Parkinson’s disease (PD) is a progressive neurodegenerative disorder. Autism spectrum disorder (ASD) neurodevelopment Clinical decision-making process complex. Due to complex nature of sign and its symptoms clinical decision making may lead misclassification. To deal with such medical problems methods or approaches soft computing play an important role. This paper will focus on presenting integ...

The techniques to recycle and reuse plastics attract public attention. These public attraction and needs result in improving the recycling technique. However, the identification technique for black plastic wastes still have big problem that the spectrum extracted from near infrared radiation spectroscopy is not clear and is contaminated by noise. To overcome this problem, we apply Raman spectro...

This paper discusses the prediction of inflation rate in Indonesia. The data used this research is assumed to have both linear and non-linear components. ARIMA model selected accommodate component, while ANFIS method accounts for component data. Thus, known as hybrid ARIMA-ANFIS model. clustering performed using Fuzzy C-Mean (FMS) with a Gaussian membership function. Consider 2 6 clusters. opti...

Firefly algorithm is a swarm-based algorithm that can be used for solving optimization problems. In this paper, we focus on image clustering algorithm using the fuzzy set of possible solution is incorporated into the original firefly to improve the performance. The movement of the firefly still follows the original pattern but they are updated according fuzzy c-means algorithm. All method, k-me...

Nonnegative matrix factorization method is a kind of new matrix rotting method. It is an effective tool for large data processing and analysis. At the same time, NMF has an important performance on in intellectual information processing and pattern recognition. We then aim for increasing an efficiency and accuracy of data clustering and classification based on NMF. NMF method is used to reduce ...

A quality of centroid-based clustering is highly dependent on initialization. In the article we propose initialization based on the probability of finding objects, which could represent individual clusters. We present results of experiments which compare the quality of clustering obtained by k-means algorithm and by selected methods for fuzzy clustering: FCM (fuzzy c-means), PCA (possibilistic ...