APPLICATION OF K-MEANS CLUSTERING METHOD AND DIJKSTRA ALGORITHM TO OPTIMIZE THE DISTRIBUTION OF POST- DISASTER HUMANITARIAN LOGISTICS: CIANJUR EARTHQUAKE CASE STUDY 2022

• Main Author: Dinnurrahman, Jihad
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/83773
• Institution: Institut Teknologi Bandung

Indonesia is a disaster-prone country, especially with earthquakes and volcanic eruptions. This is because geographically, Indonesia is passed by the Pacific Ring of Fire and three active plates in the world. Based on data from the National Disaster Management Agency (BNPB) in 2014-2023, Indonesia has experienced around 35 thousand natural disaster events, with an average of 3.5 thousand events per year. One of the natural disasters that occurred and had a considerable impact was the earthquake in Cianjur, on November 21, 2022. The effect of this disaster included material losses and casualties. A total of 114,000 people had to evacuate to 278 evacuation centers spread across five sub-districts in Cianjur. The large number of evacuation points creates various problems in the management of the distribution of disaster logistics assistance. Therefore, it is necessary to optimize the distribution of logistical assistance in handling the aftermath of this disaster. This research uses the K-Means clustering method and the Dijkstra algorithm to create a more even distribution of refugees, a more strategic distribution center location, and a shorter distribution route. This can improve the efficiency and effectiveness of aid distribution, ensuring that logistical assistance can be distributed quickly and evenly to all refugees in the affected area. These results show that the K-Means clustering method and Dijkstra algorithm are more effective than the administrative distribution of aid carried out by the BNPB team.