scholarly journals Aturan Asosiasi Antar Item Terjual pada Data Penjualan Minimarket Milik Komunitas di Hari Besar Tertentu Menggunakan Algoritma Apriori

2021 ◽  
Vol 9 (2) ◽  
pp. 208-215
Author(s):  
Luky Fabrianto ◽  
Novianti Madhona Faizah ◽  
Johan Hendri Prasetyo ◽  
Bobby Suryo Prakoso ◽  
Gani Wiharso
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
A Priori ◽  
Threshold Value ◽  
Frequent Items ◽  
Mining Methods ◽  
Rule Method ◽  
One Year ◽  
The Relationship

The popular data mining methods to find the relationship between an item and another item is the association rule method using A Priori algorithm, this method is precise to generate a pattern of relationship rules between the types of items sold based on sales data. Support values ​​on frequent items and confidence in the rules obtained can be an actionable insight that can be follow up by minimarket managers, cooperatives and etc. The categorization of product types in minimarkets is much while the total number of transactions in one year is also very large, but the number of types of items sold in a transaction is very few, thus the threshold value cannot be high. In this study, the association rule method was carried out per event or certain period related to Muslim holidays, the highest rule was obtained is Makanan ringan => Sembako with 46% confidence and 16% support which occurred in the month of Ramadan.

scholarly journals Implementasi Algoritma Apriori Pada Penyusunan Menu Makanan Rumah Makan Prasmanan

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Asep Budiman Kusdinar ◽  
Daris Riyadi ◽  
Asriyanik Asriyanik
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
High Frequency ◽  
A Priori ◽  
Apriori Algorithm ◽  
Frequency Pattern ◽  
Data Mining Techniques ◽  
Rule Method

A buffet restaurant is a restaurant that provides buffet food that is served directly at the dining table so that customers can order more food according to their needs. This study uses the association rule method which is one of the methods of data mining and a priori algorithms. Data mining is the process of discovering patterns or rules in data, in which the process must be automatic or semi-automatic. Association rules are one of the techniques of data mining that is used to look for relationships between items in a dataset. While  the apriori algorithm is a very well-known algorithm for finding high-frequency patterns, this a priori algorithm is a type of association rule in data mining. High- frequency patterns are patterns of items in the database that have frequencies or support. This high-frequency pattern is used to develop rules and also some other data mining techniques. The composition of the food menu in the Asgar restaurant is now arranged randomly without being prepared on the food menu between one another. The result of this research is  to support the composition of the food menu at the Asgar restaurant so that it is easier to take food menu with one another.  

scholarly journals Rancang Bangun Aplikasi Data Mining pada Penjualan Distro Bloods Berbasis Web menggunakan Algoritma Apriori

2021 ◽  
Vol 4 (2) ◽  
pp. 26
Author(s):  
Muhammad Muttaqin Muchlis ◽  
Iskandar Fitri ◽  
Rini Nuraini
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Association Rule ◽  
Web Applications ◽  
A Priori ◽  
Sales Data ◽  
Development Life Cycle ◽  
Technological Developments ◽  
Rule Method ◽  
Purchasing Patterns

The design of this data mining application is a computerized system in the field of technology, this proves that technological developments in data processing are increasingly advanced, this can be the basis for the development of data processing systems for sales of bloods based web applications using a priori algorithms, problems in this bloods distribution cannot Minimizing the decline in sales at the Jakarta clothing event in 2019, it is necessary to evaluate the sales data, with market basket analysis or consumer shopping baskets to find out consumer shopping patterns as a reference for the sale strategy of event Jakarta clothing at the end of the year. This analysis uses a priori algorithm with the association rule method, while the SDLC (Software Development Life Cycle) method is used as the basis for developing expert systems. From the results of the study, it was found that sales data for 5 days and 7 items got the highest 100% confidence value from the itemset calculation 1,2,3 which passed the selection so that they became aware of consumer purchasing patterns and rearranged product layouts for promotion and improving the correct sales strategy.Keywords:Applications, Data Mining, Apriori Algorithms, Association Rule Method, SDLC.

scholarly journals Analisa Association Rule Pada Algoritma Apriori Untuk Minat Pembelian Alat Kesehatan

2021 ◽  
Vol 5 (1) ◽  
pp. 280
Author(s):  
Andi Rahmadsyah ◽  
Hartono Hartono ◽  
Rika Rosnelly
Keyword(s):  
Data Mining ◽  
Medical Devices ◽  
Association Rule ◽  
A Priori ◽  
Mining Method ◽  
Market Basket Analysis ◽  
Business World ◽  
Market Basket ◽  
Analysis Techniques ◽  
The Relationship

