Clustering Menggunakan Algoritma DBSCAN (Density-Based Spatial Clustering of Applications with Noise) untuk Data Hasil Produksi Potensi Pertanian Studi kasus : Kabupaten Gresik

DBSCAN(Density-Based Spatial Clustering of Aplication with Noise) Algprthm is a clustering algorithm which is developed by density-based. This algorthm make clusters area where they have their own high density, and find those clusters in arbitrary shape in spatial database with noise. On this method, noise is used to present area which they have low density. That noise is used to be apart area where they have different cluster, on object in data spatial. To find a cluster, DBSCAN is used to a set maximum density point connected by Eps and MinPts parameter. Eps parameter is used to determine radius of set points of different cluster and MinPts parameter used to give constraint of points number which to be part of cluster in Eps radius

ESTIMASI RELIABILITAS MENGGUNAKAN ESTIMATOR MAKSIMUM LIKELIHOOD YANG DIMODIFIKASI DARI DISTRIBUSI EKSPONENSIAL

The estimation of reliability is an estimation that describes an assessment of reliability of particular component, in which and are independent random variables where represents the strength and represents the stress. The most important element in this estimation is the existence of parameters from distribution random variables and . In this thesis, we discussed the estimation of reliability of two parameters exponential distribution where and had common location parameter but different scale parameter. Scale parameters were estimated using a modified maximum likelihood estimator, while location parameter was estimated using consistent alternative estimator. In the data simulation, we calculate the confidence interval of using asymptotic distribution of . The result of the present study shows that for , , and samples with , and , in observation interval with 95% confidence interval

PENENTUAN KUALITAS AIR MINUM KEMASAN MENGGUNAKAN JARINGAN SYARAF TIRUAN (STUDI KASUS : AIR KEMASAN GALON �QANNAT� YOGYAKARTA)

Artificial neural network (ANN) is a clone of the human brain way of thinking. Neural network is implemented by using a computer program that can solve a number of calculations during the learning process. One of the kinds of neural networks is a single-layer perceptron feedfoward. The quality of bottled water is determined by the elements contained in the water with a predetermined standard. By using a single-layer perceptron neural network feedfoward, quality drinking water to be packaged can be determined, so that bottled water is consumable. This research tries to build an artificial neural network system which can identify that bottled water is not feasible or appropriate to be packed with the input data elements contained in water. Every element of water is then normalized through the process of training and testing. The process of training and testing conducted by the various values α and θ. All of the training and testing process, produce average of network performance with 87,269% accuracy, so that a single-layer perceptron feedfoward method can be used to determine the quality of bottled water that suitable with drinking water quality standard

PENERAPAN PENDEKATAN BARU METODE FUZZY-WAVELET DALAM ANALISIS DATA RUNTUN WAKTU A NEW APPROACH OF FUZZY-WAVELET METHOD�S IMPLEMENTATION IN TIME SERIES ANALYSIS

Recently, many soft computing methods have been used and implemented in time series analysis. One of the methods is fuzzy hybrid model which has been designed and developed to improve the accuracy of time series prediction. Popoola has developed a fuzzy hybrid model which using wavelet transformation as a pre-processing tool, and commonly known as fuzzy-wavelet method. In this thesis, a new approach of fuzzy-wavelet method has been introduced. If in Popoola�s fuzzy-wavelet, a fuzzy inference system is built for each decomposition data, then on the new approach only two fuzzy inference systems will be needed. By that way, the computation needed in time series analysis can be pressed. The research is continued by making new software that can be used to analyze any given time series data based on the forecasting method applied. As a comparison there are three forecasting methods implemented on the software, i.e. fuzzy conventional method, Popoola�s fuzzy-wavelet, and the new approach of fuzzy-wavelet method. The software can be used in short-term forecasting (single-step forecast) and long-term forecasting. There are some limitation to the software, i.e. maximum data can be predicted is 300, maximum interval can be built is 7, and maximum transformation level can be used is 10. Furthermore, the accuracy and robustness of the proposed method will be compared to the other forecasting methods, so that can give us a brief description about the accuracy and robustness of the proposed method

PENERAPAN METODE ELMAN RECURRENT NEURAL NETWORK DAN PRINCIPAL COMPONENT ANALYSIS (PCA) UNTUK PERAMALAN KONSUMSI LISTRIK

Electricity consumption in Indonesia each year continues to increase in line with national economic growth. Therefore, forecasting electricity demand in Indonesia is needed in order to describe the condition of the electrical current and the future. This study aims to apply the method of Elman Recurrent Neural Network and Principal Component Analysis (PCA) to construct a system for electricity consumption forecasting applications. Forecasting techniques used in this study is ARIMA Box Jenkins method used to determine the lag-lag effect on forecasting and Principal Component Analysis (PCA) is used to simplify the observed variables by means shrinking (reducing) dimension. Elman Recurrent Neural Networks Neural networks are used to model complex relationships between inputs and outputs to discover data patterns. Factors to be input ANN is a factor of population, GDP growth, industrial growth and the demographic data that includes customer electricity consumption of household, industrial, business, social and public. The results showed that the application of methods of Principal Component Analysis (PCA) to determine the dominant factors affecting power consumption and ARIMA Box Jenkins model can already be used to determine the lag-lag input data. Elman-RNN method is used to simulate the network parameters are established then performed to obtain the training and validation of the value of Mean Square Error (MSE) network. Accuracy of forecasting was measured using Mean Absolute Percentange Error (MAPE) and the average value of MAPE forecast in samples with 5-year forecast period for forecasting total consumption amounted to 0.33% 1, 2 total consumption amounted to 0.64%, 1.21% of households, industry 2.62% , business 3.25%, 0.77% and public social 0.49%

Uji Distribusi Normal Multivariat dengan Kendala Mean Terurut

In this thesis, the test ordered means of multivariate normal distribution against all alternatives for case when the covariance matrices are known. Use the likelihood ratio method and isotonic application, we obtain the test statistic and study its null distribution. to propose the critical values and simulation study for compute power of test and p-value from bivariate normal distribution