Analisa Algoritma Apriori dengan Association Rule Untuk Rekomendasi Promosi Produk Elektronik Di Toko UD Surya Kisaran

Abstract: Data Mining is a term used to describe the processes in each itemset to be able to find the results of each item. Analysis is used to determine the promotion of electronic products, namely the a priori algorithm association rules, therefore UD Surya Elektronik Shop for increasing sales results must have other strategies to be able to improve the sales system. One way is to determine the goods to be promoted to consumers. The collection of sales data that is owned can actually be processed using data mining to see customer buying patterns, with data mining for large data it will not be wasted and can be useful so that it can provide benefits to the company. In this study, the data processing uses the Apriori Algorithm, which is a data mining method that aims to find association patterns based on purchasing patterns made by consumers, so that it can be seen which items are often purchased simultaneously. Kata Kunci : Data Mining, Apriori Algorithms, Product Promotion Abstrak: Data Mining adalah suatu istilah yang digunakan untuk menguraikan proses-proses di setiap itemset untuk dapat menemukan hasil setiap item-item nya, Analisa yang digunakan untuk menentukan promosi produk-produk elektronik yaitu dengan aturan asosiasi algoritma apriori, oleh karena itu Toko UD Surya Elektronik untuk meningkatkan hasil penjualan maka harus memiliki strategi lain untuk dapat meningkatkan sistem penjualannya. Salah satunya adalah dengan menentukan barang yang akan dipromosikan kepada konsumen. Kumpulan data penjualan yang dimiliki sebenarnya dapat diolah menggunakan data mining untuk melihat pola pembelian pelanggan, dengan data mining untuk data yang besar tidak akan terbuang begitu saja dan dapat bermanfaat sehingga dapat memberikan keuntungan kepada perusahaan. Pada penelitian ini, proses pengolahan data menggunakan Algoritma Apriori yang merupakan salah satu metode data mining yang bertujuan untuk mencari pola assosiasi berdasarkan pola pembelian yang dilakukan oleh konsumen, sehingga bisa diketahui item-item barang apa saja yang sering dibeli secara bersamaan. Kata Kunci : Data Mining, Algoritma Apriori, Promosi Produk.

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Prediksi Pola Tata Letak Produk untuk Strategi Penjualan Menggunakan Algoritma Apriori

JUTSI (Jurnal Teknologi dan Sistem Informasi) ◽

10.33330/jutsi.v2i1.1168 ◽

2021 ◽

Vol 2 (1) ◽

pp. 173-180

Author(s):

Muhammad Al Amri ◽

Nurwati Nurwati ◽

Muthia Dewi

Keyword(s):

