Data Mining Teknik Association Rule Dengan Teknik Algoritma Apriori

Terus bertambahnya data transaksi yang dialami oleh Toserba BORMA Cipadung menyebabkan semakin menumpuknya data tersebut, namun pemanfaatannya belum maksimal, hanya digunakan sebagai laporan penjualan saja.
Dengan menggunakan Data mining data tersebut dapat lebih dimaksimalkan pemanfaatannya yaitu dengan mencari informasi yang tersembunyi dalam data tersebut, yaitu pola beli konsumen dalam berbelanja berupa kebiasaan suatu produk dibeli bersama dengan produk apa. Informasi ini dapat dijadikan salah satu referensi bagi manajer dalam menentukan tata letak barang yang optimal sebagai salah satu upaya untuk meningkatkan keunggulan dalam persaingan bisnis retail.
Teknik Data Mining yang digunakan adalah Association Rule yang mempunyai 2 parameter yaitu support dan confident dengan menerapkan Algoritma Frequent Pattern Growth. Hasil pencarian informasi dalam data transaksi BORMA dari tanggal 8 sampai dengan 9 April 2013 sebanyak 3.242 transaksi diperoleh informasi yaitu jika konsumen membeli Asesoris Komputer, maka akan membeli ATK dengan nilai support 4 % dan confident tertinggi yaitu 93%, dan jika konsumen membeli Mie Instant, maka akan membeli Susu Dalam Kemasan dengan nilai support tertinggi yaitu 30% dan nilai confident 33%.

An efficient way of extracting information from huge data repositories is an art. The extraction of similar patterns from a number of transactions is also an important feature of data mining. We have a lot data mining techniques to extract information. Here, we adopt " Apriory algorithm " , which is one of the popular algorithms mainly used for Association rule mining. The purpose of association rule mining is to find all sets of items that have minimum support specified by the User. These items are the elements of a set called frequent item set. The association rules are generated by using this frequent item set. In astrology, the main database used for prediction is the horoscope of person. The horoscope is a database contains data like the planetary position at the time of birth of an individual. In all the incidents related to a human being, the horoscope of that person will consider to find the chance of happening good and bad things related to that incident. Similarly in some situations we need to find a horoscope that similar to our horoscope. The astrology believers select a life partner who has the similar horoscope (Horoscope matching).In that type of situation, we can utilize the pattern recognition techniques to find the similar horoscopes. In this work, we are trying to study the existing association rule mining algorithm " Apriory " is sufficient or not to find the similar patterns in the horoscope of different Individuals.

Dengan adanya revolusi industri 4.0, persaingan bisnis semakin ketat khususnya dari sisi proses bisnis promosi, penjualan, dan transaksi. Sebuah sistem sangat diperlukan untuk meningkatkan efisiensi dan daya saing industri melalui media internet atau sering disebut E-Commerce.Industri handmade di Indonesia masih kurang berkembang dalam hal pemasaran barang-barang yang telah diproduksi dikarenakan kurangnya penyebaran informasi kepada masyarakat. Kebanyakan proses transaksi jual beli berupa mekanisme penjualan yang berjalan sekarang masih konvensional sehingga menyebabkan pangsa pasar terbatas. Dengan adanya perangkat lunak E-Commerce berbasis Market Basket Analysis, diharapkankualitas pelayanan kepada pelanggan khususnya dalam memberikan informasi pilihan produk sekaligus meningkatkan proses promosi dan pemasaran produk handmade kepada masyarakat.

Association rule mining (ARM) is the process of generating rules based on the correlation between the set of items that the customers purchase.Of late, data mining researchers have improved upon the quality of association rule mining for business development by incorporating factors like value (utility), quantity of items sold (weight) and profit. The rules mined without considering utility values (profit margin) will lead to a probable loss of profitable rules. The advantage of wealth of the customers' needs information and rules aids the retailer in designing his store layout[9]. An algorithm CSHURI, Customer Segmentation using HURI, is proposed, a modified version of HURI [6], finds customers who purchase high profitable rare items and accordingly classify the customers based on some criteria; for example, a retail business may need to identify valuable customers who are major contributors to a company's overall profit. For a potential customer arriving in the store, which customer group one should belong to according to customer needs, what are the preferred functional features or products that the customer focuses on and what kind of offers will satisfy the customer, etc., finds the key in targeting customers to improve sales [9], which forms the base for customer utility mining.

