Analisis Transaksi Konsumen Bidang Data Mining Menggunakan Algoritma Apriori Untuk Rekomendasi Bundling Produk Pada 212 Mart Kota Lhokseumawe

JURNAL TIKA ◽  
2021 ◽  
Vol 6 (02) ◽  
pp. 92-98
Author(s):  
Mutasar Mutasar ◽  
Chaeroen Niesa
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Frequent Itemset ◽  
Minimum Support

212 Mart Lhokseumawe yang bernaung di bawah PT. Syirkah Mubarakah Lhokseumawe adalah sebuah usaha ritel yang baru saja dirintis. Dalam operasionalnya masih banyak kendala yang dihadapi sehingga omzet penjualan harian masih jauh dari yang diharapkan, namun peneliti ingin menawarkan sebuah solusi untuk meningkatkan penjualan produk dengan teknik Bundling Produk yang masih diterapkan secara konvensional. Tujuan dari penelitian ini adalah merancang dan membangun sebuah aplikasi data mining untuk memprediksi hasil penjualan barang yang diminati konsumen pada 212Mart Kota Lhokseumawe dengan menggunakan algoritma apriori berdasarkan data transaksi penjualan. Algoritma Apriori adalah salah suatu algoritma yang melakukan pencarian frequent itemset dengan menggunakan teknik association rule. Algoritma Apriori menggunakan pengetahuan frekuensi atribut yang telah diketahui sebelumnya untuk memproses informasi selanjutnya. Pada algoritma Apriori menentukan kandidat yang mungkin muncul dengan cara memperhatikan minimum support dan minimum confidence. Implementasi dari hasil pengolahan data transaksi penjualan diproses dengan algoritma apriori sehingga akan menghasilkan sebuah output penawaran Bundling Barang kepada konsumen dan menawarkan harga yang relative lebih ekonomis. Proses data mining ini melalui tahapan pengenalan pola perilaku dan transaksi konsumen pada 212 Mart Kota Lhokseumawe

scholarly journals Implementasi Data Mining Menggunakan Algoritma Apriori Pada Penjualan Suku Cadang Motor

2021 ◽  
Vol 14 (2) ◽  
pp. 125
Author(s):  
Ainul Mardiaha ◽  
Yulia Yulia
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
A Priori ◽  
Spare Parts ◽  
Frequent Itemset ◽  
Sales Data ◽  
Minimum Support ◽  
Relationship Of

This research was carried out to simplify or assist Candra Motor workshop owners in managing data and archives of motorcycle parts sales by applying a data mining a priori algorithm method. Data mining is an operation that uses a particular technique or method to look for different patterns or shapes in a selected data. Sales data for a year with the number of 15 items selected using the priori algorithm method. A priori algorithm is an algorithm for taking data with associative rules (association rule) to determine the associative relationship of an item combination. In a priori algorithm, it is determined frequent itemset-1, frequent itemset-2, and frequent itemset-3 so that the association rules can be obtained from previously selected data. To obtain the frequent itemset, each selected data must meet the minimum support and minimum confidence requirements. In this study using minimum support ? 7 or 0.583 and minimum confidence of 90%. So that some rules of association were obtained, where the calculation of the search for association rules manually and using WEKA software obtained the same results.By fulfilling the minimum support and minimum confidence requirements, the most sold spare parts are inner tube, Yamaha oil and MPX oil.

scholarly journals Penerapan Association Rule Mining Berbasis Algoritma Frequent Pattern Growth untuk Rekomendasi Penjualan

2020 ◽  
Vol 7 (2) ◽  
pp. 135-148
Author(s):  
Didi Supriyadi
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Itemset ◽  
Frequent Pattern ◽  
Rule Mining ◽  
Minimum Support ◽  
Pattern Growth ◽  
Mining Association Rule