Data Mining ◽

A Priori ◽

Analytical Technique ◽

Transaction Analysis ◽

Changing Patterns ◽

Abstract Data ◽

Using Data ◽

Product Displays ◽

Product Package ◽

Product Layout

Abstract: Data mining is a term used to describe the discovery of knowledge in databases. Data mining is a process that uses statistical techniques, mathematics, artificial intelligence, and maching learning to extract and identify useful information and related knowledge from large databases. One of the data mining methods is the association rule by analyzing a sales transaction at the 212 mart latsitarda Kisaran store. Sales transaction analysis aims to design a sales strategy. To design an effective sales or marketing strategy. In addition, the use of this analytical technique can also find changing patterns of products that are often purchased together or products that tend to appear together in a transaction from transaction data which are generally large in size. Store 212 mart latsitarda Kisaran can then use this pattern to place frequently purchased products into a contiguous area. Designing product displays in catalogs, designing product package sales, and so on using data mining concepts (a priori algorithm approach) so that they can analyze buyer behavior. Keywords: Data Mining; Product Layout Pattern; Apriori Algorithm Abstrak: Data Mining adalah suatu istilah yang digunakan untuk memguraikan penemuan pengetahuan didalam database. Data Mining adalah proses yang menggunakan teknik statistik, matematika, kecerdasan buatan, dan maching learning untuk mengestraksi dan mengindentifikasi informasi yang bermanfaat dan pengetahuan yang terkait dari berbagai database besar. Salah satu metode data mining adalah aturan asosiasi dengan melakukan analisis suatu transaksi penjualan pada toko 212 mart latsitarda Kisaran. Analisis transaksi penjualan bertujuan untuk merancang strategi penjualan. Untuk merancang strategi penjualan atau pemasaran yang efektif. Selain itu, penggunaaan teknik analisis ini juga dapat menemukan pola berubah produk-produk yang sering dibeli bersamaan atau produk yang cenderung muncul bersaama dalam sebuah transaksi dari data transaksi yang pada umumnya berukuran besar. Toko 212 mart latsitarda Kisaran lalu dapat menggunakan pola ini untuk menempatkan produk yang sering dibeli kedalam sebuah area yang berdekatan. Merancang tampilan produk di katalok, merancang penjualan paket produk, dan sebagainya dengan menggunakan konsep data mining (pendekatan algoritma apriori) sehingga dapat menganalisis prilaku pembeli. Kata kunci: Data Mining; Pola Tata Letak Produk; Algoritma Apriori

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Decision Support System using Data Mining Method for a Cross Selling Strategy in Retail Stores

International Journal of Informatics and Communication Technology (IJ-ICT) ◽

10.11591/ijict.v3i3.pp171-177 ◽

2014 ◽

Vol 3 (3) ◽

pp. 171

Author(s):

Imam Tahyudin ◽

Mohammad Imron ◽

Siti Alvi Solikhatin

Keyword(s):

Data Mining ◽

Association Rule ◽

Large Data ◽

Retail Stores ◽

Mining Method ◽

Transaction Data ◽

Sales Strategy ◽

Using Data ◽

Retail Company ◽

Confidence Value

A sales transaction dataof a retail company which is collect edevery day is enormous. Very large data will bemore meaning fultoin crease the company’s profitsif itcanbe extracted properly. Based on the research resultsof Andhika, et al[1], ZhangandRuan[6], Herera et al [7], Witten [11], explained that one of the methods that can gather information from the transaction data is the method of association. With this method it can be determined the patterns of transactions performed simultaneously and repeatedly. Thus, it can be obtained amodel that can be used as a reference for cross selling sales strategy. The purpose of this research is to apply data mining association methods of data mining by using apriori algorithm to create a new sales strategy for cross selling. Based on calculations, Association Rule is implemented by applying Confidence value=0.8while the value of Support=0.1 of the defined minimum value, the total result are 77 rules.Keywords: Data Mining, Association, Apriori Algorithm, Cross Selling, Retail Stores

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

Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) ◽

10.35870/jtik.v5i1.197 ◽

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.

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Penerapan Data Mining Untuk Menggali Informasi Tersembunyi Dalam Big Data Nilai Mata Kuliah

Digital Zone Jurnal Teknologi Informasi dan Komunikasi ◽

10.31849/digitalzone.v7i2.604 ◽

2016 ◽

Vol 7 (2) ◽

pp. 129-134

Author(s):

Elvira Asril ◽

Fana Wiza ◽

Taslim Taslim

Keyword(s):