In the competition in the business world, especially the Medical Device industry, it requires developers to find an accurate strategy that can increase sales of goods. One way to overcome this problem is to continue to provide various types of medical devices in the warehouse. To find out what medical devices are purchased by consumers, market basket analysis techniques are carried out, namely analysis of consumer buying habits. In order to make it easier for companies to determine Buyers' interest in medical devices, a data mining method is needed which is accompanied by an a priori algorithm based on the purchasing process carried out by consumers based on the relationship between the products purchased. Based on the sample sales data for medical devices CV Andira Karya Jaya, amounting to 25 transactions and in this study a minimum support = 12% and a minimum confidence = 70% will be used. In the final stage, the results obtained are medical devices that are in demand by buyers at CV. Andira Karya Jaya, namely 1 M3 oxygen cylinder and 1 M3 troley of oxygen. Based on this data, CV. Andira Karya Jaya can provide supplies of medical devices that are of interest to buyers.

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 PEMANFAATAN DATA MINING UNTUK PENEMPATAN BUKU DI PERPUSTAKAAN MENGGUNAKAN METODE ASSOCIATION RULE

2019 ◽  
Vol 8 (2) ◽  
pp. 59-63
Author(s):  
Eka Sabna
Keyword(s):  
Data Mining ◽  
Data Collection ◽  
Data Processing ◽  
Association Rule ◽  
Research Data ◽  
Rule Method ◽  
Hidden Data ◽  
The Relationship

Abstract                     Data mining is the process of analyzing data to find a pattern of hidden data collection by utilizing library visit data, can dig up information about what books are often borrowed by visitors and the relationship between each borrower to be able to compile and layout books. To be able to place books in accordance with the needs of members, it is necessary to process a data processing of book borrowing using the Associtation Rule method so that support and confidence can be found between books that are often borrowed so that patterns can be identified. The research data were obtained from book lending data at the Hang Tuah Pekanbaru STIKes Library. In processing the data using the Rapidminer software with the Association Rule method with support = 0.6 and confidence = 0.9. The resulting rule to be used as a book placement pattern consists of 7 patterns / rules. Keywords : Data Mining, Library, Association Rule.   Abstrak Data mining merupakan proses analisa data untuk menemukan suatu pola dari kumpulan data yang tersembunyi dengan memanfaatkan data kunjungan perpustakaan, dapat menggali informasi tentang buku-buku apa yang sering dipinjam oleh pengunjung dan keterkaitan antar masing-masing peminjam hingga dapat melakukan penyusunan  dan tata letak buku. Untuk dapat melakukan penempatan buku sesuai dengan kebutuhan anggota perlu dilakukan suatu proses pengolahan data peminjaman buku dengan menggunakan metode Associtation  Rule agar dapat diketahui support dan confidence antara buku-buku yang sering di pinjam sehingga dapat diketahui pola penempatan buku. Data penelitian diperoleh dari data peminjaman buku di Perpustakaan STIKes Hang Tuah Pekanbaru. Dalam pengolahan data nya menggunakan software Rapidminer dengan metode  Association Rule dengan nilai support = 0.6 dan confidence = 0,9. Rule yang dihasilkan untuk dijadikan pola penempatan buku terdiri dari 7 pola/rule.   Kata Kunci : Data Mining, perpustakaan, Assocition Rule

scholarly journals Macronutrient Quality and All-Cause Mortality in the SUN Cohort

Nutrients ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 972
Author(s):  
Susana Santiago ◽  
Itziar Zazpe ◽  
Cesar I. Fernandez-Lazaro ◽  
Víctor de la de la O ◽  
Maira Bes-Rastrollo ◽  
...  
Keyword(s):  
Quality Index ◽  
Inverse Relationship ◽  
A Priori ◽  
The Sun ◽  
Significant Inverse Relationship ◽  
Carbohydrate Quality ◽  
All Cause Mortality ◽  
Middle Aged Adults ◽  
The Relationship