Tingkat persaingan dan kompleksitas permasalahan penjualan pada perusahaan retail, menuntut setiap perusahaan retail untuk mampu berkompetisi dengan perusahaan lain. Salah satu yang dapat dilakukan adalah melalui pengambilan keputusan terkait penjualan yang lebih tepat dan efektif. Besarnya data transaksinonal penjualan perusahaan retail dapat dilakukan ekstraksi informasi yang bermanfaat. Metode yang dapat digunakan untuk menggali informasi adalah melalui penerapan association rule mining. Association Rule Mining merupakan suatu metode data mining yang berfokus pada pola transaksi dengan cara mengekstraksi asosiasi atau hubungan suatu kejadian. Keranjang belanja yang terdapat pada perusahaan retail yang terkomputerisasi merupakan cara terbaik untuk memberikan dukungan rekomendasi keputusan secara ilmiah dengan cara menentukan hubungan antara barang yang dibeli secara bersamaan dalam setiap transaksi. Algoritma FP-growth digunakan untuk menentukan himpunan dataset yang paling sering muncul (frequent itemset) pada sekeompok data. Penelitian ini menghasilkan nilai minimum support 0,1% dan nilai minimum confidence 60% jumlah rule yang dihasilkan berjumlah 116457, nilai minimum confidence 70% jumlah rule yang dihasilkan berjumlah 84086, dan nilai minimum confidence 80% jumlah rule yang dihasilkan berjumlah 48623 dari data yang diolah sebanyak 22191. Hasil rule ini dapat digunakan untuk strategi pemasaran produk. Nilai minimum support 0,1% dimana semakin besar nilai minimum confidence maka menghasilkan rule yang semakin sedikit.

scholarly journals PENERAPAN DATA MINING MENGGUNAKAN ALGORITMA APRIORI UNTUK MENENTUKAN POLA GOLONGAN PENYANDANG MASALAH KESEJAHTERAAN SOSIAL

Sebatik ◽  
2022 ◽  
Vol 26 (1) ◽  
Author(s):  
Irwan Adji Darmawan ◽  
Muhammad Fakhri Randy ◽  
Imam Yunianto ◽  
Muhamad Malik Mutoffar ◽  
M Tio Putra Salis
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Itemset ◽  
Frequent Pattern ◽  
Data Set ◽  
Minimum Support

Penyandang Masalah Kesejahteraan Sosial (PMKS) menjadi satu dari sekian masalah yang terdapat di daerah perkotaan, sebab dapat mengganggu pembangunan kota, ketertiban umum, keamanan dan stabilitas. Sejauh ini langkah yang dilakukan sementara masih terfokus dengan cara penanganan PMKS, masih belum mengarah untuk mencegah. Menentukan pola golongan PMKS merupakan salah satu cara yang dapat dilakukan. Algoritma Apriori memiliki fungsi untuk membantu menemukan pola yang terdapat pada data (frequent pattern mining) untuk menentukan frequent itemset yang menggunakan metode Association Rule dalam data mining. Dalam penghitungan secara manual yang dilakukan maka didapat pola kombinasi antara lain 3 rules yang memiliki nilai minimum support 15% dengan confidence tertinggi 100% menggunakan Algoritma Apriori. Dalam menguji Algoritma Apriori digunakan aplikasi RapidMiner. RapidMiner merupakan satu dari beberapa software pengolah data mining, misalnya menganalisis teks, mengekstrak pola data set kemudian dikombinasikan menggunakan metode statistik, database, dan kecerdasan buatan agar didapat informasi yang tinggi berasal dari olahan data. Hasil yang didapat dari pengujian perbandingan pola antar golongan PMKS. Dari pengujian menggunakan aplikasi RapidMiner dan penghitungan secara manual Algoritma Apriori, maka disimpulkan dengan kriteria pengujian, bahwa pola (rules) golongan dengan nilai confidence (c) penghitungan manual Algoritma Apriori dapat dibilang tidak mendekati hasil pengujian aplikasi RapidMiner, maka dapat dikatakan tingkat keakuratan pengujian rencah, hanya 37,5%.

scholarly journals Minimum Threshold Determination Method based on Dataset Characteristics in Association Rule Mining

2021 ◽  
Author(s):  
Erna Hikmawati ◽  
Nur Ulfa Maulidevi ◽  
Kridanto Surendro
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Threshold Value ◽  
Extraction Process ◽  
Frequent Itemset ◽  
Rule Mining ◽  
Minimum Threshold ◽  
Minimum Support ◽  
Associative Behavior

Abstract The process of extracting data to obtain useful information is known as data mining. Furthermore, one of the promising and widely used techniques for this extraction process is association rule mining. This technique is used to identify interesting relationships between sets of items in a dataset and predict associative behavior for new data. The first step in association rule mining is the determination of the frequent item set that will be involved in the rule formation process. In this step, a threshold is used to eliminate items excluded in the frequent itemset which is also known as the minimum support. Furthermore, the threshold provides an important role in determining the number of rules generated. However, setting the wrong threshold leads to the failure of the association rule mining to obtain rules. Currently, the minimum support value is determined by the user. This leads to a challenge that becomes worse for a user that is ignorant of the dataset characteristics. In this study, a method was proposed to determine the minimum support value based on the characteristics of the dataset. Furthermore, this required certain criteria to be used as thresholds which led to more adaptive rules according to the needs of the user. The results of this study showed that 6 from 8 datasets, obtained a rule with lift ratio > 1 using the minimum threshold value that was determined through this method.

