Rapidminer Application Research Articles

Klasifikasi pada Tempat Tinggal Menurut Provinsi dan Jenis Kepemilikan Berdasarkan Algoritma K-Means

<p><em>H</em><em>ousing is derived from the word house</em><em> which means</em><em> a place that has a place to live which will stay or stop in a certain time. Housing is a residence that has been grouped into a place that has facilities and infrastructure. The problem in this study focuses on the type of residential ownership in the form of SHM ART, SHM Non ART, NON SHM and others. </em><em>T</em><em>hese four types</em><em> can be used</em><em> to know the percentage of ownership in all provinces in Indonesia. Due to the fact that there is still a lot of information about the type of certificate ownership, there is still not much ownership. Therefore, the use of the k-Means algorithm as a data mining concept in the form of clusters, where the data already has parameters or values that fall into the category of unsupervised learning. That data produced the best. The data was obtained from published sources of the Republic of Indonesia government agency, namely the Central Statistics Agency data with the category of household processing with self-owned residential buildings purchased from developers or non-developers by province and type of ownership in 2016 throughout Indonesia. In conducting the dataset, researchers used the RapidMiner application as a clustering process application. This research </em><em>shows that</em><em> there are more types of ownership in the SHM ART, but for other values it is still smaller than the value in other types of ownership which is the second largest value. So</em><em>,</em><em> in this case, the role of government in providing assistance in the process of ownership in order to become SHM ART</em><em> is very important</em><em>.</em></p>

COMPARISON OF NAIVE BAYES ALGORITHM AND C.45 ALGORITHM IN CLASSIFICATION OF POOR COMMUNITIES RECEIVING NON CASH FOOD ASSISTANCE IN WANASARI VILLAGE KARAWANG REGENCY

Non-Cash Food Assistance or Bantuan Pangan Non-Tunai (BPNT) is food assistance from the government given to the Beneficiary Family (KPM) every month through an electronic account mechanism that is used only to buy food at the Electronic Shop Mutual Assistance Joint Business Group Hope Family Program (e-Warong KUBE PKH ) or food traders working with Bank Himbara. In its distribution, BPNT still has problems that occur that are experienced by the village apparatus especially the apparatus of Desa Wanasari on making decisions, which ones are worthy of receiving (poor) and not worthy of receiving (not poor). So one way that helps in making decisions can be done through the concept of data mining. In this study, a comparison of 2 algorithms will be carried out namely Naive Bayes Classifier and Decision Tree C.45. The total sample used is as much as 200 head of household data which will then be divided into 2 parts into validation techniques is 90% training data and 10% test data of the total sample used then the proposed model is made in the RapidMiner application and then evaluated using the Confusion Matrix table to find out the highest level of accuracy from 2 of these methods. The results in this classification indicate that the level of accuracy in the Naive Bayes Classifier method is 98.89% and the accuracy level in the Decision Tree C.45 method is 95.00%. Then the conclusion that in this study the algorithm with the highest level of accuracy is the Naive Bayes Classifier algorithm method with a difference in the accuracy rate of 3.89%.

PENGELOMPOKAN KASUS PENYAKIT AIDS BERDASARKAN PROVINSI DENGAN DATA MINING K-MEDOIDS CLUSTERING

Acquired Immunodeficiency Syndrome or Acquired Immune Deficiency Syndrome (AIDS abbreviated) is a set of symptoms and infections that arise due to the destruction of the human immune system due to HIV viral infection. This study discusses about Rapidminer Application in Grouping Cases of AIDS Disease by Province with K-medoid Clustering Data Mining. The rise of AIDS cases in Indonesia has become a case that never escapes government attention. Attention to the ever increasing rate of death makes people worry about the spread of the AIDS virus. Sources of data and research are collected from the information document Number of Villages Who Have Health Facilities produced by the Social Security Administering Board. The data used in this study is data from 2008-2011 which consists of 34 provinces. Assessment criteria used are 2 ie 1). the average number of AIDS cases and 2). the average number of AIDS cases deaths were managed using 3 clusters ie high cluster level (C1), medium cluster level (C2) and low cluster level (C3). So that the cluster C1 obtained for Cases of AIDS Disease by Province as many as 4 provinces of Papua, DKI Jakarta, West Java and East Java, 9 provinces for cluster C2 and for cluster C3 as much as 20. This can be input to the government, the province of concern in the number of cases of AIDS.Keywords: Maining Data, AIDS Disease, Clustering, K-medoid

Penerapan K-Nearest Neighbour dalam Penerimaan Peserta Didik dengan Sistem Zonasi