Data Mining ◽

Association Rule ◽

College Graduates ◽

Process Mining ◽

A Priori ◽

Basic Competencies ◽

Using Data ◽

Object Of Study ◽

Mining Association Rule

Abstrak- Jarang sekali perguruan tinggi melihat kompetensi lulusannya sebelum dilepas ke dunia nyata. Salah satu variabel yang bisa digunakan adalah nilai matakuliah yang telah diperoleh mahasiswa atau calon lulusan. Kemudian memetakan nilai matakuliah yang telah diperoleh tiap mahasiswa atau calon lulusan pada aspek kompetensi dasar lulusan Strata satu Informatika yang disusun oleh asosiasi perguruan tinggi komputer (APTIKOM) dengan menggunakan teknik data mining. Pemetaan dilakukan berdasarkan nilai matakuliah yang telah ditempuh oleh mahasiswa atau calon lulusan, dalam hal ini objek penelitian adalah mahasiswa angkatan 2012 s/d 2015 yang telah mencapai 120 sks. Daftar aspek kompetensi dasar yang digunakan adalah aspek kompetensi yang disusun oleh APTIKOM berdasarkan ACM/IEEE 2013. Kemudian dilakukan penentuan kelompok matakuliah pada tiap kompetensi tersebut. Topik-topik yang dikaji antara lain meliputi : database, data mining, association rule, apriori dan beberapa algoritma lain yang mungkin dapat digunakan, serta perangkat lunak yang digunakan untuk proses mining. Pengolahan data yang telah disiapkan menggunakan beberapa perangkat lunak bantu seperti Excel, dan Tanagra. Mining data yang telah dilakukan menghasilkan informasi mengenai kompetensi dari calon lulusan yang dapat digunakan sebagai bahan analisa untuk pengambilan keputusan. Kata kunci : kompetensi, informasi, nilai mata kuliah Abstract- Rarely college graduates look competence before being released into the real world. One of the variables that can be used is the value of the courses that have been acquired or prospective graduate students. Then mapping the value of the courses that have been taken by each student or graduate candidates on the basis of competence of graduates Strata aspects of the Information compiled by the association of colleges computer (APTIKOM) using data mining techniques. Mapping is done based on the value of the courses that have been taken by students or prospective graduates, in this case the object of study is the student of 2012 s / d in 2015 which has achieved 120 credits. List aspects of basic competencies that are used are compiled by the competence aspect APTIKOM based ACM / IEEE 2013. Then is the determination of subjects in each group that competency. Topics to be studied include: databases, data mining, association rule, a priori and some other algorithm that may be used, as well as the software used to process mining. Processing of the data which has been prepared using some assistive software such as Excel, and Tanagra. Data mining has been done to produce information concerning the competence of prospective graduates who can be used as material analysis for decision making. Keywords: competence, information, mark

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Recognizing Job Apathy Patterns of Iraqi Higher Education Employees Using Data Mining Techniques

Journal of Southwest Jiaotong University ◽

10.35741/issn.0258-2724.54.4.30 ◽

2019 ◽

Vol 54 (4) ◽

Author(s):

Mustafa S. Abd ◽

Suhad Faisal Behadili

Keyword(s):

Higher Education ◽

Data Mining ◽

Psychiatric Patients ◽

Human Life ◽

A Priori ◽

Psychological Research ◽

Attribute Selection ◽

Specific Class ◽

Data Mining Techniques ◽

Using Data

Psychological research centers help indirectly contact professionals from the fields of human life, job environment, family life, and psychological infrastructure for psychiatric patients. This research aims to detect job apathy patterns from the behavior of employee groups in the University of Baghdad and the Iraqi Ministry of Higher Education and Scientific Research. This investigation presents an approach using data mining techniques to acquire new knowledge and differs from statistical studies in terms of supporting the researchers’ evolving needs. These techniques manipulate redundant or irrelevant attributes to discover interesting patterns. The principal issue identifies several important and affective questions taken from a questionnaire, and the psychiatric researchers recommend these questions. Useless questions are pruned using the attribute selection method. Moreover, pieces of information gained through these questions are measured according to a specific class and ranked accordingly. Association and a priori algorithms are used to detect the most influential and interrelated questions in the questionnaire. Consequently, the decisive parameters that may lead to job apathy are determined.

