Rustam

Rustam, Agus Yodi Gunawan, Made Tri Ari Penia Kresnowati

This paper examines the use of artificial neural network approach in identifying the origin of clove buds based on metabolites composition. Generally, large data sets are critical for accurate identification. Machine learning with large data sets lead to precise identification based on origins. However, clove buds uses small data sets due to lack of metabolites composition and their high cost of extraction. The results show that backpropagation and resilient propagation with one and two hidden layers identifies clove buds origin accurately. The backpropagation with one hidden layer offers 99.91% and 99.47% for training and testing data sets, respectively. The resilient propagation with two hidden layers offers 99.96% and 97.89% accuracy for training and testing data sets, respectively.

Rustam, Koredianto Usman, Mudyawati Kamaruddin et al.

Possibilistic fuzzy c-means (PFCM) algorithm is a reliable algorithm has been proposed to deal the weakness of two popular algorithms for clustering, fuzzy c-means (FCM) and possibilistic c-means (PCM). PFCM algorithm deals with the weaknesses of FCM in handling noise sensitivity and the weaknesses of PCM in the case of coincidence clusters. However, the PFCM algorithm can be only applied to cluster complete data sets. Therefore, in this study, we propose a modification of the PFCM algorithm that can be applied to incomplete data sets clustering. We modified the PFCM algorithm to OCSPFCM and NPSPFCM algorithms and measured performance on three things: 1) accuracy percentage, 2) a number of iterations to termination, and 3) centroid errors. Based on the results that both algorithms have the potential for clustering incomplete data sets. However, the performance of the NPSPFCM algorithm is better than the OCSPFCM algorithm for clustering incomplete data sets.