scholarly journals Weather Forecasting in Bandung Regency based on FP-Growth Algorithm

2019 ◽  
Vol 4 (2) ◽  
pp. 1 ◽  
Author(s):  
Farida Nur Khasanah ◽  
Fhira Nhita
Keyword(s):  
Association Rule ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Data Entry ◽  
Frequent Pattern ◽  
The People ◽  
The World ◽  
Rule Method ◽  
Confidence Value ◽  
Weather Change

<p>Weather change is one of the things that can affect people around the world in doing activities, including in Indonesia. The area of Indonesia, especially in Bandung regency has a high intensity of rainfall, compared with other regions. The people of Bandung Regency mostly have livelihoods in the fields of industry and agriculture, both of which are closely related to the effects of weather. Weather prediction is used for reference, so the future of society can prepare all possible weather before the move. One method of data mining used to predict weather is the association rule method. In this method there is Frequent Pattern Growth (FP-Growth) algorithm, this algorithm is used to determine the pattern of linkage between attribute weather with rainfall. The result of the FP-Growth algorithm is an association rule, the result of the algorithm rules is then used as reference for data entry in the classification process, where the process is done to get the forecast based on the rainfall category to obtain maximum accuracy. The highest performance result of FP-Growth from the result of rules based on its confidence value is 92%.</p>

TEXT CLASSIFICATION USING MODIFIED MULTI CLASS ASSOCIATION RULE

2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Siti Sakira Kamaruddin ◽  
Yuhanis Yusof ◽  
Husniza Husni ◽  
Mohammad Hayel Al Refai
Keyword(s):  
Text Classification ◽  
Association Rule ◽  
Classification Accuracy ◽  
Classification Problem ◽  
Frequent Pattern ◽  
Associative Classification ◽  
Vertical Data ◽  
Rule Method ◽  
Class Association Rule ◽  
Two Stages

This paper presents text classification using a modified Multi Class Association Rule Method. The method is based on Associative Classification which combines classification with association rule discovery. Although previous work proved that Associative Classification produces better classification accuracy compared to typical classifiers, the study on applying Associative Classification to solve text classification problem are limited due to the common problem of high dimensionality of text data and this will consequently results in exponential number of generated classification rules. To overcome this problem the modified Multi-Class Association Rule Method was enhanced in two stages. In stage one the frequent pattern are represented using a proposed vertical data format to reduce the text dimensionality problem and in stage two the generated rule was pruned using a proposed Partial Rule Match to reduce the number of generated rules. The proposed method was tested on a text classification problem and the result shows that it performed better than the existing method in terms of classification accuracy and number of generated rules.

Penggunaan Association Rule Data Mining Untuk Menentukan Pola Lama Studi Mahasiswa F-MIPA UNSRAT

d'CARTESIAN ◽  
2014 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
M. Zainal Mahmudin ◽  
Altien Rindengan ◽  
Winsy Weku
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Large Data ◽  
Apriori Algorithm ◽  
Data Mining Algorithms ◽  
Adequate Information ◽  
Processing Data ◽  
Rule Method ◽  
Mining Algorithms ◽  
Confidence Value

