Online Analytical Processing System for Hotspot Data Analysis

This paper was not presented at the conference.

Abstract

This paper was not presented at the conference.

Forests have an important role as contributors of oxygen for living creatures. Indonesia's forests are one of the largest forests in the world. However, forest and land fires often strike and become an endemic crisis that must be paid attention to by the government and society. Therefore, prevention efforts are needed to anticipate the early occurrence of forest and land fires, one of which is monitoring hotspots in an area. The Ministry of Environment and Forestry, through the Directorate General of Climate Change Control, developed a daily hotspot monitoring system called SiPongi. However, the system cannot yet display data summaries and hotspot trends up to the village level, and there is no visualization in the form of graphs. This study aims to build an OLAP system for web-based hotspot data. The prototyping method used to build an OLAP system is able to understand and identify user needs in analyzing hotspot data. This study successfully developed an OLAP system for hotspot data using the Go Fiber framework with the xdbsoft/olap library for the backend and Angular for the front end. The system is capable of performing OLAP operations such as drill down, roll up, slice, and dice and is able to visualize hotspot data in the form of tables and bar graphs. Testing was carried out using the black box method. There are two tests that are compared to ensure whether the system is running properly. The first is a scenario test of system functionality by carrying out OLAP operations. The second test is an OLAP scenario test that directly queries the database. Both tests produce the same value in carrying out OLAP operations. Therefore, the test results show that the system that has been built is able to run well according to user needs.