scholarly journals Prediksi Pola Tata Letak Produk untuk Strategi Penjualan Menggunakan Algoritma Apriori

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            

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

2021 ◽  
Vol 1 (2) ◽  
pp. 89-94
Author(s):  
Yustika Margolang ◽  
Fauriatun Helmiah ◽  
Mardalius Mardalius
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Item Analysis ◽  
A Priori ◽  
Large Data ◽  
Electronic Products ◽  
Abstract Data ◽  
Using Data ◽  
Purchasing Patterns ◽  
Item Item

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.

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

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.

scholarly journals APPLICATION OF DATA MINING FOR ANALYSIS OF CONSUMER PURCHASE DATA ON SALES TRANSACTION DATA AT HALAL MART HNI HPAI DHARMASRAYA

2020 ◽  
Vol 3 (1) ◽  
pp. 68-75
Author(s):  
Sri Kurnia Yuliarnis ◽  
Yeka Hendriyani ◽  
Denny Kurniadi ◽  
M. Giatman
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Online Media ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Sales Strategy ◽  
Using Data ◽  
Purchase Patterns ◽  
Product Layout ◽  
Archive Data

The sales strategy determines the continuity of the business being run. The problems that occur are the sales archive data has not been analyzed in-depth, the information system has not been integrated with applications for sales data analysis, online media promotion has not been maximized, inadequate stock of goods, the layout of goods is not optimal, and the combination of the number of products is not optimal. This study aims to extract hidden information in the sales database using Data Mining. From the information generated, sales strategy recommendations are developed relating to promotions, inventory, catalogue design, item layout, and the combination of product quantities. The method used is the association rule with Apriori algorithm to find consumer purchase patterns through the resulting association. The importance of association can be identified by two benchmarks, namely support and confidence. The sales strategy analyzed includes product promotion, catalogue design, product layout, stock predictions, and product combinations for sale. Based on the research produced 7 strong rules which are the highest association rules which are then developed into a sales strategy recommendation.

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

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

scholarly journals Implementasi Data Mining dengan Metode Algoritma Apriori dalam Menentukan Pola Pembelian Obat

2015 ◽  
Vol 2 (2) ◽  
pp. 102 ◽  
Author(s):  
Robi Yanto ◽  
Riri Khoiriah
Keyword(s):  
Data Mining ◽  
System Development ◽  
Current System ◽  
A Priori ◽  
Health Sector ◽  
Field Studies ◽  
Visual Basic ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Visual Basic 6.0

Data mining merupakan proses untuk mendapatkan informasi yang berguna dari gudang basis data yang berupa ilmu pengetahuan. penelitian ini melakukan analisa data dengan menggunakan data mining dan metode algoritma appriori. Sistem yang dibangun ditujukan untuk pemenuhan dalam penentuan pola pembelian obat dengan menggunakan bahasa pemrograman Visual Basic 6.0 dan database Mysql pada studi kasus di sektor kesehatan. Sistem ini dibangun berdasarkan kebutuhan pengguna yang diperoleh melalui metode wawancara dan studi lapangan. Metodelogi pengembangan sistem yang digunakan yaitu metode waterfall yang terdiri Analisis, Desain, Pengkodean dan Pengujian. Hasil pengujian dengan algoritma apriori dan sistem yang dibangun menunjukan hasil yang telah memenuhi kebutuhan dalam penentuan pola pembelian obat berdasarkan kecenderungan pembelian obat oleh pelanggan. Dibandingkan dengan sistem yang sedang berjalan kinerja tersebut ditunjukan pada efektifitas informasi dari sistem tentang penentuan pola pembelian obat untuk ketersediaan obat dan tata letak obat untuk memudahkan dalam mengetahui keberadaan obat yang dilihat dari 2 itemset obat.Data mining is the process to obtain useful information from the warehouse database in the form of science. This study analyzes the data by using data mining algorithms and methods appriori. The system is built is intended for fulfillment in determining the pattern of drug purchases by using Visual Basic 6.0 programming language and MySQL database on a case study in the health sector. The system is built based on the needs of users obtained through interviews and field studies. System development methodology used is the waterfall method which consists Analysis, Design, Coding and Testing. Test results with a priori algorithms and systems built show results that have met the requirements in determining the pattern of drug purchases by the tendency of drug purchases by customers. Compared with the current system performance information is shown on the effectiveness of the system of determining the pattern of drug purchases for the availability of drugs and drug layout for ease in knowing where drugs are viewed from 2 itemset drug.

scholarly journals Penerapan Data Mining Menggunakan Task Market Basket Analysis Pada Transaksi Penjualan Barang di Ab Mart dengan Algoritma Apriori

2021 ◽  
Vol 5 (2) ◽  
pp. 676
Author(s):  
Andreas Lewis ◽  
Muhammad Zarlis ◽  
Zakarias Situmorang
Keyword(s):  
Data Mining ◽  
Data Collection ◽  
Association Rules ◽  
A Priori ◽  
Study Data ◽  
Market Basket Analysis ◽  
Literature Study ◽  
Problem Identification ◽  
Market Basket ◽  
Using Data

Data Mining is the process of extracting information or something interesting from the data in the database so as to produce valuable information using techniques such as clustering, estimation, description, and others. Based on observations at AB Mart, there were 44 product items whose data was not revealed. This problem will be solved using data mining analysis. The purpose of this research is to apply market basket analysis to the sale of goods at AB Mart with the a priori algorithm. This research uses a clear structure of the framework, namely problem identification, literature study, data collection, calculation & analysis of association rules with a priori algorithm, forming association rules and making reports. The results of the sales transaction of AB Mart in August resulted in or generated relationships between shopping product items where the% purchase of Pepsodent was 115%, Frisian Flag 96%, Sugar 96%, Indomilk 93%, and Nasi Jempol 91%. The conclusion of this research is using Weka software with a priori algorithm which produces an association relationship between pepsodent goods and the number of transactions purchased

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

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