scholarly journals ANALISIS TINGKAT KEJAHATAN ANAK DIBAWAH UMUR MENGGUNAKAN METODE FP-GROWTH (STUDI KASUS DI POLRESTA PADANG)

2020 ◽  
Vol 1 (1) ◽  
pp. 16-23
Author(s):  
Randika Farike Bania
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Frequent Itemset ◽  
Mining Association Rule

 Kekerasan terhadap anak sebagai setiap tindakan atau serangkaian tindakan wali atau kelalaian oleh orang tua atau pengasuh lainnya yang dihasilkan dapat membahayakan, atau berpotensi bahaya, atau memberikan ancaman yang berbahaya kepada anak..  Mengimplementasikan Data Mining, Association Rule dan Algoritma FP-Growth pada kekerasan kekerasan pada anak di bawah umur  untuk mengekstrak ilmu pengetahuan, informasi penting dan menarik dari database. Sumber  data yang digunakan masih merupakan data mentah yang belum diolah dan merupakan data kekerasan pada anak di bawah umur yang mencangkup laporan  di Polresta Padang. Hasil penelitian ini adalah berupa suatu perangkat lunak dengan mengimplementasikan algoritma FP-Growth yang menggunakan konsep pembangunan FP-Tree dalam mencari Frequent Itemset dan unutk pengujian hasil dilakukan dengan aplikasi yang telah dirancang menggunakan bahasa pemogramman PHP MYSQL. Hasil  pengujian didapatkan dari assosiasi kasus kekerasan yang dominan terjadi pada anak dibawah umur, yaitu jika Kasus Penganiayaan maka Korbannya Pelajar dengan nilai support 30% dan nilai confidence 84%, jika Pelaku Swasta maka Korban Pelajar dengan nilai support 20% dan confidence 73%, jika Pelaku Swasta, Kasus Penganiayaan maka Korban Pelajar dengan nilai support 17% dan confidence 71%, jika Kasus Cabul maka Korban Pelajar dengan nilai support 28% dan confidence 65% dan jika Pelaku Pengangguran maka Korban pelajar dengan nilai support 17% dan confidence 64%.

scholarly journals Postdiffset: an Eclat-like algorithm for frequent itemset mining

2018 ◽  
Vol 7 (2.28) ◽  
pp. 197
Author(s):  
W A.W.A. Bakar ◽  
M A. Jalil ◽  
M Man ◽  
Z Abdullah ◽  
F Mohd
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Underlying Structure ◽  
Data Format ◽  
Itemset Mining ◽  
Data Formats ◽  
Vertical Data ◽  
Mining Algorithms

Frequent itemset mining is a major field in data mining techniques. This is because it deals with usual and normal occurrences of set of items in a database transaction. Originated from market basket analysis, frequent itemset generation may lead to the formulation of association rule as to derive correlation or patterns.  Association rule mining still remains as one of the most prominent areas in data mining that aims to extract interesting correlations, frequent patterns, association or casual structures among set of items in the transaction databases. Underlying structure of association rules mining algorithms are based upon horizontal or vertical data formats. These two data formats have been widely discussed by showing few examples of algorithm of each data formats. The works on horizontal approaches suffer in many candidate generation and multiple database scans that contributes to higher memory consumptions. In response to improve on horizontal approach, the works on vertical approaches are established. Eclat algorithm is one example of algorithm in vertical approach database format. Motivated to its ‘fast intersection’, in this paper, we review and analyze the fundamental Eclat and Eclat-variants such as tidset, diffset, and sortdiffset. In response to vertical data format and as a continuity to Eclat extension, we propose a postdiffset algorithm as a new member in Eclat variants that use tidset format in the first looping and diffset in the later looping. We present the performance of postdiffset results in time execution as to indicate some improvements has been achieved in frequent itemset mining. 

