Development and Optimization of NoSQL Database in Food Insecurity Early Warning System Based on Local Community Participation
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Development and Optimization of NoSQL Database in Food Insecurity Early Warning System Based on Local Community Participation

Yani Nurhadryani, Wiradani Ramadhan, Auzi Asfarian


As a part of the food insecurity early warning system based on local participation, a robust and scalable database service is required. This necessity caused by the large area of services which include 34 provinces, 416 districts, 7,215 sub-districts and 80,534 villages in Indonesia. The abundant number of the expected daily transaction might not be handled properly using the traditional model. In this research, we design, implement, and optimize the NoSQL database to create scalable, dynamic, and flexible database service for the early warning system. The cohesion of the model is then measured, resulting in 5 entities with high cohesion, 16 with moderate cohesion, and 3 with low cohesion. After refactoring, we reduced the number of the low-cohesion entity into one and increased the average cohesion from 0.62 to 0.67. An empirical experiment was conducted to compare the response time before and after the refactoring. As the results, the average response time is decreased from 11.0 ms to 7.99 ms or equal to 1.38 in speedup. The experiment results suggest there is an impact of the logical data model improvement, by increasing their cohesion, to the performance of the NoSQL database.


Database Design, Database Optimization, NoSQL, Early Warning System, Information System

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