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Computational Cost of Learning Vector Quantization Algorithm for Malaria Parasite Classification in Realtime Test

Iis Hamsir Ayub Wahab, Adhi Susanto, P. Insap Santosa, Maesadji Tjokronegoro


Analysis and interpretation of malaria parasite images were performed in which one of them was to obtain the parasite image patterns, thus it could be conducted classification process towards image based on its pattern. The parasite image pattern is different between one and another, this depends on parasite type. Differentiating between one image and another needs a feature for each pattern. This study, therefore, aimed to analyze and evaluate algorithms of learning vector quantization neural network for malaria parasite pattern recognition test in real time. The result of this study showed that the LVQ network classification method could recognize 92% object,. Algorithm time complexity for LVQ is O(n).


LVQ, Parasite, Realtime, Big (O)

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