Stemming is a process to return a derivative word into its root word by eliminating the affixes. This is necessary to support a better information retrieval system. Some research on stemming algorithm, including algorithm Nazief & Adriani and Porter. Each stemmer has advantages and disadvantages of each. The purpose of this study is to compare the two stemmers, so it is known which algorithm is better to support information retrieval system. This research mostly applied literature study with reference to research conducted by Asian Jelita and Fadillah Z Tala. Test documents obtained from online news sites (detik.com). The process of analysis is done by calculating the number of correct results and experiencing stemming errors (over stemming, under stemming, unchanged, spelling exception), then comparing the result and time of the process, so it will be known which stemmer is better to support information retrieval system.

Based on data of dairy milk cow in Animal Farms of Boyolali  District, only shows the total amount of dairy milk cow in Boyolali  District. So that Animal Farms of Boyolali  District does not know which areas produce dairy milk cows with large numbers or small. Therefore, an algorithm is needed to facilitate the grouping of potentially dairy milk cow based on milk production data (liter), number of female dairy cows (how many), number of owners and year of production. In this research, using the K-Means algorithm is used to the grouping of potential dairy milk cow producing areas. By using K-Means aims in facilitating the classification of an area that has the greatest potential dairy milk cows, medium and small. The result is an illustration that shows the regional grouping based on dairy milk cow yields, which are 13 districts that have a potency of dairy milk cow (cluster1), 28 districts that have medium potency dairy cows producing (cluster2), and 28 districts less potential dairy milk cows (cluster3). For further research could be carried out the excavation process variation data variables that clustering results produced can be maximized.Keywords: K-Means algorithm, clustering, data mining, dairy milk cows

Currently car rental business is a business that promises many benefits. The number of car rental service customers each year is increasing, this is seen from the number of car rental businessmen increasingly from year to year. But behind the business profits are quite tempting there have been many complaints in the car rental business is the crime of theft. There have been many tools or ways taken to overcome the crime of car theft, but not many can be the right solution. Making this monitoring application aims to implement a GPS Tracking based security system in which the monitor can display the status and location of the existence of the car that has been hired.The method used is object-oriented software engineering including data types, data retrieval methods, system analysis, system design, system development, and testing. The end result of this research is a tool and monitoring application system capable of displaying icon maker according to the existence of the car and can display notification or alert. The tool used is Arduino Uno with some modules like GPS Module to capture coordinates and GPRS Module to send data to the database server.To view using the web with the monitoring map and alert system. Alert system will automatically process the data into the database, if the lease time exceeds the limit of rent then the alert will process the distance data between a rental car with garage. If the distance exceeds 4 km then the system will display alert/notification. Then the admin can also track the existence of the car. The functional test result of the system works well, the result of the validity test of the system provides valid data with 0.0002 tolerance average, and the result of the feasibility test of the system can be declared eligible for use.

Poverty is one of the problems experienced by some developing countries, including Indonesia. There are many ways to mitigate poverty, for example, Indonesian government policy overcome this situation by Non-Cash Food Aid Program (BPNT). The electoral candidate for BPNT in the rural area is carried out by Poverty Reduction Team (SATGASKIN). To avoid uneven and untargeted assistance with the process, a system is capable for addressing the matter. The selection methods in this research were Naive Bayes and Simple additive weighting. The purpose of this research was to design and build an application that provided convenience to SATGASKIN in determining the eligibility of prospective beneficiaries and prioritizing beneficiaries. As a result of the study, the system can be used by SATGASKIN to help determining the recipients’ eligibility with 85% accuracy value, 85.71% Precision, and 92.31% Recall. Naive Bayes and Simple Additive Weighting (SAW) methods reached 100% according to the results by manual calculations.

The library is a collection place of various kinds of books. Arrangement of books by category called book shelving makes easier for customers to choose and find books. However, the location and arrangement of book categories becomes a problem in a library. Based on the book borrowing data, data mining was carried out to find out a book borrowed simultaneously by library visitor in one transaction. This can be solved by using the association rule technique and a priori algorithm. Possible combinations of borrowed books were based on certain rules and then tested whether the combination of items meets the minimum support requirements to create eligible rules. The results of this study were in the form of information about a combination of borrowed books for libraries to arrange the location of books according to categories that are often borrowed together.