Perbandingan Algoritma K-Means Clustering dengan Fuzzy C-Means Dalam Mengukur Tingkat Kepuasan Terhadap Televisi Dakwah Surau TV

Da'wah Television Surau TV is a broadcasting media that presents broadcasts around Islam. This media will quickly develop as it presents broadcasting material in meeting the spiritual needs of its viewers. To Increased media development is highly dependent on the satisfaction of the audience in all aspects of broadcast supporting. It is therefore, to measure the level of audience satisfaction as an effort to generate continuous broadcast quality improvement.This research is performing of algorithm clustering comparation with K-Means Clustering modeling and Fuzzy C-Means modeling to classify and mapping the most appropriate dataset so that it can assist analysing or measuring the level of audience satisfaction toward the da'wah television Surau TV. Comparison of clustering algorithm performance with K-Means Clustering modeling and Fuzzy C-Means modeling is based on processing speed and trace value of each RMSE parameter of clustering algorithm. The RMSE result of clustering research using algorithm with K-Means Clustering is 2.09879 and by using algorithm with Fuzzy C-Means model is 2.07911. Fuzzy C-Means modeling speed is faster in conducting the clustering process compared with K-Means Clustering modeling. It can be concluded that clustering with Fuzzy C-Means modeling is able to produce more accurate cluster compared to clustering with K-Means Clustering modeling accuracy   Keywords: Clustering; K-Means; Fuzzy C-Means; Satisfaction rate survey; RMS

OPTIMIZATION OF MARKET BASKET ANALYSIS USING CENTROID-BASED CLUSTERING ALGORITHM AND FP-GROWTH ALGORITHM

The proliferation of the food and beverage sales business requires the creativity of business owners to offer their flagship products to every consumer, both new and subscribed consumers. A large number of menu choices makes the ordering process long because consumers are confused about which menu will be the best choice. the seller to be able to provide the right recommendations so that orders can take place faster. Shopping cart analysis is an activity that has often been done to find out the items found that are sold simultaneously. The FP-Growth association method is a faster algorithm for generating association rules, but the association process in large dataset sizes tends to add large items so that the accuracy value of association rules decreases. So that in this study, the grouping of datasets was carried out using a clustering model with a centroid-based algorithm, namely k-means, k-medoids, and fuzzy c-means. This research was conducted through dataset collection, dataset preparation, clustering modeling, evaluation of clustering models using DBI and silhouette index, association modeling, and evaluation of association models using lift ratio. The results of this study showed that the clustering model with the best DBI and silhouette index values ​​was at k=3 for k-means, k=2 for k-medoids, and k=7 for fuzzy c-means. The number of association rules is generated from the grouped data set using fuzzy c-means, but the highest average lift ratio is in the association rules generated from the grouping data set using k-means. From the association model using k-means and FP-Growth, 32 unique association rules were found with the 4 most frequently found items, namely cireng chili oil, regal milk coffee, banana cheese, and vietnam drip

Analisa Perbandingan Algoritma K-Means Dan Fuzzy C-Means(Studi Kasus:Topik Skripsi Sistem Informasi)

The performance of each algorithm is very important, as well as the selection of a thesis topic for students final year. Clustering is a grouping of data without specific data based on the class. Clustering can be used to label the data class is not yet known. The method used is the CRISP-DM which through understanding of business processes, understanding the data, the data preparation, modeling, evaluation and deployment. The algorithm used for the formation of clusters is a K-Means algorithm and Fuzzy C-Means. K-Means and Fuzzy C-Means is one of data method non-hierarchical clustering.RapidMiner 7.0 is using the research to aid clustering of attributes used are the academic year, sex and thesis topic. The result this research is efficiency based on time. The result are used as a feedback in the use of an algorithm to study the case further