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Visual Data Mining for Discovering Association Rules

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch125 ◽

2008 ◽

pp. 2105-2120

Author(s):

Kesaraporn Techapichetvanich ◽

Amitava Datta

Keyword(s):

Data Mining ◽

Association Rules ◽

Association Rule ◽

Large Data ◽

Data Sets ◽

Visual Data Mining ◽

Useful Knowledge ◽

Large Databases ◽

A New Technique ◽

Mining Association Rule

Both visualization and data mining have become important tools in discovering hidden relationships in large data sets, and in extracting useful knowledge and information from large databases. Even though many algorithms for mining association rules have been researched extensively in the past decade, they do not incorporate users in the association-rule mining process. Most of these algorithms generate a large number of association rules, some of which are not practically interesting. This chapter presents a new technique that integrates visualization into the mining association rule process. Users can apply their knowledge and be involved in finding interesting association rules through interactive visualization, after obtaining visual feedback as the algorithm generates association rules. In addition, the users gain insight and deeper understanding of their data sets, as well as control over mining meaningful association rules.

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Visual Data Mining for Discovering Association Rules

Business Applications and Computational Intelligence ◽

10.4018/978-1-59140-702-7.ch011 ◽

2011 ◽

pp. 209-226

Author(s):

Kesaraporn Techapichetvanich ◽

Amitava Datta

Keyword(s):

Data Mining ◽

Association Rules ◽

Association Rule ◽

Large Data ◽

Data Sets ◽

Visual Data Mining ◽

Useful Knowledge ◽

Large Databases ◽

A New Technique ◽

Mining Association Rule

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Research on Teaching Quality Evaluation Using Data Mining Technique

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.926-930.4582 ◽

2014 ◽

Vol 926-930 ◽

pp. 4582-4585

Author(s):

Ai Feng Li ◽

Ying Hu ◽

Wen Jing Zhao

Keyword(s):

Data Mining ◽

Decision Tree ◽

Association Rule ◽

Quality Evaluation ◽

Teaching Quality ◽

Data Mining Technique ◽

Higher Education System ◽

Potential Factors ◽

Rule Method ◽

Using Data

—In this paper, we employ data mining (DM) technique to analyze various potential factors which impact the in-class teaching quality evaluation. Based on an effective dataset, we first exploit association rule method to mine the relationship between the teacher’s attributions, such as title, degree, age, seniority, and load, and the in-class teaching quality evaluation results. Then, we construct the decision tree of course’s attributions to reveal how the course’s attributions, such as property, credit, week hour, and number of students, impact the in-class teaching quality evaluation results. Our mined rules can provide effective guidance to talent development, teaching management, and input of talent in higher education system. Index Terms—data mining, decision tree, association rule, teaching quality evaluation

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

ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA ◽

10.30829/algoritma.v3i2.6953 ◽

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

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

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IMPLEMENTASI DATA MINING UNTUK MENGETAHUI POLA PEMBELIAN OBAT MENGGUNAKAN ALGORITMA APRIORI

Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika ◽

10.33751/komputasi.v17i2.2156 ◽

2020 ◽

Vol 17 (2) ◽

pp. 396-402

Author(s):

Nadya Febrianny Ulfha ◽

Ruhul Amin

Keyword(s):

Data Mining ◽

Association Rules ◽

A Priori ◽

Marketing Strategies ◽

Business World ◽

Association Pattern ◽

Green Lake ◽

Purchasing Patterns ◽

The Relationship ◽

Confidence Value

Competition in the business world requires entrepreneurs to think of finding a way or method to increase the transaction of goods sold. The purpose of this research is to provide drug stock data that is widely purchased by pharmacy customers at Kimia Farma, Green Lake branch in Jakarta. The algorithm used in this study is a priori to determine the relationship between the frequency of sales of drug brands most frequently purchased by customers. The association pattern formed with a minimum support of 40% and a minimum value of 70% confidence produces 17 association rules. The strong rules obtained are that if you buy a 500Mg Ponstan KPL @ 100, you will buy an Incidal OD 10Mg Cap with a support value of 59% and a confidence value of 84%. A priori algorithm can be used by companies to develop marketing strategies in marketing products by examining consumer purchasing patterns.

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