A course recommender system has a great importance in expecting the selection of courses by students in an university, especially for new students who can't easily select the proper elective courses offered for a specific semester. The computer science department in Ajloun University College at Balqa Applied University (BAU) will be taken as a case study. In this paper, an efficient cluster based rule mining algorithm will be used on a course database to describe a courses recommendation system that assist students to choose elective courses based on students already studied these courses or some of them.

Menjamurnya perusahaan retail di Indonesia membuat persaingan yang cukup ketat di bisnis ini. Ketersediaan informasi rinci produk yang dibeli dari setiap transaksi pelanggan di database akan menjadi sampah data, yang terus berkembang setiap harinya. Manajer perusahaan tentunya akan tertarik untuk mengetahui apakah kelompok-kelompok tertentu produk secara konsisten dibeli bersama-sama. Mereka bisa menggunakan data ini untuk pengelompokkan dan penempatan produk secara optimal, dan pihak manajer juga bisa  menggunakan informasi tersebut untuk cross-selling, untuk promosi, untuk desain katalog dan untuk mengidentifikasikan  segmen pelanggan berdasarkan pola pembelian. Dalam penelitian ini digunakan Data mining dengan teknik Association rule untuk menggali pola hubungan atribut-atribut dan frequent itemset dalam database Retail. Paradigma apriori digunakan untuk mencari large itemset dalam penetapan association rule. Integrasi association rule dengan paradigma apriori telah berhasil menemukan sejumlah pola hubungan antar atribut dalam database retail

Persediaan barang pada suatu usaha yang bergerak dalam bisang bisnis marketing harus mampu memastikan persediaan stok selalu tersedia. Maka dari itu ketersediaan barang menjadi hal yang krusial dalam penyediaan stok. Maka dari itu untuk mendukung perkembangan usaha diperlukan suatu fasilitas untuk menentukan persediaan stok sesuai dengan banyaknya permintaan. Menggali informasi berharga dari kumpulan data transaksi yang banyak disebut data mining atau  Knowledge Discovery in Database (KDD). Salah satu teknik data mining yang cukup banyak digunakan yaitu  algortima apriori. Untuk menentukan pola kombinasi antar itemset digunakan  Association Rules. Dengan adanya algoritma apriori diharapkan dapat memfasilitasi penentuan persediaan stok pada suatu usaha.