Abstract The requirement of highest information sometimes is not balance with the provision of adequate information, so that the information must be re-excavated in large data. By using the technique of association rule we can obtain information from large data such as the college data. The purposes of this research is to determine the patterns of study from student in F-MIPA UNSRAT by using association rule method of data mining algorithms and to compare in the apriori method and a hash-based algorithms. The major’s student data of F-MIPA UNSRAT as a data were processed by association rule method of data mining with the apriori algorithm and a hash-based algorithm by using support and confidance at least 1 %. The results of processing data with apriori algorithms was same with the processing results of hash-based algorithms is as much as 49 combinations of 2-itemset. The pattern that formed between 7,5% of graduates from mathematics major that studied for more 5 years with confidence value is 38,5%. Keywords: Apriori algorithm, hash-based algorithm, association rule, data mining. Abstrak Kebutuhan informasi yang sangat tinggi terkadang tidak diimbangi dengan pemberian informasi yang memadai, sehingga informasi tersebut harus kembali digali dalam data yang besar. Dengan menggunakan teknik association rule kita dapat memperoleh informasi dari data yang besar seperti data yang ada di perguruan tinggi. Tujuan penelitian ini adalah menentukan pola lama studi mahasiswa F-MIPA UNSRAT dengan menggunakan metode association rule data mining serta membandingkan algoritma apriori dan algoritma hash-based. Data yang digunakan adalah data induk mahasiswa F-MIPA UNSRAT yang  diolah menggunakan teknik association rule data mining dengan algoritma apriori dan algoritma hash-based dengan minimum support 1% dan minimum confidance 1%. Hasil pengolahan data dengan algoritma apriori sama dengan hasil pengolahan data dengan algoritma hash-based yaitu sebanyak 49 kombinasi 2-itemset. Pola yang terbentuk antara lain 7,5% lulusan yang berasal dari jurusan matematika menempuh studi selama lebih dari     5 tahun dengan nilai confidence 38,5%. Kata kunci : Association rule data mining, algoritma apriori, algoritma hash-based

scholarly journals Market Basket Analysis to Identify Customer Behaviours by Way of Transaction Data

2017 ◽  
Vol 1 (1) ◽  
pp. 20 ◽  
Author(s):  
Fachrul Kurniawan ◽  
Binti Umayah ◽  
Jihad Hammad ◽  
Supeno Mardi Susiki Nugroho ◽  
Mochammad Hariadi
Keyword(s):  
Association Rule ◽  
Market Basket Analysis ◽  
Data Mining Technique ◽  
Market Basket ◽  
Transaction Data ◽  
New Information ◽  
Analysis Application ◽  
Rule Method ◽  
Confidence Value

Transaction data is a set of recording data result in connections with sales-purchase activities at a particular company. In these recent years, transaction data have been prevalently used as research objects in means of discovering new information. One of the possible attempts is to design an application that can be used to analyze the existing transaction data. That application has the quality of market basket analysis. In addition, the application is designed to be desktop-based whose components are able to process as well as re-log the existing transaction data. The used method in designing this application is by way of following the existing steps on data mining technique. The trial result showed that the development and the implementation of market basket analysis application through association rule method using apriori algorithm could work well. With the means of confidence value of 46.69% and support value of 1.78%, and the amount of the generated rule was 30 rules.

scholarly journals Data: The New Currency of The Digital World and the Race among Nations to Protect Data

2019 ◽  
Vol 8 (2S6) ◽  
pp. 618-620
Keyword(s):  
Good Governance ◽  
Weather Prediction ◽  
Climate Modelling ◽  
The European Union ◽  
Digital World ◽  
The People ◽  
The World ◽  
General Data ◽  
Greater Good ◽  
The Government

This article focuses on the significance of the data with the advancements in the technology and its consequent implications on various sectors. With nations around the world and especially India concentrating on digitalizing all the aspects of life, it is important to secure the data that will be created because of its digitalization. India's flagship program DIGITAL INDIA makes it evident how important is digitalizing for the welfare of the nation. The article has described the importance of data analyzing in maximizing the efficiency, profitability, the productivity of companies and also how it helps the Government with good Governance by reducing the leakages in subsidy transfer, identifying the beneficiaries of the welfare schemes, etc. Another aspect regarding climate modelling and weather prediction, which was made possible because of the availability of the data also has been described. Finally, how countries are trying to safeguard the domestically generated data in the form of regulations such as the General Data Protection Regime of the European Union are also discussed. Eventually, it also proposes how the various stakeholders should come in together and resolve the differences among them for the greater good of the people around the globe.

