Kopyor coconut is a coconut that has genetic abnormalities which cause the coconut meat to have a unique texture and is detached from the coconut shell. Its uniqueness attracts many enthusiasts resulting in a high economic value, 4-5 times that of the ordinary coconut. From its external appearance, kopyor coconut does not differ with ordinary coconut and this poses a challenge in the detection stage. To date, both farmers and sellers use a traditional approach by listening to the sound of whisk from kopyor coconut to detect them. Unfortunately, this approach relies heavily on experience and expertise of the person. Therefore, a new detection approach is proposed based on sound recognition using Mel Frequency Cepstrum Coefficient (MFCC) as the method for feature extraction and Dynamic Time Warping (DTW) as the method for feature matching. Objects that will be detected are kopyor coconuts and ordinary coconut which has grown mature. By implementing both methods, a program has been developed to detect kopyor coconut with an accuracy of 93.8%.
In a training activity, a strategy is needed to form groups and assess the best participants. Also like the Student Organization Basic Training (LDOK) at Satya Negara Indonesia University or elsewhere, good group division will increase the competitiveness of each group. Personality theory and the K-Mean method can provide options for forming training groups with equal distribution of participants' characters. With a balanced competition, it is hoped that the scores achieved by the participants can be optimal in each group. Selection of the best participants using the Analytical Hierarchy Process (AHP) method can be used for this purpose by determining the level of importance of each activity being assessed. The results of this study indicate that these two methods can be used as an option by the implementing committee to optimize the results of the training activities .
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