scholarly journals Penerapan Data Mining Apriori Pada Persediaan Obat (Studi Kasus Apotek Rafif Farma Medan)

2019 ◽  
Vol 3 (2) ◽  
pp. 115
Author(s):  
Mardiah Mardiah
Keyword(s):  
Data Mining ◽  
Phase Analysis ◽  
Customer Service ◽  
Association Rule ◽  
High Frequency ◽  
A Priori ◽  
Inventory Systems ◽  
Frequent Pattern ◽  
Apriori Algorithm ◽  
Analysis Algorithm

<span><em>The importance of inventory systems at a pharmacy and the type of goods which</em><br /><span><em>are a top priority that must be in stock. It is useful to anticipate the void stuff. Due to the</em><br /><span><em>lack of inventory may affect customer service and asset to the pharmacy. Therefore, this</em><br /><span><em>study was conducted to help resolve those problems by designing a data mining</em><br /><span><em>application that serves to predict sales of the drug is needed most knowable a priori</em><br /><span><em>algorithm with the help of Tools Tanagra. One of the interesting association analysis</em><br /><span><em>phase analysis algorithm that generates a high frequency patterns (frequent pattern</em><br /><span><em>mining).</em><br /><span><em>Keywords: Data Mining, Apriori Algorithm, Association Rule</em></span></span></span></span></span></span></span></span><br /><br class="Apple-interchange-newline" /></span>

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 Advanced Data Mining of SSD Quality Based on FP-Growth Data Analysis

2021 ◽  
Vol 11 (4) ◽  
pp. 1715
Author(s):  
Jieh-Ren Chang ◽  
You-Shyang Chen ◽  
Chien-Ku Lin ◽  
Ming-Fu Cheng
Keyword(s):  
Data Mining ◽  
Customer Service ◽  
Flash Memory ◽  
Association Rule ◽  
Production Control ◽  
Frequent Pattern ◽  
Nand Flash ◽  
Growth Data ◽  
Nand Flash Memory ◽  
Service Strategy

Storage devices in the computer industry have gradually transformed from the hard disk drive (HDD) to the solid-state drive (SSD), of which the key component is error correction in not-and (NAND) flash memory. While NAND flash memory is under development, it is still limited by the “program and erase” cycle (PE cycle). Therefore, the improvement of quality and the formulation of customer service strategy are topics worthy of discussion at this stage. This study is based on computer company A as the research object and collects more than 8000 items of SSD error data of its customers, which are then calculated with data mining and frequent pattern growth (FP-Growth) of the association rule algorithm to identify the association rule of errors by setting the minimum support degree of 90 and the minimum trust degree of 10 as the threshold. According to the rules, three improvement strategies of production control are suggested: (1) use of the association rule to speed up the judgment of the SSD error condition by customer service personnel, (2) a quality strategy, and (3) a customer service strategy.

MARKETING STRATEGY FOR THE DETERMINATION OF STAPLE CONSUMER PRODUCTS USING FP-GROWTH AND APRIORI ALGORITHM

2019 ◽  
Vol 24 (3) ◽  
pp. 225-235
Author(s):  
Winda Widya Ariestya ◽  
Wahyu Supriyatin ◽  
Ida Astuti
Keyword(s):  
Data Mining ◽  
Marketing Strategy ◽  
A Priori ◽  
Decision Rules ◽  
Consumer Products ◽  
Frequent Pattern ◽  
Apriori Algorithm ◽  
Data Set ◽  
Integration Data ◽  
The Right

The demand for staple products that vary among customers makes it necessary for the store to determine how the marketing strategy should be. Data mining are known as KDD (Knowledge Discovery in Database) is to digging up valuable knowledge from the data. Research purpose is to identify the right marketing strategy to sales the goods. The marketing strategy is took by analyze how much consumers demand for basic needs. The algorithms used in this research are FP (Frequent Pattern)-Growth and A-priori Algorithm. Finding combinations patterns between itemset using the Association Rule. FP-Growth algorithm is an algorithm that been used to determining a set of data in a data set that often appears on the frequency of the itemset. the KDD stages study are data cleansing, data integration, data selection, data transformation, data mining, pattern evaluation and knowledge presentation. the Testing used Rapidminer software with a minimum confidence value of 0.6 and a minimum support of 0.45. FP-Growth algorithm obtained 5 rule conclusions while Apriori Algorithm obtained 3 rule conclusions. The FP-Growth algorithm make a better decision rules than a priori algorithms in determining of marketing strategies, because it produces more decisions on how the goods sold.