scholarly journals THE OPTIMIZE OF ASSOCIATION RULE METHOD FOR THE BEST BOOK PLACEMENT PATTERNS IN LIBRARY: A MONTHLY TRIAL

TEKNOKOM ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 53-59
Author(s):  
T. Husain ◽  
Nuzulul Hidayati
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Research Method ◽  
Information Service ◽  
User Needs ◽  
Research Approach ◽  
Sources Of Information ◽  
Rule Method ◽  
Confidence Value

Data mining is the process of finding interesting patterns and knowledge from large amounts of data. Sources of information service, especially in the library, include books, reference books, serials, scientific gray literature (newsletters, reports, proceedings, dissertations, theses, and others). The importance of this research being carried out in the library in this study aims to implement data mining with the association rule method to solve problems, especially in the placement of shelves based on the category of the printed version of the book collection. This research method uses a qualitative research approach. Data was collected using documentation techniques and deep analysis of existing weaknesses to identify user needs whose information was obtained through observation and interviews with key informants (admin, user, etc.). For example, the determination of the best book placement patterns can be done by looking at the results of the tendency of visitors to borrow books based on a combination of 2 item sets with 60 percent of confidence value every month or week and must be evaluated or take a calculate again.

scholarly journals Application of Association Rule Method Using Apriori Algorithm to Find Sales Patterns Case Study of Indomaret Tanjung Anom

2021 ◽  
Vol 1 (2) ◽  
pp. 54-66
Author(s):  
M. Hamdani Santoso
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
A Priori ◽  
Apriori Algorithm ◽  
Rule Discovery ◽  
Association Rule Discovery ◽  
Rule Method ◽  
Confidence Value ◽  
Method Show

Data mining can generally be defined as a technique for finding patterns (extraction) or interesting information in large amounts of data that have meaning for decision support. One of the well-known and commonly used association rule discovery data mining methods is the Apriori algorithm. The Association Rule and the Apriori Algorithm are two very prominent algorithms for finding a number of frequently occurring sets of items from transaction data stored in databases. The calculation is done to determine the minimum value of support and minimum confidence that will produce the association rule. The association rule is used to produce the percentage of purchasing activity for an itemset within a certain period of time using the RapidMiner software. The results of the test using the priori algorithm method show that the association rule, that customers often buy toothpaste and detergents that have met the minimum confidence value. By searching for patterns using this a priori algorithm, it is hoped that the resulting information can improve further sales strategies.

scholarly journals Implementasi Metode Association Rule untuk Menganalisis Data Twitter tentang Badan Penyelenggara Jaminan Sosial dengan Algoritma Frequent Pattern-Growth

2018 ◽  
Vol 8 (1) ◽  
pp. 25
Author(s):  
Jemaictry Tamaela ◽  
Eko Sediyono ◽  
Adi Setiawan
Keyword(s):  
Social Media ◽  
Association Rule ◽  
Visual Information ◽  
Frequent Pattern ◽  
Frequency Of Occurrence ◽  
Text Data ◽  
The People ◽  
The Social ◽  
New Knowledge ◽  
Before And After

BPJS services cannot be separated from criticism and complaints of the people in Indonesia. Twitter is one of the social media choose to share experiences related to things about BPJS. The information that is shared can be processed to gain new knowledge (knowledge discovery), which is related to public opinion about BPJS. Tweets collected from the national BJPS twitter are divided into words, then, specified words can be used as items to form the itemset. The association rule technique with the FP-Growth algorithm that is implemented in the application can process text data from Twitter to form the item set. Each item set contains a collection of tweets that are responses and the opinion of the community about an event or phenomenon related to BPJS services. The tree structure of FP-Growth simplifies the process of the validation because it can track and display the frequency of occurrence of each word and itemset, before and after branch pruning which is not included in the support value. The OSM API integration with the application in this study provides visual information about where the tweet comes from, so it can be used to generate itemset from a collection of tweets from a particular region.