No previous study has assessed the relationship between overall macronutrient quality and all-cause mortality. We aimed to prospectively examine the association between a multidimensional macronutrient quality index (MQI) and all-cause mortality in the SUN (Seguimiento Universidad de Navarra) (University of Navarra Follow-Up) study, a Mediterranean cohort of middle-aged adults. Dietary intake information was obtained from a validated 136-item semi-quantitative food-frequency questionnaire. We calculated the MQI (categorized in quartiles) based on three quality indexes: the carbohydrate quality index (CQI), the fat quality index (FQI), and the healthy plate protein source quality index (HPPQI). Among 19,083 participants (mean age 38.4, 59.9% female), 440 deaths from all causes were observed during a median follow-up of 12.2 years (IQR, 8.3–14.9). No significant association was found between the MQI and mortality risk with multivariable-adjusted hazard ratio (HR) for the highest vs. the lowest quartile of 0.79 (95% CI, 0.59–1.06; Ptrend = 0.199). The CQI was the only component of the MQI associated with mortality showing a significant inverse relationship, with HR between extreme quartiles of 0.64 (95% CI, 0.45–0.90; Ptrend = 0.021). In this Mediterranean cohort, a new and multidimensional MQI defined a priori was not associated with all-cause mortality. Among its three sub-indexes, only the CQI showed a significant inverse relationship with the risk of all-cause mortality.

scholarly journals Implementation of Apriori Algorithm Data Mining for Increase Sales

SinkrOn ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 17
Author(s):  
Reza Alfianzah ◽  
Rani Irma Handayani ◽  
Murniyati Murniyati
Keyword(s):  
Data Mining ◽  
Business Strategy ◽  
A Priori ◽  
Data Mining Algorithm ◽  
Sales Data ◽  
The Right ◽  
Or Organization ◽  
Product Sales ◽  
The Relationship ◽  
Confidence Value

Any company or organization that wants to survive needs to determine the right business strategy. The product sales data carried out by Lakoe Dessert Pondok Kacang will eventually result in a pile of data, so it is unfortunate if it is not re-analyzed. The products offered vary with a wide variety of products as many as 45 products, to find out the products with the most sales and the relationship between one product and another, one of the algorithms is needed in the data mining algorithm, namely the a priori algorithm to find out, and with the help of the Rapidminer 5 application, with a support value 2,4% and a confidence value 50%, products that customers often buy or are interested in can be found. This study used sales data for March 2020, which amounted to 209 transaction data. From the research, it was found that the item with the name Pudding Strawberry and Pudding Vanilla was the product most purchased by consumers. With knowledge of the most sold products and the patterns of purchasing goods by consumers, Lakoe Dessert Pondok Kacang can develop marketing strategies to market other products by analyzing the profits from selling the most sold products and anticipating running out or empty of stock or materials at a later date.

scholarly journals Chromosome 10 Abnormality Predicts Prognosis of Neuroblastoma Patients With Bone Marrow Metastasischromosome 10 Abnormality Predicts Prognosis of Neuroblastoma Patients With Bone Marrow Metastasis

2021 ◽  
Author(s):  
Chiyi Jiang ◽  
Xiao Xu ◽  
Binglin Jian ◽  
Xue Zhang ◽  
Zhixia Yue ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Clinical Characteristics ◽  
Chromosomal Abnormalities ◽  
Abnormal Chromosome ◽  
Mycn Amplification ◽  
Chromosome 10 ◽  
Orbital Metastases ◽  
One Year ◽  
The Relationship

Abstract Background Neuroblastoma (NB) is the most common extracranial solid tumor in children with high heterogeneity and concealed onset. The mechanism for its occurrence and development has not been revealed. The purpose of this study was to summarize the clinical characteristics of children with NB and abnormal chromosome 10. To investigate the relationship between the number and structure of chromosome 10 abnormality and NB prognosis.MethodsWe used chromosome G-banding in the first diagnosis to evaluate the genetics of chromosomes in patients with NB, and follow up their clinical characteristics and prognosis. All participants were diagnosed with NB in Hematology Oncology Center, Beijing Children’s Hospital from May 2015 to December 2018, and were followed up for at least one year. ResultsOf all 150 patients with bone marrow metastases, 42 were clearly diagnosed with chromosomal abnormalities. There were 13 patients with chromosome 10 abnormalities definitely, and the loss of chromosome 10 was the most common decrease in the number of chromosomes. These 13 patient had higher LDH, lower OS and EFS than that of children in abnormal group without chromosome 10 abnormality. Eight patients both had MYCN amplification and 1p36 deletion. Two of them had optic nerve damage and no vision, and 1 had left supraorbital metastases five months after treatment. Among the 16 children with suspected chromosome 10 abnormalities, 3 also had orbital metastases. ConclusionsThe above results showed that chromosome 10 might be a new prognostic marker. MYCN amplification and 1p36 deletion may be related with chromosome 10 abnormalities in NB. And NB patients with abnormal chromosome 10 were prone to have orbital metastases.

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