Apriori algorithm use an iterative approach where k-itemset used to explore (k+1)-itemset. (K+1)-itemset candidates containing subset frequency that rarely appears will not used in determining association rules. Association rules formed by "if antecedent then consequent". Implementation of the apriori algorithm was preceded by the preparation of transactions database and determination of minimum support and confidence. Apriori algorithm scanning database repeated, pair one item to another and record the number of occurrences in the overall transaction. Frequent itemset is determined by selecting a combination or itemset that the count value greater than or equal to the minimum support then calculated the percentage value of support and confidence of each candidate. The association rules selected from which fit the minimum support and confidence. Data used in this study was sample of 100 transactions from database point if sales. Final Association rules are obtained from implementation of apriori algorithm is "if HELLO PANDA REFIL then HELLO PANDA 10gr" with percentage of support 2.00% and confidence 100.00% and "if HELLO PANDA 10gr then HELLO PANDA REFIL" with percentage of support 2.00% and confidence 100.00%. So it can be concluded that most customers buy HELLO PANDA REFIL will also buy HELLO PANDA 10gr so goes otherwise. This study proves that the apriori algorithm suitable implemented to search the frequent itemset in the shopping cart. Association rules that formed of frequent itemset can be used as decision support in sales. Intisari-Algoritma apriori menggunakan pendekatan iteratif dimana k-itemset digunakan untuk mengeksplorasi (k+1)-itemset. Calon (k+1)-itemset yang mengandung frekuensi subset yang jarang muncul tidak dipakai menentukan aturan asosiasi. Aturan asosiasi berbentuk if antecedent then consequent. Implementasi algoritma apriori didahului dengan persiapan database transaksi serta penentuan batas minimum support dan confidence. Algoritma apriori melakukan scaning database berulang-ulang, memasangkan satu item dengan item lainnya dan mencatat jumlah kemunculan kombinasi dalam keseluruhan transaksi. Frequent itemset ditentukan dengan memilih itemset yang nilai kemuculannya diatas atau sama dengan nilai minimum support kemudian dihitung persentase nilai support dan confidencenya. Aturan asosiasi yang berlaku dipilih dari yang memenuhi syarat minimum support dan confidence. Data yang digunakan dalam penelitian ini adalah sampel 100 transaksi dari database point of sales. Aturan asosiasi final yang diperoleh dari penerapan algoritma apriori adalah "if HELLO PANDA REFIL then HELLO PANDA 10GR" dengan persentase support 2,00% dan confidence 100,00% dan aturan "if HELLO PANDA 10GR then HELLO PANDA REFIL" dengan persentase support 2,00% dan confidence 100,00%. Jadi dapat disimpulkan kebanyakan pelanggan membeli item HELLO PANDA REFIL juga akan membeli item HELLO PANDA 10GR begitu juga berlaku sebaliknya. Penelitian ini membuktikan bahwa algoritma apriori cocok diimplementasikan untuk mencari frequent itemset pada keranjang belanja. Aturan asosiasi yang dibentuk dari frequent itemset tersebut dapat dipakai sebagai pendukung keputusan dalam penjualan. Kata Kunci-Data Mining, Association Rules, Apriori Algorithm, Frequent Itemset, Item Combination, keranjang belanja

Apriori is one of the key algorithms to generate frequent itemsets. Analysing frequent itemset is a crucial step in analysing structured data and in finding association relationship between items. This stands as an elementary foundation to supervised learning, which encompasses classifier and feature extraction methods. Applying this algorithm is crucial to understand the behaviour of structured data. Most of the structured data in scientific domain are voluminous. Processing such kind of data requires state of the art computing machines. Setting up such an infrastructure is expensive. Hence a distributed environment such as a clustered setup is employed for tackling such scenarios. Apache Hadoop distribution is one of the cluster frameworks in distributed environment that helps by distributing voluminous data across a number of nodes in the framework. This paper focuses on map/reduce design and implementation of Apriori algorithm for structured data analysis.

Breast cancer is one of the leading cancers for women in developed countries including India. It is the second most common cause of cancer death in women. The high incidence of breast cancer in women has increased significantly in the last years. In this paper we have discussed various data mining approaches that have been utilized for breast cancer diagnosis and prognosis. Breast Cancer Diagnosis is distinguishing of benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast Cancer is to recur in patients that have had their cancers excised. This study paper summarizes various review and technical articles on breast cancer diagnosis and prognosis also we focus on current research being carried out using the data mining techniques to enhance the breast cancer diagnosis and prognosis.

Multi-agent systems underpin the vision for ambient intelligence. However, developing multi-agent systems is a complex and challenging process. For example, pervasive computing has been found susceptible to instability, due to unwanted behaviour arising from unplanned interaction between rule based agents. This instability is impossible to predict, as it depends on the rules of interaction, the initial state of the system, the user interaction, and in the time delay of the system (due to network traffic, different speed of processing, etc). In this paper we present a theoretical framework, an Interaction Network (IN), together with a communication locking strategy that we call INPRES (Instability Prevention System) that can be used to identify and eliminate this problem. In addition we describe a Multi-Dimensional Model (MDM) to represent the agents and the state of each agent over time. A theorem showing the role of delays in an unstable system is presented. We present experimental...

