Application of Digital Mining Facing Information Fusion Technology in the Field of National Costume Culture Design

In order to improve the design effect of minority clothing, according to the needs of minority clothing design, this paper uses data mining and Internet of Things technologies to construct an intelligent ethnic clothing design system and builds an intelligent clothing design system that meets customer needs based on the idea of human-computer interaction. In data processing, this paper uses the constraint spectrum clustering algorithm to take the Laplacian matrix and the constraint matrix as input and finally outputs a clustering indicator vector to improve the data processing effect of minority clothing design. Finally, this paper verifies the performance of the system designed in this paper through experiments. From the experimental research, it can be known that the minority clothing design system based on the Internet of Things and data mining constructed in this paper has a certain effect and can effectively improve the minority clothing design effect.

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An Enhanced K-Means Clustering Algorithm for Pattern Discovery in Big Data Analysis of 3-Phase Electrical Quantities

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.44.26854 ◽

2018 ◽

Vol 7 (4.44) ◽

pp. 8

Author(s):

Dikpride Despa ◽

Gigih Forda Nama

Keyword(s):

Data Mining ◽

Big Data ◽

Internet Of Things ◽

Power Distribution ◽

Distribution System ◽

Clustering Algorithm ◽

Measurement Data ◽

Power Distribution System ◽

Multiple Sensors ◽

Industry Standard

The Unila Internet of Things Research Group (UIRG) was developed online monitoring of power distribution system based on Internet of Things (IoT) technology on Department of Electrical Engineering University of Lampung (Unila), has been running for several months, this system monitored electrical quantities of 3-phase main distribution panel of H-building. The measurement system involve multiple sensors such current sensors and voltage sensors, the measurement data stored in to database server and shown the information in a real-time through a web-based application.Main objective of this research was to capture, analyze, and identified the knowledge pattern of electrical quantities data measurements, using Cross-Industry Standard Process for Data Mining (CRISP-DM) data mining framework, for helping the stake holders to continuous improvement of the quality of electricity services, the initial research limited to total 770847 electrical quantities recorded data that save on database system, since 1 September - 31 October 2018, the dataset consist of 21 attribute electrical quantities such as; voltage, current, power factor values, energy consumption, frequency, on H building 3-Phase main panel control.Rapidminer as leading application on knowledge discovery application was used to analyze the big data, K-Mean cluster algorithm implemented to identify the data pattern, the result indicated that 3-Phase load was unbalanced, and Phase-0 was the most utilized phase, based on from total 5 cluster analysis result.

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DATA MINING DALAM PENGELOMPOKAN JENIS DAN JUMLAH PEMBAGIAN ZAKAT DENGAN MENGGUNAKAN METODE CLUSTERING K-MEANS (STUDI KASUS: BADAN AMIL ZAKAT KOTA BENGKULU)

JURNAL TEKNOLOGI INFORMASI ◽

10.36294/jurti.v1i2.298 ◽

2018 ◽

Vol 1 (2) ◽

pp. 211

Author(s):

Prahasti Prahasti

Keyword(s):

Data Mining ◽

Data Processing ◽

Clustering Algorithm ◽

Test Results ◽

Clustering Method ◽

Center Point

Abstrack - This research applies data mining by grouping the types and recipients of zakat. The application is done by the k-means clustering algorithm where the data to be entered is grouped by education and type of work in the distribution of zakat. Then a cluster is formed using the centroid value to determine the closest center point of distance between data. In the k-means clustering algorithm data processing is stopped in the iteration count of the data has not changed (fixed data) from the data that has been grouped. The test is done by using the RapidMiner software experiment conducted by the k-means clustering method which consists of input units, data processing units and output units, k-means clustering grouping data 1-2-1-1, 1-2-1-2 and 3-4-3-4. The results obtained from these tests are grouping the distribution of zakat with each cluster not the same. The test results are displayed in slatter graph. Keywords - Data Mining, K-Means Clusttering, Zakat

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VILLAGE GROUPING BASED ON THE NUMBER OF HEALTH FACILITIES IN WEST JAVA USING K-MEANS CLUSTERING ALGORITHM

Jurnal Riset Informatika ◽

10.34288/jri.v4i1.300 ◽

2021 ◽

Vol 4 (1) ◽

pp. 71-78

Author(s):

Frieyadie Frieyadie ◽

Anggie Andriansyah ◽

Tyas Setiyorini

Keyword(s):

Data Mining ◽

Data Processing ◽

Clustering Algorithm ◽

National Development ◽

Health Facilities ◽

West Java ◽

The Future ◽

The Government

Health is very important for the welfare and development of the Indonesian nation because as a capital for the implementation of national development, it is essentially the development of all Indonesian people and the development of all Indonesian people. Due to the outbreak of the Covid-19 virus, many health facilities must be provided for patients. Of course, the government must pay attention to the health facilities that can be used in every district/city in West Java in the future. Therefore, to determine the level of availability of sanitation facilities in each district/city in West Java, we need a technology that can classify data correctly. One method of data processing in data mining is clustering. The application of clustering to this problem can use the K-Means algorithm method to group the most frequently used data. The purpose of this study is to classify sanitation data on the highest sanitation facilities, medium sanitation facilities, and low sanitation facilities, so that areas/cities that are included in the low cluster will receive more attention from the government to improve/provide sanitation facilities.

