Searching Intermediary Closely Related Languages On Big Graph Data To Find Mediator For Conflict Resolution Between Indonesian Tribes

Fitri, Shella Eldwina (2021) Searching Intermediary Closely Related Languages On Big Graph Data To Find Mediator For Conflict Resolution Between Indonesian Tribes. Other thesis, Universitas Islam Riau.

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Abstract

Indonesia is a country with diverse ethnic and cultural backgrounds. However, this diversity sometimes creates social problems in society, such as conflicts between tribes. With such differences in tribal languages, it will be difficult for deliberations to resolve conflicts. To address these issues, we aim to find intermediary closely related languages on big graph data using the best-performing pathfinding algorithms to be able to find mediators in resolving inter-tribe conflicts in Indonesia. We analyze the performance of three pathfinding algorithms, which are Dijkstra, A*, and Yen's K algorithm by comparing execution time, geographical distance, and the total lexical distance of the intermediary languages as cost. The research findings show that even though Dijkstra and Yen's K algorithm have equal total cost and geographical distance for all cases, Yen?s K is the fastest at searching for intermediary languages that are closely related while Dijkstra is the slowest. Even though A* always has the highest total cost due to the consideration of additional information in the form of a heuristic function that is geospatial distance, the A* algorithm shows a promise by getting one case with the smallest total geographical distance between languages. Therefore, the A* has the potential to be used to select a mediator with a distance closer to the conflicting languages, while in the general case, Yen?s K algorithm can be considered.

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