ACK merupakan franchise makanan cepat saji yang berpusat di Badung Bali dengan lebih dari 200 cabang yang tersebar di seluruh Bali. Sebagian besar franchise ACK yang tersebar masih menggunakan sistem yang manual sehingga informasi mengenai penjualan dan stok tidak terintegrasi serta mengharuskan karyawan menghitung transaksi inventory secara manual yang memungkinkan terjadinya kesalahan. Teknologi digital menjadikan proses bisnis menjadi efisiensi dan efektiftas serta telah terbukti dimasyarakat, seperti hotel yang bekerja sama dengan aplikasi travel berbasis online seperti Traveloka dan Trivago untuk membuat pemesanan kamar di hotel yang semakin mudah dan efisien (Danuri, 2019). Tak terkecuali franchice ACK, Digitalisasi proses bisnis dengan memanfaatkan business intelligence menjadikan peluang baru yang lebih memudahkan perusahaan untuk mengelola bisnisnya. Upaya digitalisasi proses bisnis pada franchice ACK dapat dilakukan dengan menggunakan teknologi berbasis web. Teknologi website memungkinkan perusahaan memanfaatkan sistem tanpa perlu melakukan instalasi aplikasi serta dapat diakses multi platform dan multi sistem operasi. Teknologi Website yang dibangun pada franchise ACK menggunakan Algoritma Apriori untuk memberikan rekomendasi terhadap produk yang dapat ditawarkan kepada pelanggan. Rekomendasi produk didapatkan dengan melakukan perhitungan terhadap data transaksi yang telah terjadi sebelumnya. Sumber data didapat dari salah satu outlet ACK cabang Keramas, Gianyar. Diharapkan sistem rekomendasi pada franchise ACK dapat memberikan dampak secara langsung terhadap peningkatan penjualan pada outlet ACK serta memberikan efisiensi dan transparansi inventaris didalamnya.

This paper represents the results of the research, which have allowed us to develop a hybrid approach to the processing, classification, and control of traffic routes. The approach enables to identify traffic flows in the virtual data center in real-time systems. Our solution is based on the methods of data mining and machine learning, which enable to classify traffic more accurately according to more criteria and parameters. As a practical result, the paper represents the algorithmic solution of the classification of the traffic flows of cloud applications and services embodied in a module for the controller of the software-defined network. This solution enables to increase the efficiency of handling user requests to cloud applications and reduce the response time, which has a positive effect on the quality of service in the network of the virtual data center.

Business intelligence (BI) is formulation of business strategies which help organizations to achieve its objectives and to predict its future. Data mining is often referred as BI in the domain of business. One of the major tasks in data mining is Association Rule Mining (ARM). ARM techniques incorporated in BI systems can be utilized in business decision-making such as retail shelf management, catalog design, customer segmentation, cross-selling, quality improvement and bundling products marketing. ARM technique is used for the identification of frequent itemsets from huge databases and then generating strong association rules by considering each item having same value. But in a large number of real world applications, items have different values according to their impact on the respective decision making processes. Traditional ARM techniques cannot fulfil the arising demands from these applications. The data mining researchers are continuously improving the quality of ARM technique...

A new methodology is developed to analyse existing water quality monitoring networks. This methodology incorporates different aspects of monitoring, including vulnerability/probability assessment, environmental health risk, the value of information, and redundancy reduction. The work starts with a formulation of a conceptual framework for groundwater quality monitoring to represent the methodology’s context. This work presents the development of Bayesian techniques for the assessment of groundwater quality. The primary aim is to develop a predictive model and a computer system to assess and predict the impact of pollutants on the water column. The process of the analysis begins by postulating a model in light of all available knowledge taken from relevant phenomenon. The previous knowledge as represented by the prior distribution of the model parameters is then combined with the new data through Bayes’ theorem to yield the current knowledge represented by the posterior distribution of model parameters. This process of updating information about the unknown model parameters is then repeated in a sequential manner as more and more new information becomes available.