KLASIFIKASI POLA SIDIK JARI MENGGUNAKAN JARINGAN SYARAF TIRUAN BACKPROPAGATION

This reseach describes the fingerprint classification. Proposed to classify human based on three classes such as: whorl, arch, and loops. The proposed system consist of five steps preprocessing, segmentation, feature extraction and classification. In preprocessing there are some of steps such as grayscale, median filter, auto contras, and histogram. Segmentation used otsu thresolding method and features extraction used gray level coocurence matrix (GLCM), in wich the features are correlation, contrast, energy, homogeneity, and entropy. These classification use backpropagation neural network. The result shown that system can classify fingerprint with accuracy 87,5%

PERAMALAN KLB CAMPAK MENGGUNAKAN GABUNGAN METODE JST BACKPROPAGATION DAN CART

Forecasting Measles Outbreak in an area is necessary because to prevent widespread occurrence in an area. One way that is done in this study is to predict the incidence of measles by using a combination of backpropagation ANN and CART. Backpropagation ANN is used to predict the incidence of measles periodic data, then the CART method used to perform the determination of an outbreak or non-outbreak area. Backpropagation neural network is one of the most commonly used methods for forecasting which can result in a better level of accuracy than other ANN methods. While the methods of CART is a binary tree method is also popular for the classification, which can produce models or classification rules. Results of this study show that the number of the best window for backpropagation neural network to forecast the outcome affect forecasting accuracy. Methods of the ANN can do forecasting for time series with accuracy 86.71%. The classification using of CART is 88.52%, but the classification with ANN is 83.61%. So that classification was done by CART for prediction outbreak/non outbreak in this research has accuracy more better than classification with ANN backpropagation

PENGGUNAAN DATA MINING UNTUK MENCARI ATURAN ASOSIATIF DARI DATABASE PENGOBATAN PADA KLINIK AMANAH KABUPATEN SLEMAN PROPINSI DIY DENGAN METODE QUANTITATIVE ASSOCIATION RULES

The problem of association rules discovery between medicine item combination and quantities, occurred at many hospitals and health clinics agencies, they are difficulty in predicting or estimating medicine needs and discovering association between the medicine with other medicine. This study focus on discovering association rules of medicine and quantities medicine of prescription provided by a doctor, in this study we use quantitative association rules methods and Lqtid algorithms. We found 14 interesting rule, 303 new items generated in the mapping process, and the support of quantitative association rules will decrease drastically as the effect of new items generated in partition and mapping process

PEMODELAN DIMENSIONAL DAN IMPLEMENTASI OLAP UNTUK ANALISIS DATA BLM PNPM MANDIRI PERKOTAAN

Dimensional modelling and OLAP implementation is an effort to improve OLTP information systems capability that already exists and is used as one tool in decision making. Increased capability of the system is expected to solve the problems in decision making through more intuitive and interactive data retrieval using multidimensional analysis techniques so that data exploration can be done through the OLAP operations (drill down / roll up, drill across, pivoting and slicing/ dicing). The study used data utilization BLM PNPM Urban as a case study using data 17 cities/ regencies in Central Java as a sample and results are expected to have the scalability range up to national level. Pentaho Business Intelligence Platform are intended for portal and business intelligence used as a platform in the development and implementation of the system. Business intelligence in the design phase of this research include needs analysis, business process analysis, analysis of data sources, multidimensional modeling, design datamart, designing ETL, OLAP design and user interface design. Data analysis using OLAP helps improve the accessibility and value of of information compared with the analysis of data on OLTP-based systems especially for data analysis for the various aspects that should be reviewed to improve the quality of decisions will be taken

PERANCANGAN DAN IMPLEMENTASI DATA WAREHOUSE PADA PONDOK PESANTREN SALAFIYAH SYAFI�IYAH SUKOREJO SITUBONDO

As an Islamic boarding education providers that have been nearly 100 years old, Salafiyah Syafi'iyah Islamic Boarding School has a large enough data and have different data types because it uses some kind of application. Program applications are generally not able to support the daily operational activities as well as support the strategic decision-making. While the data analysis of students in this environment of Salafiyah Syafi'iyah Islamic Boarding Schools is absolutely necessary, because it is one tool that can be used to determine the next strategic steps. Design and implementation of data warehouse Islamic Boarding School Salafiyah Syafi'iyah Sukorejo Situbondo aims to provide information based on data analysis students who have been saved in a database owned several Islamic boarding schools. Implementation of multidimensional analysis with OLAP (On- Line Analytical Processing) in the data warehouse uses ContourCube that can provide the results of complex analysis. In implementation, the data warehouse has been able to answer the needs of information that can be used to support strategic decision making boarding schools such as: the number of students in each dorm, the number of students in each educational institution, the number of students who quit as well as financial contributions boarding informatio

KLASIFIKASI DATA NAP (NOTA ANALISIS PEMBIAYAAN) UNTUK PREDIKSI TINGKAT KEAMANAN PEMBERIAN KREDIT: Studi Kasus : Bank Syariah Mandiri Cabang Luwuk Sulawesi Tengah

Mandiri Syariah Bank Branch Office of Luwuk, receives a very large number of proposal credit in every month and needs a quick response. Thus, the system should be developed to perform data mining in the data heap to be used for specific purpose, one of the purpose is to analyze the risk of credit allowance. Naive bayes classifier is an approach that refers to the bayes theorem, which combine the prior knowledge and the new knowledge. So that, this classifier is one of a simple classification algorithm but has a high accuracy. this research will prove the ability of naive bayes classifier to classify the debitur data that contains information of credit allowance in Mandiri Syariah Bank Branch Office of Luwuk. Before doing the classification, data of debitur needs to pass a preprocessing method. Then the classification process by naive bayes classifier was done after passing the preprocessing method. After the data is classified, it produces the probability of classification model to predict the class of next debitur. From the testing result, the program shows the smallest value of the accuracy is 80% by using 100 records of sample and generating highest accuracy for about 98,66% by using 463 records of sample. The testing results by Rapid Miner 5.3 software obtained the smallest value of the accuracy is 64,79% by using 100 records of sample and the highest accuracy is 80,06% by using 463 records of sample for naive bayesian classification. For the method of support vector machine obtained the smallest value is 63,99% accuracy by using 100 records of sample and the highest accuracy of 78,64% by using 463 records of sample

PENGAPLIKASIAN ALGORITMA CLASSIFICATION BASED ON PREDICTIVE ASSOCIATION RULES UNTUK ANALISA KARAKTERISTIK KECELAKAAN LALU LINTAS (Studi pada Kepolisian Daerah Sulawesi Tenggara)

Data of traffic accident in Southeast Sulawesi has increased every year. Therefore, traffic accident in Southeast Sulawesi needs to get more effective handling. Effective handling related to right policies about the management and traffic engineering. It should be supported by knowledge based on a traffic accidents database. One of the knowledge that may be got is the characteristic of severity (dead, seriously injured, lightly injured) of the traffic accident. This research is to apply Classification based on Predictive Association Rules (CPAR) algorithm in data base traffic accident, Southeast Sulawesi Police Department between in the period of 2010 to 2012. CPAR algorithm produces Class Association Rules (CARs) which is used to describe knowledge about the characteristics of severity of the traffic accident victims. The results of experiment shows that the main cause of traffic accident were human factors (driving under the influence of alcohol and driving exceed the maximum speed) and environmental physical factors (damage road and elbow road). Types of accidents (single and head-on) and accidents involving motor cycles contribute potentially that the victims died. Testing the accuracy using 10-fold cross validation shows that the average accuracy of CPAR algorithm is 48,75% that is higher than PRM algorithm 41.13%