Batch Picking Method for Bookstore Distribution Center

2014 ◽  
Vol 1061-1062 ◽  
pp. 1213-1219
Author(s):  
Ya Ping Wu
Keyword(s):  
Data Mining ◽  
Integer Programming ◽  
Association Rule ◽  
High Frequency ◽  
Order Picking ◽  
Distribution Center ◽  
Apriori Algorithm ◽  
Distribution Centers ◽  
Distribution Operations

Order picking for bookstore distribution centers was studied. The batch picking method is proposed based on association rule data mining and 0-1 integer programming for the characteristics of bookstore distribution operations such as multi-variety, small-batch and high-frequency. The associations between orders is mined by apriori algorithm. Orders are grouped into batches by establishing and solving 0-1 integer programming to maximize the support value of association between orders. This model is applied in the bookstore distribution center and application results show the effectiveness of the proposed method.

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.

Analisa Algoritma Apriori Untuk Peminjaman Buku Pada Perpustakaan SMA 1 Silima Pungga-Pungga Parongil

2019 ◽  
Vol 4 (1) ◽  
pp. 154-160
Author(s):  
Oktaviani Manurung ◽  
Penda Sudarto Hasugian
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
High Frequency ◽  
A Priori ◽  
Apriori Algorithm ◽  
Minimum Support ◽  
Minimum Requirements ◽  
To Come

ABSTRACT The library has the role of helping students to love reading books. The availability of books in various fields motivates students to come to visit the library, students can read or borrow library books. For this reason, library officers apply the rules for visiting the library. The Apriori algorithm is a part of data mining, namely the search for high frequency patterns such as activities that often appear simultaneously. The pattern that will be analyzed is the pattern of borrowing any books that are often borrowed so that librarians know the information of books that are often borrowed. With the application of a priori algorithms, book data is processed to produce a book borrowing pattern. After all the high frequency patterns were found, then association rules were found that met the minimum requirements for associative confidence A → B minimum confidence = 25%. Rules for sequential final association based on minimum support and minimum confidence, if borrowing an IPA, then borrowing MTK Support = 15%, Confidence = 42.8%. Keywords:Patterns of borrow of books, Library, Apriori Algorithms

scholarly journals Penerapan Data Mining Menggunakan Algoritma Apriori untuk Menentukan Pola Penyebab Gelandangan dan Pengemis

2020 ◽  
Vol 7 (2) ◽  
pp. 229
Author(s):  
Wirta Agustin ◽  
Yulya Muharmi
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Urban Areas ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Itemset ◽  
Frequent Pattern ◽  
Data Sets ◽  
Apriori Algorithm ◽  
Data Set

<p class="Judul2">Gelandangan dan pengemis salah satu masalah yang ada di daerah perkotaan, karena dapat mengganggu ketertiban umum, keamanan, stabilitas dan pembangunan kota. Upaya yang dilakukan saat ini masih fokus pada cara penanganan gelandangan dan pengemis, belum untuk pencegahan. Salah satu cara yang bisa dilakukan adalah dengan menentukan pola usia gelandangan dan pengemis. Algoritma Apriori sebuah metode <em>Association Rule</em> dalam data mining untuk menentukan frequent itemset yang berfungsi membantu menemukan pola dalam sebuah data (<em>frequent pattern mining</em>). Perhitungan manual menggunakan algoritma apriori, menghasilkan pola kombinasi sebanyak 3 rules dengan nilai minimum <em>support</em> sebesar 30% dan nilai <em>confidence</em> tertinggi sebesar 100%. Pengujian penerapan Algoritma Apriori menggunakan aplikasi RapidMiner. RapidMiner salah satu software pengolahan data mining, diantaranya analisis teks, mengekstrak pola-pola dari data set dan mengkombinasikannya dengan metode statistika, kecerdasan buatan, dan database untuk mendapatkan informasi bermutu tinggi dari data yang diolah. Hasil pengujian menunjukkan perbandingan pola usia gelandangan dan pengemis yang berpotensi menjadi gelandangan dan pengemis. Berdasarkan hasil pengujian aplikasi RapidMiner dan hasil perhitungan manual Algoritma Apriori, dapat disimpulkan sesuai kriteria pengujian, bahiwa pola (rules) usia dan nilai confidence (c) hasil perhitungan manual Algoritma Apriori tidak mendekati nilai hasil pengujian menggunakan aplikasi RapidMiner, maka tingkat keakuratan pengujian rendah, yaitu 37.5 %.</p><p class="Judul2"> </p><p class="Judul2"><strong><em>Abstract </em></strong></p><p class="Judul2"><strong> </strong></p><p><em>Homeless and beggars are one of the problems in urban areas as they possibly disrupt public order, security, stability and urban development. The efforts conducted are still focusing on managing the existing homeless and beggars instead of preventing the potential ones. One of the methods used for solving this problem is Algoritma Apriori which determines the age pattern of homeless and beggars. Apriori Algorithm is an Association Rule method in data mining to determine frequent item set that serves to help in finding patterns in a data (frequent pattern mining). The manual calculation through Apriori Algorithm obtains combination pattern of 3 rules with a minimum support value of 30% and the highest confidence value of 100%. These patterns were refences for the incharged department in precaution action of homeless and beggars arising numbers. Apriori Algorithm testing uses the RapidMiner application which is one of data mining processing software, including text analysis, extracting patterns from data sets and combining them with statistical methods, artificial intelligence, and databases to obtain high quality information from processed data. Based on the results of the said testing, it can be concluded that the level of accuracy test is low, i.e. 37.5%.</em></p>