ANALYSIS OF K-MEANS ALGORITHM FOR RECOMMENDATIONS STUDENT CAREER DETERMINATION

ABSTRACTCareer is a person's progress in a job that is obtained through training or work experience during his life. The stages in a career start from knowing the type of job you are interested in based on your expertise, so there is a reference for finding the job you want. After knowing the job you want, the next step is to stay focused and deepen your skills in that field, so you can master the job you're looking for. Based on these stages, a system is needed that can recommend careers that can assist students in determining careers that match their potential based on their academic grades. In this study, the K-Means algorithm was used to analyze the problem. This study designed a k-means algorithm analysis system for career suitability recommendations for web-based students using HTML, PHP, CSS and XAMPP programming languages. The method used in this study is the Unified Modeling Language (UML) method. This research is able to provide career recommendations for students using the k-means clustering algorithm for three types of careers, namely Web Engineer, Programmer and Software Engineering. This study produces an accuracy rate of 96.6% with manual calculations with results in the system This research is able to provide career recommendations for students using the k-means clustering algorithm for three types of careers, namely Web Engineer, Programmer and Software Engineering. This study produces an accuracy rate of 96.6% with manual calculations with results in the system This research is able to provide career recommendations for students using the k-means clustering algorithm for three types of careers, namely Web Engineer, Programmer and Software Engineering. This study produces an accuracy rate of 96.6% with manual calculations with results in the systemKeywords: Career, K-means, Recommendations

Pengelompokkan Dataset Bus Menggunakan Algoritma K-Means

Data mining is the process of finding information by identifying patterns from datasets. The process of finding this information can be done by grouping data into several groups from a dataset which in data mining is called the clustering method. Clustering is the process of partitioning a dataset into several subsets or groups based on the similarity of the characteristics of each data in the existing groups. The clustering method used in this research is K-Means which belongs to the Partition Clustering algorithm group. This method has also been widely used in solving problems related to sales clustering, forest fires, agriculture, transportation, and so on. In this study, the k-means algorithm was used to classify the Bus BB dataset based on data collected during 2022. In the process of converting raw datasets into useful information, the Knowledge Discovery in Database (KDD) process was used. In the early stages, data cleaning will be carried out, then data selection, data transformation, and data mining will be carried out using the Rapidminer software. Modeling results were evaluated using the Davies Bouldin Index (DBI) instrument. Based on the research that has been done, it can be seen that the K-Means algorithm can be used to group BB bus datasets. Which later can be used by companies as an illustration, this research can also be used as input for companies/service providers.   Abstrak Bahasa Inggris maksimum 250 kata dalam satu alinea menggunakan huruf Arial 10, spasi 1. Abstrak berisi pendahuluan singkat, tujuan, metode dan hasil secara ringkas dan jelas. Penulisan singkatan yang tidak umum tidak diperkenankan kecuali didefinisikan sebelumnya

Cluster Analysis of IRIS Spectroscopic Line Profiles and SDO/AIA EUV Emission in Observations and RMHD Simulations of the Solar Atmosphere

Spatially-resolved observations from the IRIS and SDO/AIA satellites, especially when coupled with realistic 3D RMHD simulations, are a powerful tool for analysis of processes in the solar chromosphere, transition region, and corona. However, the complexity of the data makes understanding the observations and modeling results difficult. In this work, we apply unsupervised clustering algorithms for analysis of observational and synthetic chromospheric Mg II h&k 2796&2803 and transition region C II 1334&1335 line profiles observed by IRIS, and extreme ultraviolet (EUV) emission observed by SDO/AIA, for various types of problems. The synthetic line profiles are computed for simulations of the quiescent solar atmosphere (using the StellarBox and RH1.5 codes). The K-Means clustering algorithm is applied, and the selection of an optimal number of clusters is supported by the average silhouette width technique. We discuss applications of the line profile clustering method to 1) visualization of computational and observational spectroscopic imaging data; 2) understanding of evolutionary trends and behavior patterns of quiet Sun emission and during solar flares; and 3) recognition of heating events and shock waves

Pemodelan K-Means Pada Penentuan Predikat Kelulusan Mahasiswa STMIK Palangka Raya

oai:ojs2.stmikplk.ac.id:article/2Data mining by applying the cluster model can use the K-Means concept as a clustering method. K -Means model application can be used to classify data such as the title of graduation students based on the amount of load studies, GPA, and graduation thesis. This research was conducted to find out the results of the use of the concept of K-Means on the determination of student graduation in order to get the results in the form of an analysis of the modeling. The problem of this research is to seek to model the concept of K-Means on the determination of graduation Students using Student Data of STMIK Palangkaraya. K-Means algorithm is used through three stages: initialization , the first iteration and the second iteration. The results of the modeling is the use of K-Means algorithm to determine student graduation to obtain analysis of the results in the form of 70 % could determine the appropriate graduation of 10 data were used as a sample. Modeling using the K-Means is one of the concepts to be able to classify the data, so the use of K-Means algorithm can be a reference for the development of a modeling study, especially regarding data mining