Admission of new students is a routine activity at the beginning of each new meeting year in all formal educational institutions. At the moment the acceptance of new students uses the zoning system and has been regulated by Permendikbud No. 20 in 2019. This zoning system will accept students where their residence enters through the user area with the school environment. With this Permendikbud the government hopes that there will be an evenness in the quality of education in all schools, so that schools will no longer get the title of superior and non-superior schools. But in a system, the zoning improves anxieties in the school environment. This research supports to help the participating school students will be accepted in accordance with the provisions of the Ministry of Education and Culture. In overcoming problems that arise in the school environment there needs to be a system that can overcome that problem. In this study using the K-Nearest Neighbor (K-NN) method. Where the K-NN method will do the classification of new learners' residence with the school. In determining the classification using the K-NN method used for zoning and non-zoning areas, it is seen based on the closest K value. In finding the optimal value in this study using the Rapidminer application. The optimal high-level test results at K 5 where the value of this K is 83.36%

Penerapan Data Mining Metode Clustering Pada CV. Secom Infotech Menggunakan Algoritma K-Means

Grouping sales data at the Secom Infotech Computer Store is still done manually in Excel. How to group it takes time and allows data to be lost. Clustering is one of the data mining methods that are unsupervised and K-Means is a non-hierarchical clustering method that attempts to divide existing data into one or more groups. The K-Means clustering method can be applied to classify a sales data based on the type of item, type of customer, number of items. The data used is sales data in January-June 2018 as many as 30 data. The results of the tests were carried out using the RapidMiner application where the results contained 2 clusters, namely cluster 0 totaling 14 data and cluster 1 totaling 16 data. K-Means clustering method can be used for data processing using the concept of data mining in grouping data according to attributes.

Application of Classification Method C4.5 on Selection of Exemplary Teachers

The study aims to analyze the selection of exemplary teachers using C4.5 algorithm, which is one of the existing decision tree methods in data mining theory. Teacher data was obtained from the school in SMA Negeri 2 Pematangsiantar (2010-2016). The data used contains information about teacher history and teacher assessment data. This research uses interview technique and questionnaire in obtaining data. There are 11 attributes used in the assessment process: NUPTK, name, age, education, status, Appointment Letter, competence, award, work, teaching load, personality, Position, and label. The system has been tested using the Rapidminer application with 16 data samples. Where the rules are obtained as many as 24 rules. The level of accuracy of the system states that four of the ten attributes have a very significant correlation to the model of relationship rules in determining the proposed model teacher, such as (Position, competence, education, and personality). The four attributes (Position, competence, education, and personality) contribute 82.8% to the model of the rule of connectedness rules in determining the best teacher.

KLASIFIKASI PENERIMA DANA BANTUAN DESA MENGGUNAKAN METODE KNN (K-NEAREST NEIGHBOR)

Determining the status of poor families as recipients of assistance is very important so that poverty reduction assistance from the government can be channeled on target. Data mining utilizes experience or even mistakes in the past to improve the quality of the model and the results of its analysis, one of which is the ability possessed by data mining techniques, namely classification. The purpose of this study was to test K-Fold Cross Validation in the K-Nearst Neighbors algorithm in predicting receipt of village aid funds. In the beneficiary dataset used in this study, there were 159 records or tuples with four attributes (house condition, income, employment and number of dependents). The new data category prediction is done by using the Euclidean Distance manual calculation stage of five different K values. While using the Rapidminer application aims to test the accuracy of the dataset in five different K values. The results show that with K=15 and K=30 the new data (D160) has a "Not Eligible" category with an accuracy of 100%. Then with K=45, K=60 and K=75, the new data (D160) has the category "Eligible" with an accuracy rate of 81.25%.

SENTIMENT ANALYSIS OF BPJS KESEHATAN SERVICES TO SMK EKLESIA AND BINA INSANI JAILOLO TEACHERS

BPJS health is a public legal entity that is formed to hold a guarantee program. BPJS health works with government or private health facilities to provide health services for JKN participants and their families. Through the government's health insurance program the community can get good health services. However, the application is still not in accordance with the objectives and benefits of BPJS health so that it creates positive opinions and negative opinions about the use of BPJS health services. The purpose of the research conducted was to analyze public sentiment related to the service benefits of the Social Security Organizing Agency's services to Jailolo Vocational School Teachers of Eclesia and Bina Insani Vocational Schools. Analysis and data processing were carried out using a qualitative approach using the Rapidminer application, and for document classification using the Naives Bayes method. Based on the results of analysis of BPJS service utilization documents where the highest opinion is a positive opinion with an accuracy rate of 84.50%

Clustering Analysis of Premier Research Fields

The clusterization is one of methods which utilized to grouping a dataset which has a specific characteristics value. The processed data can be numerical or non-numerical data. Non-numeric data must be transformed first into numerical data. The case study in this study was to group research from six fields of science. The research data is non-numerical data is converted into the research contributions percentage in the science field. Utilized the c-means algorithm, the data was successfully grouped into three excellent research fields. The aim of the clustering is to know how many researchers in one cluster. Dataset is processed by utilizing the c-means algorithm to generated 3 clusters, they are an expeditious technology, entrepreneur and economic creative development, social engineering and strategic area infrastructure development. The data clustering result is presented in the graphic form by utilized the studio Rapidminer application.