scholarly journals Data Mining Untuk Klasifikasi Tamu Hotel Dengan Algoritma Apriori

2019 ◽  
Vol 7 (1) ◽  
pp. 69-78
Author(s):  
Entin Sutinah ◽  
Nani Agustina ◽  
Randi Ashar Asmoro
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
A Priori ◽  
Apriori Algorithm ◽  
Hotel Management ◽  
Management Association ◽  
Record Data ◽  
Data Record ◽  
Operational Activities ◽  
Executive Suite

Abstract   Data from hotel activity is one of the assets of a hotel. The amount of data produced will increase with the day-to-day operational activities,. The large amount of data will be a problem if the hotel cannot process it. In this study, we will implement a priori algorithm to classify guest record data in Delua Hotel in Jakarta based on trends that arise from a category at a certain time in the process of checking in at Delua Hotels on every day, week, and month. Guest record data that is processed using itemset room type Superior Queen/Twin, Deluxe Room, Holywood Room, and Executive Suite. The results of this study are in the form of data used to predict the available rooms when preparing a room reservation and the results of this algorithm can also be used as a reference for the hotel in preparing the reservation room which is most often attracted by hotel visitors.   Keywords: Apriori Algorithm, hotel management, Association Rule   Abstrak   Data yang dimiliki suatu hotel merupakan salah satu aset dari suatu hotel tersebut. Dengan adanya kegiatan operasional sehari-hari akan semakin memperbanyak jumlah data yang dihasilkan. Jumlah data yang begitu besar justru akan menjadi masalah bila hotel tersebut tidak bisa mengolahnya. Dalam penelitian ini, akan mengimplementasikan algoritma apriori untuk mengklasifikasikan data record guest yang ada di Delua Hotel Jakarta berdasarkan kecenderungan yang muncul dari suatu kategori pada kurun waktu tertentu pada proses chek in pada Delua Hotel pada setiap hari, minggu, bulannya. Data record guest yang diolah menggunakan itemset room type Superior Queen/Twin, Deluxe Room, Holywood Room, dan Executive Suite. Hasil dari penelitian ini berupa data yang digunakan untuk memprediksikan room yang tersedia saat mempersiapkan reservation room dan hasil dari algoritma ini juga dapat dijadikan rujukan bagi pihak hotel dalam mempersiapkan reservation room yang paling sering di minati oleh pengunjung hotel.   Kata kunci: Algototma Apriori, manajemen hotel, aturan asosiasi

scholarly journals IMPLEMENTASI ALGORITMA APRIORI TERHADAP DATA PENJUALAN PADA PERUSAHAAN RETAIL

2019 ◽  
Vol 15 (1) ◽  
pp. 85-90 ◽  
Author(s):  
Jordy Lasmana Putra ◽  
Mugi Raharjo ◽  
Tommi Alfian Armawan Sandi ◽  
Ridwan Ridwan ◽  
Rizal Prasetyo
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
A Priori ◽  
Apriori Algorithm ◽  
Business World ◽  
Transaction Data ◽  
Retail Company ◽  
Special Strategy

The development of the business world is increasingly rapid, so it needs a special strategy to increase the turnover of the company, in this case the retail company. In increasing the company's turnover can be done using the Data Mining process, one of which is using apriori algorithm. With a priori algorithm can be found association rules which can later be used as patterns of purchasing goods by consumers, this study uses a repository of 209 records consisting of 23 transactions and 164 attributes. From the results of this study, the goods with the name CREAM CUPID HEART COAT HANGER are the products most often purchased by consumers. By knowing the pattern of purchasing goods by consumers, the company management can increase the company's turnover by referring to the results of processing sales transaction data using a priori algorithm

Sign in / Sign up

Export Citation Format

Share Document