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A desirable dietary pattern (DDP) index is an index to measure the balance and variance of the nutrition intake of an individual. This index is composed of the calory values of protein, fat, and carbohydrates. Grouping individuals based on the DDP index is required to measure and improve an individual food security state. We took 14 individual purposively as samples to fill a set of DDP questioner. They were asked about their daily food consumption. They were grouped based on the DDP variables. A 3-dimensional plot showed that there were three to four clusters. Then, medoid-based partitioning algorithms, namely partitioning around medoids (PAM) and simple k-medoids (SKM), were applied in the data set. The inputted distances were generalized distance function to vary the distance options. The cluster results were then validated by medoid-based shadow value validation. This index was comparable to the 3-dimensional plot such that four clusters were opted as the most suitable number of clusters. The barplot of the cluster results showed that cluster 1 was characterized by an abundance of fat, while cluster 2 had very sufficient carbohydrates. Cluster 3 and 4 were two clusters with opposite characteristics where the former had a shortage of protein, fat, and carbohydrates, while the latter had an abundance of them.

In 2018, Indonesian government set 100 districts as the priority to reduce stunting. In this study, we hypothesize that the 100 determined districts should not be treated on an equal policy, due to some underlying factors that might affect the stunting in those districts. Thus, it is necessary to identify and analyze the grouping of 100 priority districts for stunting interventions in 2018 based on the National Team’s indicators for the Acceleration of Poverty Reduction to see the severity of stunting. It is hoped that this clustering could be a reference for the government in determining priority regency groups to reduce stunting rates. Data on 100 districts, represented by eight numerical measurements and six categorical measurements were analyzed using Partitioning Around Medoids (PAM) method. Data similarity was measured using Gower distance, which can handle the clustering of mixed data types. We identified five priority district groups which provide meaningful insights. One of the groups has the worst stunting severity condition among other groups, for each indicator; implying the high priority to follow-up by the government. The majority of districts in Papua and East Nusa Tenggara Provinces are districts with poor stunting severity. We also found that poverty, proportion of the population with defecation in latrines, access to clean water and proper sanitation, number of integrated healthcare centers (posyandu) in a village, and number of doctors in each district are important factors that explain the stunting severity.

Gestures are one of the best ways of communication between dumbs and other people using the expression of signs language. In this paper, we suggest an algorithm for recognizing hand gestures of Arabic words (اتمنى لك حیاة سعیدة-اقتباس) to by using dumb (through signs) and convert the sings into voice corresponding to sings words. The proposed algorithm for Convert Gestures of Arabic Words into Voice, record video of gesture (of the dumb person) then convert the video into frames (images), preprocessing for the resulted image must done by remove the noise, resize the images and increase the contrast, then calculate the distance to clustering the words by using (C4.5, k-mean, k- medoid and artificial neural network), calculate the distance (or features) by using Euclidean distance and slope where, there are eighteen features (eight features from Euclidean distance, eight features from slop, Area, and perimeter). The results in the training stage were (C4.5 gave 100%, k-mean gave 95.2% k-medoid gave 91.9% and ANN gave 91.27%). While in the testing stage we used three classifiers (Euclidian Distance, Modify of the Standardize Euclidian Distance and Correlation) and the results show that (Euclidian Distance gave 94.4%, Modify of the Standardize Euclidian Distance gave 100% and Correlation gave 94.4%) We create our database (three videos with 250 frames) for training and one video for testing.

As the residual value of the lead-acid batteries is not effectively evaluated in the current scraping and recycling processes of the lead-acid batteries, the partition around medoids (PAM) clustering algorithm is adopted, and the scraped lead-acid batteries are classified with the changes in the temperature and charge-discharge occurring when the lead-acid batteries are in service as the characteristic parameters. Besides, the validity of the algorithm is validated using 300 lead-acid battery packs to be scrapped at the communication base station, from which the results showed that the residual value of the lead-acid batteries can be effectively distinguished by the partition around medoids algorithm according to the temperature and charge-discharge characteristic parameters.

Corn plants has an important role in compliance national and international food needs after rice and wheat. According to Ir. Pending Dadih Permana, Director General of Food Infrastructure and Facilities (PSP) of the Ministry of Agriculture, East Java is one of province that the biggest corn producers in Indonesia. But it still cannot supply the increasing market needs in Indonesia. Therefore, it requires a grouping corn plants based on productivity, production, and area of land in East Java in order to increase level of procurement of corn plants in East Java. In process of grouping, it use K-Medoids algorithm. K-Medoids algorithm as known as Partitioning Around Medoids (PAM), which is variants of K-Means method. This is based on the use of medoids, not from observation of mean that owned by each cluster, with purpose reduce the sensitivity of partitions due to the extreme values in the dataset. This algorithm is an algorithm that can produce data that is not sensitive to outliers because one of object with some big value. These deviations can occur from data distribution. Based on the result of grouping corn plants based on productivity, production, and area of land which has been done, there are 3 clusters with at most 21 members, and at least 8 members. From this research, it can concluded that K-Medoids algorithm can help determine potential of the location of corn producing plants in East Java.

Principal component analysis (PCA) is an important tool in exploring data. The conventional approach to PCA leads to a solution which favours the structures with large variances. This is sensitive to outliers and could obfuscate interesting underlying structures. One of the equivalent definitions of PCA is that it seeks the subspaces that maximize the sum of squared pairwise distances between data projections. This definition opens up more flexibility in the analysis of principal components which is useful in enhancing PCA. In this paper we introduce scales into PCA by maximizing only the sum of pairwise distances between projections for pairs of datapoints with distances within a chosen interval of values [l,u]. The resulting principal component decompositions in Multiscale PCA depend on point (l,u) on the plane and for each point we define projectors onto principal components. Cluster analysis of these projectors reveals the structures in the data at various scales. Each structure is described by the eigenvectors at the medoid point of the cluster which represent the structure. We also use the distortion of projections as a criterion for choosing an appropriate scale especially for data with outliers. This method was tested on both artificial distribution of data and real data. For data with multiscale structures, the method was able to reveal the different structures of the data and also to reduce the effect of outliers in the principal component analysis., 24 pages, 22 figures