Highway Roughness Index Forecasting with Combination Grey Forecasting Model and Similarity Spatial Data (Case Research : Pondok Aren - Serpong Highways)
Ridwan Nurhadiansyah
Master of Information System
Universitas Komputer Indonesia
Abstract
The index of road roughness or IRI is one of the main parameters to clasify the road service conditions, age, and kind of handling needed. As the main supporting factor of safety and comfortness for highway users, the Government state of PERMEN PU No. 16/PRT/M/2014 requires that each highway must be measured by IRI annually, with determinate max value 4 m/km (unit of measurement). The purpose of this research is IRI forecasting on the Pondok Aren Serpong highways uses limited data history, testing results in 2013, 2015, 2016 and 2017 with Grey Forecasting Model (GM). In case of unavailability of testing results in 2014 that might cause an inaccuracy of forecasting results. Despite of that, this research using the application of similarity spatial data form IRI testing result in 2018 on Pondok Aren Ulujamis highway. The final result of tis research are to discovering the effect of combining GM techniques and similarity spatial data on the accuracy of forecasting.
Keywords: Grey Forecasting Model, Similarity Spatial Data, Roughness Index Forecasting, Highway
Topic: Informatic and Information System