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Dewi Retno Sari Saputro
Program Studi Matematika Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Sebelas Maret Surakarta
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ALGORITME PARTITIONING AROUND MEDOID (PAM) DENGAN CALINSKI-HARABASZ INDEX UNTUK CLUSTERING DATA OUTLIER Aliyatussya’ni Aliyatussya’ni; Dewi Retno Sari Saputro
UNEJ e-Proceeding 2022: E-Prosiding Seminar Nasional Matematika, Geometri, Statistika, dan Komputasi (SeNa-MaGeStiK)
Publisher : UPT Penerbitan Universitas Jember

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Abstract

The process of gathering information from a mathematical pattern in big data to help make decisions is called data mining. Cluster analysis is a multivariate statistical analysis technique that groups observations based on several variables based on the level of similarity. Clustering is a technique in data mining that aims to group data into several clusters. Data objects that have high similarity will be in the same cluster. Outliers data that is different from other data. In statistics, the presence of this outlier will result in data analysis being biased and not reflecting the actual phenomenon. Partitioning Around Medoid (PAM) or K-Medoid is a non-hierarchical-based clustering algorithm. The steps carried out in the PAM algorithm are grouping the data by dividing the data into n groups. Calinski-Harabasz Index is one of the methods used to determine the best number of clusters. The purpose of this study was to examine the PAM algorithm on data containing outliers and the Calinski-Harabasz Index as a method for selecting the best cluster. The results showed that the PAM algorithm and the Calinski-Harabasz Index have good robustness for outlier data. Keywords: Calinski-Harabasz Index, Clustering, Outlier, PAM
METODE FUZZY TIME SERIES MUSIMAN BERDASARKAN PARTISI INTERVAL FREKUENSI DENSITAS Nikmatul Ilmi; Dewi Retno Sari Saputro
UNEJ e-Proceeding 2022: E-Prosiding Seminar Nasional Matematika, Geometri, Statistika, dan Komputasi (SeNa-MaGeStiK)
Publisher : UPT Penerbitan Universitas Jember

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Time series data can be used as material to predict the probability of future events. Time series data has several patterns, one of which is a seasonal pattern. In processing time series data, an analytical method is needed. The fuzzy time series method can be used to analyze time series data using the concept of fuzzy logic. Some fuzzy time series methods usually produce large errors if the data being tested has a seasonal pattern. Therefore, a seasonal fuzzy time series method was developed that can be used for time series data with seasonal patterns. In the fuzzy time series method, it is necessary to determine the effective interval length in order to obtain optimal accuracy. In this study, the frequency density interval partitioning was used to determine the length of the interval. The purpose of this study is to examine the seasonal fuzzy time series method based on the frequency density interval partition. The results of this study indicate that the seasonal fuzzy time series method is suitable for processing seasonal patterned data and the determination of interval length using frequency density interval partitioning can provide optimal accuracy. Keywords: Frequency Density, Fuzzy Time Series, Interval Partition, Seasonal Fuzzy Time Series, Time Series
CLUSTERING DATA NUMERIK MENGGUNAKAN ALGORITME X-MEANS Ayya Agustina Riza; Dewi Retno Sari Saputro
UNEJ e-Proceeding 2022: E-Prosiding Seminar Nasional Matematika, Geometri, Statistika, dan Komputasi (SeNa-MaGeStiK)
Publisher : UPT Penerbitan Universitas Jember

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Abstract

Data mining is the extraction of new and useful information from large data sets that helps in the decision-making process. Clustering is a technique of grouping data that has similar characteristics into the same cluster. Generally, the Clustering process is used for numeric or categorical data. The K-Means algorithm is one of the algorithms that can be used for numeric type data. The stage carried out in the K-Means algorithm is to divide n observations into k clusters so that each observation is included in the cluster with the closest average (centroid), but K-Means still has a weakness in determining the number of clusters. This must be determined specifically by the user. To overcome the weakness of K-Means, the X-Means algorithm was developed by Dan Pelleg and Andre Moore. In X-Means, the value of k is estimated by inputting a range of clusters based on the dataset itself, so that no specific determination of the number of clusters is needed. The purpose of this study is to examine the X-Means algorithm. The results showed that the division of clusters in the X-Means algorithm used the Bayesian Information Criterion (BIC) value. In the X-Means algorithm, inputting a range of clusters for the number of clusters can make the clustering process more efficient. Keywords: Clustering, K-Means, numeric data, X-Means.
METODE HIGH ORDER FUZZY TIME SERIES MULTI FACTORS DENGAN ALGORITMA FUZZY C-MEANS Yuni Wulandari; Dewi Retno Sari Saputro
UNEJ e-Proceeding 2022: E-Prosiding Seminar Nasional Matematika, Geometri, Statistika, dan Komputasi (SeNa-MaGeStiK)
Publisher : UPT Penerbitan Universitas Jember

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Abstract

Clustering is the process of grouping data into several clusters so that the data in a cluster has a high degree of similarity between data with one another but is very different from the data in other clusters. Fuzzy clustering is a technique to determine the optimal cluster in a vector space based on the Euclidian normal form for the distance between vectors. Fuzzy clustering is very useful for fuzzy modeling, especially in identifying fuzzy rules. There are various kinds of fuzzy clustering techniques, one of which is Fuzzy Cluster-Means (FCM). Fuzzy C-Means clustering is a data clustering technique in which the existence of each data point in a cluster is determined by the degree of membership. The purpose of this study is to examine the High Order Fuzzy Time Series Multi Factors method with Fuzzy C-Means in order to get k locations of the data cluster center points as many as k which are then used to form subintervals. The results show that Fuzzy C-Means replaces the process in the High Order Fuzzy Time Series Multi Factors method, which is when the subinterval is formed. Keywords: Clustering, Fuzzy C-Means, Metode High Order Fuzzy Time Series Multi Factors.
Co-Authors Aliyatussya’ni Aliyatussya’ni Ayya Agustina Riza Nikmatul Ilmi Yuni Wulandari