FORECASTING DATA TRAFFIC DEMAND FOR RURAL AREAS USING MULTI-DIMENSIONAL PARAMETERS - Annisa Sarah (Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta, 2019)
Deploying network in rural is not attractive for commercial based broadband network providers. However, it is important to provide internet access for villagers since internet may give positive impacts on social development. A failure to forecast traffic demand might lead to a sloppy network design which is inefficient and costly. The mobile traffic demand forecast method needs wider perspectives other than technical records of network providers only. This research introduces a multidimensional model to predict data traffic demand in the future, by combining government spatial planning, demography statistics, and network records. Two different areas were studied and analyzed: Panimbang and Leuwidamar districs, in Banten Province, Indonesia. The main parameter to compare single- and multi-dimensions model is the areal traffic demand. For Panimbang, the areal traffic demand for multi-dimensions model has 24 Mbps/km2 higher, compared to the single-dimension model. For Leuwidamar, the demand for multi-dimension is 10 Mbps/km2 lower, compared to the single-dimension. In case of Panimbang, a pessimistic forecasting might not be a big problem since adding cells in a dense area is not costly, however for the case of Leuwidamar, multi-dimensions models could help to design a more efficient network since the single-dimension model is too optimistic and leads to a high capital investment for providers. This multidimensional model suits best for remote and sparsely distributed users (Leuwidamar). However, it might give no high impact for residential or urban areas (Panimbang).