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Analysis of Data Mining Using K-Means Clustering Algorithm for Product Grouping

IJIIS: International Journal of Informatics and Information Systems ◽

10.47738/ijiis.v3i1.3 ◽

2020 ◽

Vol 3 (1) ◽

pp. 12-22

Author(s):

Mohammad Imron ◽

Uswatun Hasanah ◽

Bahrul Humaidi

Keyword(s):

Data Mining ◽

Data Processing ◽

Clustering Algorithm ◽

Processing Technology ◽

Clustering Method ◽

Data Mining Techniques ◽

Sales Data ◽

Using Data

Rizki Barokah Store is one of the stores that every day sell a variety of basic materials of daily necessities such as food, drinks, snacks, toiletries, and so on. However, some problems occur in the Rizki Barokah Store is often a build-up of product stocks that resulted in the product has expired. This is due to an error in making decisions on the product stock. In addition to these problems, with the amount of sales data stored on the database, the store has not done data mining and grouping to know the potential of the product. Whereas data-processing technology can already be done using data mining techniques. To overcome the period of the land, the technique used in data mining with the clustering method using the algorithm K-means. With the use of these techniques, the purpose of this research is to grouping products based on products of interest and less interest, advise on the stock of products, and know the products of interest and less demand.

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Encrypted Query Data Processing in Internet Of Things(IoTs) CryptDB and Trusted DB

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i8.735741 ◽

2018 ◽

Vol 6 (8) ◽

pp. 735-741

Author(s):

G. Ambika ◽

P. Srivaramangai

Keyword(s):

Internet Of Things ◽

Data Processing

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Teknik Data Mining Dalam Clustering Produksi Susu Segar Di Indonesia Dengan Algoritma K-Means

BRAHMANA: Jurnal Penerapan Kecerdasan Buatan ◽

10.30645/brahmana.v1i1.5 ◽

2019 ◽

Vol 1 (1) ◽

pp. 31-39

Author(s):

Ilham Safitra Damanik ◽

Sundari Retno Andani ◽

Dedi Sehendro

Keyword(s):

Data Mining ◽

Milk Production ◽

Clustering Algorithm ◽

Clustering Method ◽

Data Mining Techniques ◽

Low Level ◽

Fresh Milk ◽

Nutritional Needs ◽

High Level ◽

Level Cluster

Milk is an important intake to meet nutritional needs. Both consumed by children, and adults. Indonesia has many producers of fresh milk, but it is not sufficient for national milk needs. Data mining is a science in the field of computers that is widely used in research. one of the data mining techniques is Clustering. Clustering is a method by grouping data. The Clustering method will be more optimal if you use a lot of data. Data to be used are provincial data in Indonesia from 2000 to 2017 obtained from the Central Statistics Agency. The results of this study are in Clusters based on 2 milk-producing groups, namely high-dairy producers and low-milk producing regions. From 27 data on fresh milk production in Indonesia, two high-level provinces can be obtained, namely: West Java and East Java. And 25 others were added in 7 provinces which did not follow the calculation of the K-Means Clustering Algorithm, including in the low level cluster.

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K-MEANS CLUSTERING ALGORITHM FOR SERVICE DATA ANALYSIS BASED ON CUSTOMERS COMBINATION

Unes journal of Information System ◽

10.31933/ujis.3.1.001-007.2018 ◽

2018 ◽

Vol 3 (1) ◽

pp. 001

Author(s):

Zulhendra Zulhendra ◽

Gunadi Widi Nurcahyo ◽

Julius Santony

Keyword(s):

Data Mining ◽

Data Analysis ◽

Clustering Algorithm ◽

Customer Complaints ◽

Using Data ◽

Clustering Data ◽

Service Data ◽

Selection Of

In this study using Data Mining, namely K-Means Clustering. Data Mining can be used in searching for a large enough data analysis that aims to enable Indocomputer to know and classify service data based on customer complaints using Weka Software. In this study using the algorithm K-Means Clustering to predict or classify complaints about hardware damage on Payakumbuh Indocomputer. And can find out the data of Laptop brands most do service on Indocomputer Payakumbuh as one of the recommendations to consumers for the selection of Laptops.

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