The purpose of this research is to see how much open unemployment rate according to the highest education completed in the country of Indonesia for subsequent years through predictions used on the basis of existing data, which later as input for the government so that the government can make better policies to suppress the unemployment rate. This research uses artificial neural network application using a combination of Levenberg-Marquardt Algorithm with bipolar sigmoid function. Open unemployment data according to the highest education is sourced from the National Labor Force Survey of the Republic of Indonesia, 2013-2017 in each semester. The data processing consists of two stages where the first phase of pattern recognition and the second stage is predicted. Pattern recognition and prediction use different data from the same process that uses data training and data testing. Data Training year 2013-2015 with a target of 2016, while data testing year 2014-2016 with the target year 2017. Architectural model used there are five, among others 6-2-5-2, 6-5-6-2, 6- 5-8-2, 6-5-10-2 and 6-8-12-2. From the 5 models, it can be concluded that the best model is 6-5-10-2 with the epoch of 13 iterations, MSE in February 0.0109696004, MSE in August 0.0233797200. While the accuracy rate in February and August is the same, that is equal to 88%.

Diarrhea is a condition in which a person defecates in a runny or liquid form and occurs repeatedly. Diarrhea can cause the loss of large amounts of water and substances needed by the body. Diarrhea is one of the health problems in developing countries, especially in Indonesia both in urban and rural areas. Diarrhea morbidity rates around 200-400 occurrences among 1000 residents each year. Thus in Indonesia there can be around 60 million incidents each year, most (70-80%) of these sufferers are children under five years (BALITA). Some patients (1- 2%) will fall into dehydration and if not immediately helped 50-60% of them can die. This group experiences more than one incidence of diarrhea every year. K-Medoids Algorithm Clustering is one of the algorithms used for to group data.Keywords: Data Mining, K-Medoids, Clustering, Diarrhea

Based on data on the results of oil palm production in PTPN IV Marihat displays several locations with fruit yields that vary in number. For this reason, grouping of potential fruit-producing locations is needed to know which locations produce large or small numbers of palm fruit. The production sharing is usually done based on the location or block of harvesting oil palm fruit. Therefore, a method is needed to facilitate the grouping of fruit producing locations. With the K-Means clustering approach, the division of location groups can be done based on harvested area (Ha), production realization (kg) and harvest year. In this research, clustering of potential fruit-producing areas was carried out using the K-Means algorithm. By using K-Means aims to facilitate the grouping of a block with a lot of fruit production, and low. The result of this research is that C1 (highest) is 14 Harvest Block data, and C2 (lowest) is 11 Harvest Block data.

Measles is a contagious infections disease that attacks children caused by a virus. Transmission of measles from people through coughing and sneezing. Measles causes disability and death, so further threatment is needed. Measles immunization program that can inhibit the development of measles is one of the efforts in eradicating the disease. In this study the data used were sourced from the Central Statistics Agency National in 2013-2017. This study uses datamining techniques in data processing with K-Medoids algorithm. The K-Medoids method is a clustering method that functions to break datasets into groups. The advantages of this method are the ability to overcome the weaknesses of the K-Means method which is sensitive to outliers. Another advantage of this algorithm is that the results of the clustering process do not depend on the entry sequence of the dataset. The k-medoids clustering method can be applied to the data on the percentage of measles immunization can be identified based on province, so that the grouping of provinces based on these data. From the data grouping three clusters are obtained: low cluster (2 provinces), medium cluster (30 provinces) and high cluster (2 provinces) with the percentage of measles immunization in each of these provinces from data grouping in percentage. It is expected this research can provide information to the govermant about the data on grouping measles immunization for toddlers in Indonesia which has an impact on the distribution of immunization against measles toddlers in Indonesia.

Illiteracy is the state of being unable to read and to write for communication. A large number of people still experiencing illiteracy in a country is one indicator showing that the country is still not developed. As many as 3.4 million people or around 2.07% of the population in Indonesia are still illiterate. This study aims to create a grouping model using the k-medoids algorithm. The k-medoids method is a clustering method that serves to break down datasets into groups. The data used is sourced from the Central Statistics Agency. Entered data are percentage of illiterate population in 2009-2017. The number of records used is 34 provinces which are divided into 3 clusters namely high cluser, medium cluster and low cluster. From the results of k-medoids calculation, one (1) province was categorited as a high cluster, twelve (12) provinces as a medium cluster and twenty-one (21) provinces as a low cluster. The implementation process using the RapidMiner 5.3 application is used to help find accurate values. It is hoped that this research can be used as one of the bases for decision making for the government in an effort to equalize the level of illiteracy according to the province which has an impact on reducing of illiteracy rates in Indonesia.