Pasang Aksara Bot: A Balinese Script Writing Robot using Finite State Automata Transliteration Method

Bali is a world tourism destination known for its cultural riches. Balinese culture is a legacy of the world’s ritches that must be preserved. In the development of the world of education and technology today, there have been many efforts to preserve Balinese culture such as researches used to document the Balinese culture so this culture can still be maintained. One study for the preservation of Balinese culture in the field of technology is the application of Balinese script authors. Balinese script is one of the traditional Indonesian archipelago that developed on Bali. Because its use is limited to a narrow scope, so in everyday use, most Balinese script has been replaced with Latin letters. Therefore this research is focused to make conservation effort to Balinese script with robot technology. Robot is a tool that is a combination of mechanical circuits and electronics circuits. The robot that developed in this research is a robot that can write balinese script with design resembles human arm. In this research for connectivity using USB and on construction platform using Makeblock and mDraw application is a graphics program developed based on open source python. By developing the transliteration application of the script, it can be converted from .png generated into the form .svg or vector as input mDraw and give the command to the robot to write the transliteration result on a piece of paper. In this paper a new idea has been proposed to implement the robot on cultural preservation and Balinese script education. This research has developed a robotics system that can write Balinese Script. This robotic arm was successfully tested with 60% (21 glyph) categorized as very good, 37.14% (13 glyph) categorized as good, and 2.86% (1 glyph) enough. This research was developed on accuracy analysis of transliteration result and result of writing robot to paper. Through the experiment, there was an accuracy of 40.72% for the correct test count and 59.28% for the number of false tests of 1137 words on thirteen types of special words.