capacity expansion.
Journal ArticleDOI
Demand scenario analysis and planned capacity expansion: A system dynamics framework
TL;DR: An approach to develop models for forecasting demand and evaluating policy scenarios related to planned capacity expansion for meeting optimistic and pessimistic future demand projections is established.
Journal ArticleDOI
Dynamic simulation model of air cargo demand forecast and terminal capacity planning
TL;DR: It was found that GDP and FDI play an important role in fostering the demand and system dynamics simulation model can provide reliable forecast and generate scenarios to test alternative assumptions and decisions.
Journal ArticleDOI
Analysis of Soybean Production and Demand to Develop Strategic Policy of Food Self Sufficiency: A System Dynamics Framework
TL;DR: Soybean production could be produced to meet the needs of soybean demand in Indonesia for 20 years by increasing expansion of land of at least 70% per year, use of seeds with a minimum production level 2.4 tons / hectare, and use of biological fertilizers which can increase seed productivity at least 125%.
Book ChapterDOI
Automatic Clustering Combining Differential Evolution Algorithm and k-Means Algorithm
TL;DR: An improved differential evolution algorithm which integrates automatic clustering based differential evolution (ACDE) algorithm and k-means algorithm and it requires no prior knowledge about number of clusters is proposed.