Rizky Januar Akbar

Poverty classification using Analytic Hierarchy Process and k-means clustering

The successfulness of poverty alleviation programs depends on the accuracy of poverty data. The government needs to collect poverty data and analyze them to determine which poverty alleviation programs should be delivered to. A data collection process is often done by conducting a survey that consists of 14 survey variables. However, raw data collected from surveys are not useful if they are presented as is. These survey data need to be processed further to support decision making. This paper presents a method to process survey data into categories using Analytic Hierarchy Process (AHP) and k-means clustering method. The categories consist of three poverty levels, such as near poor, poor, and very poor. We also present a workflow of survey and a implementation of this method to collect and process poverty data.

Spline and color representation for batik design modification

A computer based system for batik design modification using spline and color representation is proposed to create a new batik design from the existing batik design image. The system can be used to support artisan/designer works for drawing motifs and applying color composition, so the design can be seen visually on the screen without having to make a real design. To evaluate the performance of the system, five batik images are used as the input of the system. The experimental results show that the system generates the similar batik design using spline and color representation from the existing batik design image and the user can edit the spline and its color to create new batik design.