THE MODELING OF "MUSTAHIQ" DATA USING K-MEANS CLUSTERING ALGORITHM AND BIG DATA ANALYSIS (CASE STUDY: LAZ)

There are a lot of Mustahiq data in LAZ (Lembaga Amil Zakat) which is spread in many locations today. Each LAZ has Mustahiq data that is different in type from other LAZ. There are differences in Mustahiq data types so that data that is so large cannot be used together even though the purpose of the data is the same to determine Mustahiq data. And to find out whether the Mustahiq data is still up to date (renewable), of course it will be very difficult due to the types of data types that are not uniform or different, long time span, and the large amount of data. To give zakat to Mustahiq certainly requires speed of information. So, in giving zakat to Mustahiq, LAZ will find it difficult to monitor the progress of the Mustahiq. It is possible that a Mustahiq will change his condition to become a Muzaki. This is the reason for the researcher to take this theme in order to help the existing LAZ to make it easier to cluster Mustahiq data. Furthermore, the data already in the cluster can be used by LAZ managers to develop the organization. This can also be a reference for determining the zakat recipient cluster to those who are entitled later. The research is "Modeling using K-Means Algorithm and Big Data analysis in determine Mustahiq data ". We got data Mustahiq with random sample from online and offline survey. Online data survey with Google form and Offline Data survey we got from BAZNAS (National Amil Zakat Agency) in Indonesia and another zakat agency (LAZ) in Jakarta. We conducted by combining data to analyzed using Big Data and K-Means Algorithm. K-Means algorithm is an algorithm for cluster n objects based on attributes into k partitions according to criteria that will be determined from large and diverse Mustahiq data. This research focuses on modeling that applies K-Means Algorithms and Big Data Analysis. The first we made tools for grouping simulation test data. We do several experimental and simulation scenarios to find a model in mapping Mustahiq data to developed best model for processing the data. The results of this study are displayed in tabular and graphical form, namely the proposed Mustahiq data processing model at Zakat Agency (LAZ). The simulation result from a total of 1109 correspondents, 300 correspondents are included in the Mustahiq cluster and 809 correspondents are included in the Non-Mustahiq cluster and have an accuracy rate of 83.40%. That means accuracy of the system modeling able to determine data Mustahiq. Result filtering based on Gender is “Male” accuracy 83.93%, based on Age is ”30-39” accuracy 71,03%, based on Job is “PNS” accuracy 83.39%, based on Education is “S1” accuracy 83.79%. The advantaged of research expected to be able to determine quickly whether the person meets the criteria as a mustahik or Muzaki for LAZ (Amil Zakat Agency). The result of modeling is K-Means clustering algorithm application program can be used if UIN Syarif Hidayatullah Jakarta want to develop LAZ (Amil Zakat Agency) too

Analisa Sentimen Menggunakan Data Twitter, Flume, Hive Pada Hadoop dan Java Untuk Deteksi Kemacetan di Jakarta

Traffic congestion big cities in Indonesia is unavoidable, especially in Jakarta. The increasing number of vehicle and the lack of public transportation is the main cause of traffic congestion in Jakarta. It disturb people activities. Government already did various efforts to resolve congestion problem, however it needs high installation, maintenance cost and need time to be implemented. Peoples often complained about traffic congestion in Jakarta by posting in Twitter which called tweets. Every tweets post are saved in API Twitter and used for sentiment analysis. It analyzed emotion of the user. Based of problems we do research how  to detect traffic congestion in Jakarta. Therefore, we try to makes Congestion Detection App. We design the app using UML diagrams. Congestion Detection App is connected with Hadoop, Flume, Hive and Derby. The app stream twitters data to colected by connecting with API Twitter. This app is Java-based application which can makes and view data tables. It  performance searching tweets data by ID and analyze traffic condition on a certain region in Jakarta. The perform sentiment analysis to a certain tweet and display the result based on the data table. The result of research is comparing Data from Congestion Detection App with data from Google Maps. We make three valus categories which consist of three colors: green for less traffic congestion have a value of 1. Orange for medium-scale traffic congestion has value of 2 and Red for heavily traffic congestion has a value of 3.  Based on three categories and value we use 4 regions for sample and comparing the values with value from Google Maps Data to get the accuracy. We got 81% average accuracy from the four samples. The result of Data from tweet sample compared with Google Maps Data. It  have big detected congestion with Congestion Detection App