Apriori Algorithm through RapidMiner for Age Patterns of Homeless and Beggars

Homeless and beggars are one of the problems in urban areas because they can interfere public order, security, stability and urban development. The efforts conducted are still focused on how to manage homeless and beggars, but not for the prevention. One method that can be done to solve this problem is by determining the age pattern of homeless and beggars by implementing Algoritma Apriori. Apriori Algorithm is an Association Rule method in data mining to determine frequent item set that serves to help in finding patterns in a data (frequent pattern mining). The manual calculation through Apriori Algorithm obtaines combination pattern of 11 rules with a minimum support value of 25% and the highest confidence value of 100%. The evaluation of the Apriori Algorithm implementation is using the RapidMiner. RapidMiner application is one of the data mining processing software, including text analysis, extracting patterns from data sets and combining them with statistical methods, artificial intelligence, and databases to obtain high quality information from processed data. The test results showed a comparison of the age patterns of homeless and beggars who had the potential to become homeless and beggars from of testing with the RapidMiner application and manual calculations using the Apriori Algorithm.

Metode Decision Tree Algoritma C.45 Dalam Mengklasifikasi Data Penjualan Bisnis Gerai Makanan Cepat Saji

Advances in technology and information currently produces smart innovations in business, which can be called business intelligence. One that we can use is Data Mining technology in digging useful information from sales company data warehouse. The purpose of this research is to apply data mining decision decision tree algorithm C4.5 on fast food outlets business and expected to provide information in the form of sales information about food menu that most liked by customers and less popular (bestselling and less in demand). The methodology used in classifying the sales of this research uses the steps of Algorithm C.45, The process uses five steps in KDD (Knowledge Discovery in Databases), which perpetuates activities such as pre-processing, transformation, data mining, interpretation and evaluation. In addition to performing calculations manually, this research case is also tested using Rapidminer application. From the results of the experiment to find data from the sales data of fast food outlets using algorithm C4.5 results of entropy and the highest gain is 1.501991 on the Food Menu attributes on manual calculations. When using the Rapidminer application the results of the results tree as shown in Figure 3.2. Price (s) - Sold Out - Food Menu (Bento Rice = Less Selling, Chest = Laris) Weight (weight) each attribute: Price (0.738), Menu Type (0.067), Sold Number (0.156), Sales Status (0.040).

PENENTUAN TINGKAT MINAT BELANJA ONLINE MELALUI MEDIA SOSIAL MENGGUNAKAN METODE CLUSTERING K-MEANS

Social media becomes the pre-eminent businessperson in marketing the goods. This is based on the increasing use of social media from year to year. The determination of the level of interest in online shopping through social media aims to help find out which social media are in demand in online shopping. In this study the data used are social media BBM Group, Instagram, Facebook, Twitter and Whatsapp and age and gender. In determining the level of online shopping interest one method of Data Mining used is the method of Clustering K-Means. Clustering is the process of dividing data into classes or clusters based on their similarity. To assist in data processing used Rapid Miner application that is able to provide information on the level of interest in online shopping in social media

Komparasi dan Analisis Kinerja Model Algoritma SVM dan PSO-SVM (Studi Kasus Klasifikasi Jalur Minat SMA)

Attribute Selection is very important for classification process. This research has been done by doing attribute selection using PSO method (Particle Swarm Optimization) on SVM algorithm (Support Vector Machine). The development of the classification model uses three parameters especially data attribute, influence of the transformation of various kernel function and penalty factor (C) toward the performance of SVM and PSO-SVM classification. The analysis uses five kernels in mySVM library that existed in Rapidminer application namely dot, radial, polynomial, neural, and anova kernel. The training data used in the first model classification development is student interest data at ABC high school on 2013-2014 year academic. The first model is evaluated using accuracy, precision, recall, and auc value test. The first result shows that the anova kernel on PSO-SVM is able to work with accuracy level 99.30% using penalty factor 0.1. The second model has been developed to predict student interest in XYZ high school. The second result shows that PSO-SVM with kernel anova is able to classify students interest with 99.29% accuracy level. Keywords— Optimization, SVM, PSO-SVM, Student Interest.