A performance evaluation of hybrid genetic algorithm approach for forecasting tropical cyclone categories

2020 ◽  
pp. 002072091989107
Author(s):  
Karthick S ◽  
Malathi D
Keyword(s):  
Genetic Algorithm ◽  
Tropical Cyclone ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Hybrid Genetic Algorithm ◽  
Significant Challenge ◽  
Natural Catastrophes ◽  
The People ◽  
Proposed Model ◽  
Genetic Algorithm Approach

One of the most and essential parts of weather forecasting is the prediction of a tropical cyclone. All over the world there are weather prediction stations to analyze the natural disasters for safeguarding the people before they would get any damage. Cyclone is one of the dangerous natural catastrophes that several researchers have undergone research over it, and many technologies were developed to find accurate results for its prediction. Several algorithms were proposed for classifying the cyclone data in terms of latitude, longitude, wind speed, and pressure. Still, it is a significant challenge for most of the researchers for predicting the accurate measurement and observations for cyclonic data. In this paper, a hybrid model is proposed, which is a combination of Genetic Algorithm and XP boost for predicting cyclone severity. The data are collected from the Bay of Bengal Ocean, which has been used in the proposed model for classification. Simulation results show a better improvement in predicting the tropical cyclone categories by using the proposed model. Furthermore, comparison of other existing algorithms with the proposed technique is also discussed.

scholarly journals Data Mining Menggunakan Algoritma Apriori untuk Rekomendasi Produk bagi Pelanggan

2019 ◽  
Vol 7 (3) ◽  
pp. 103-108
Author(s):  
Ariefana Ria Riszky ◽  
Mujiono Sadikin
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Marketing Strategy ◽  
Association Rule ◽  
Apriori Algorithm ◽  
Processing Data ◽  
Total Data ◽  
Product Recommendations ◽  
Rule Method ◽  
Confidence Value

The implementation of a marketing strategy requires a reference so that promotion can be on target, such as by looking for similarities between product items. This study examines the application of the association rule method and apriori algorithm to the purchase transaction dataset to assist in forming candidate combinations among product items for customer recommended product promotion. The purchase transaction dataset was collected in October and November 2018 with a total data of 1027. In the experiment, the minimum value of support is 85%, and the minimum confidence value is 90% by processing data using the Weka software 3.9 version. Apriori algorithm can form association rules as a reference in the promotion of company products and decision support in providing product recommendations to customers based on defined minimum support and confidence values.

scholarly journals Aplikasi Web Usage Mining Menggunakan Metode Association Rule Dengan Algoritma Fp-Growth Untuk Mengetahui Pola Browsing Pengunjung Website

2019 ◽  
Vol 1 ◽  
pp. 1060
Author(s):  
Yori Apridonal M ◽  
Febri Dristyan ◽  
Afdhal Syafnur
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Layout Design ◽  
Association Pattern ◽  
Processing Data ◽  
Alternative Algorithm ◽  
Rule Method ◽  
Using Data ◽  
Confidence Value ◽  
The Web

As a way to improve the promotion of institutions via the web, there is a need for a method to view browsing patterns of visitors on the site unilak.ac.id, thereby showing the user's interest in the links he visits. Data mining or knowledge discovery is a process of extracting valuable information by analyzing the existence of certain patterns or relationships. To find visitor patterns in the form of association rules is to use the association rule method. FP-Growth is an alternative algorithm that can be used to determine the most frequent set of data in a set of data. FP-Growth is applied to get a pattern of visitors, about what links are frequently visited and seen by visitors on the site unilak.ac.id. This pattern is used to help web administrators in developing the site unilak.ac.id by utilizing knowledge from the association pattern to regulate the layout / layout design of the categories available on the site unilak.ac.id. From the results of processing the dataset with FP-Growth algorithm and processing data processed using data mining software, namely Rapidminer 6.5. It was found that the minimum value of support was 1% and the minimum confidence value of 50% resulted in 124 rules of association.

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