Tomato fruit is one of agroproducts that has high-economic value in the world particularly in
Indonesia. To compete in a worldwide market a tomato fruit producer must produce fresh or processed
tomato with high quality. High quality tomato products are influenced by the application of post-harvest
treatment or processing. One of the vital process in post-harvest treatment is sortation. Mannual
sortation introduces subjectivity (bias), inaccuracy, slowness and inconsistency. This needs more
intelligent sortation methods and tools that overcome the sort comings of manual process. Probabilistic
Neural Network (PNN) is one of Artificial Neural Network (ANM variants that can be to develop a
computer-based sortation engine for tomato fruits. However, to accelerate the sortation process, parallel
computation is employed allowing multiple processors to execute simultaneously the sortation process.
This research is aimed towards the implementation and testing of a parallel computation algorithm with
PNN to perform sortation for tomato fruits. Some criteria being observed and tested include accuracy,
total execution time, speedup, and efficiency compared to sequential algorithm. The experimental results
show that the application of parallel computation algorithm with PNN introduces the increase of
accuracy, total execution time, speedup, and efficiency with the same accuracy.
