The statistics used include 24 industrial classifications, with the number of manufacturing employees in the 2017-2019 industry as a percentage. The data collection process is carried out through a survey conducted by the Central Statistical Agency (abbreviated as the BPS) with Url: https:/in Large Medium Industries and Micro & Small Industries. This study explores the use of unregulated learning clustering techniques in data mining in the form of clusters of employees in the production industry. Human work is still needed on the production floor in the manufacturing industry to ensure a smooth operation. The manufacturing sector is one of the major contributors to the Indonesian economy. This mapping can be one of the foundations on which to constantly increase the number of micro and small firms, since the role of MSEs in jobs is extremely important, since it is apparent that MSEs have continued to grow considerably. Accuracy, accuracy, reminder and f-measurement parameters are 100% demonstrated in the cluster test results. In the high cluster are only Central Java, West Java and East Java. The results of the calculation of k-medoids show that 90% of Indonesia's area is within the low class. In accordance with the results of the cluster (k=2), the label is divided into two (high cluster (clt1) and low group) (clt2). The numbers of clusters were determined with the Davies Bouldin Index method (DBI), where k = 2 is the best value (0.111). The average number of small and micro enterprises for 2017-2019 is the attribute used. The k-medoids method is the solution used in cluster mapping.
The dataset used consists of three-four records of the central Statistical Bureau (SourceUrl: https:/composed of the micro and smaller companies in 2017-2019.
The research aims to analyze the number of micro and small companies in Indonesia using data mining techniques to map a cluster. The largest segment of national economic actors is the micro and small enterprises (MSEs). The results of the research based on the analysis obtained the best network architecture 4-11-1 with an MSE Training value of 0.00000012, MSE testing/performance of 0.00115144 (the smallest compared to other architectures) and Epoch 343 Iterations. Meanwhile, the test data used 2016-20 as the target test data. The training data uses the 2011-20 dataset as the training data target. Based on this dataset, the dataset will be divided into two parts. The analysis process uses a dataset of Foreign Exchange Reserves (US $ Million) in Indonesia 2011-2020.
Musical instruments quiz pdf update#
This method is able to update the values of bias and weights according to the one-step secant method. This paper aims to analyze the ability and performance of one of the training functions in the backpropagation algorithm, namely One-step secant, which can later be used or used as a reference in the case of data forecasting. The training function used in the ANN method, especially backpropagation, can produce different forecasting accuracy, depending on the method parameters given and the data to be predicted. In addition, there is usually an interactivity like a quiz that can display quizzes about musical instruments by playing their voices and asking where these musical instruments originated. The implementation of this paper is a program that can display musical instruments, sounds, history, and information about this instrument.
Musical instruments quiz pdf software#
This software usually contains theory and information about a musical instrument and to test user knowledge, it can be tested through quizzes in this program where all the questions displayed are related to musical instruments. One of them is a program that can introduce and teach various types of musical instruments. Multimedia computers are now widely used to introduce a particular topic of discussion. But the way to introduce this is very limited to the tools that are available and relatively inexpensive, such as guitars, violins, and the like. How the instructor introduces musical instruments is shows the instrument physically. Identify music equipment is an early stage in the process of learning music, both for theoretical and practical purposes.
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