Comparative Analysis of Association Rule Mining Algorithms in Market Basket Analysis Using Transactional Data

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
AA Izang ◽  
SO Kuyoro ◽  
OD Alao ◽  
RU Okoro ◽  
OA Adesegun
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Market Basket Analysis ◽  
Rule Mining ◽  
Business Decisions ◽  
Market Basket ◽  
Minimum Support ◽  
Support Threshold ◽  
Transactional Data

Association rule mining (ARM) is an aspect of data mining that has revolutionized the area of predictive modelling paving way for data mining technique to become the recommended method for business owners to evaluate organizational performance. Market basket analysis (MBA), a useful modeling technique in data mining, is often used to analyze customer buying pattern. Choosing the right ARM algorithm to use in MBA is somewhat difficult, as most algorithms performance is determined by characteristics such as amount of data used, application domain, time variation, and customer’s preferences. Hence this study examines four ARM algorithm used in MBA systems for improved business Decisions. One million, one hundered and twele thousand (1,112,000) transactional data were extracted from Babcock University Superstore. The dataset was induced with Frequent Pattern Growth, Apiori, Association Outliers and Supervised Association Rule ARM algorithms. The outputs were compared using minimum support threshold, confidence level and execution time as metrics. The result showed that The FP Growth has minimum support threshold of 0.011 and confidence level of 0.013, Apriori 0.019 and 0.022, Association outliers 0.026 and 0.294 while Supervised Association Rule has 0.032 and 0.212 respectively. The FP Growth and Apirori ARM algorithms performed better than Association Outliers and Supervised Association Rule when the minimum support and confidence threshold were both set to 0.1. The study concluded by recommending a hybrid ARM algorithm to be used for building MBA Applications. The outcome of this study when adopted by business ventures will lead to improved business decisions thereby helping to achieve customer retention. Keywords: Association rule mining, Business ventures, Data mining, Market basket analysis, Transactional data.

scholarly journals Penerapan Metode Data Mining Pada Point of Sale Berbasis Web Menggunakan Algoritma Apriori

2021 ◽  
Vol 5 (3) ◽  
pp. 1158
Author(s):  
Adam Firmansyah ◽  
M Iwan Wahyudin ◽  
Ben Rahman
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
A Priori ◽  
Point Of Sale ◽  
Minimum Support ◽  
Confidence Value

To be able to understand which products have been purchased by customers, it is done by describing the habits when customers buy. Use association rules to detect items purchased at the same time. This study uses an a priori algorithm to determine the association rules when buying goods. The results of the study and analyzing the data obtained a statement that using the a priori algorithm to select the combined itemset using a minimum support of 25% and a minimum confidence of 100%, found the association rule, namely, if the customer buys at the same time. Buying goods has the highest value of support and trust. Likewise with the support value of 25%, the confidence value is 100%. In this way, if a customer buys an item, the probability that the customer buys the item is 100%

scholarly journals An Enhanced Approach to Mine Maximal Frequent Itemset using Maximal Frequent Itemset Prima Algorithm (MFIPA)

2019 ◽  
Vol 8 (S2) ◽  
pp. 9-12
Author(s):  
R. Smeeta Mary ◽  
K. Perumal
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Itemsets ◽  
Frequent Itemset ◽  
Decision Makers ◽  
New Method ◽  
Rule Mining

In data mining finding out the frequent itemsets is one of the very essential topics. Data mining helps in identifying the best knowledge for different decision makers. Frequent itemset generation is the precondition and most time-consuming method for association rule mining. In this paper we suggest a new algorithm for frequent itemset detection that works with datasets in distributed manner. The proposed algorithm brings in a new method to find frequent itemset not including the necessitate to create candidate itemsets. The proposed approach could be implemented using horizontal representation for transaction datasets and allocating prime value. It explores all the frequent itemset that is present in the input and according to the support the maximum frequent itemset is identified. It was applied on different transactions database and compared with well-known algorithms: FP-Growth and Parallel Apriori with different support levels. The try out showed that the proposed algorithm attain major time improvement over both algorithms.

scholarly journals Analisa Pola Frekuensi Keranjang Belanja Dengan Dengan Perbandingan Algoritma Fp-Growth (Frequent Pattern Growth) dan Eclat pada minimarket

2020 ◽  
Vol 7 (2) ◽  
pp. 364-373
Author(s):  
Krisna Nata Wijaya
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Frequent Pattern ◽  
Minimum Support ◽  
Pattern Growth

Dalam kegiatan transaksi jual beli di minimarket ataupun toko pemilik harus mengerti apa yang diinginkan komsumen dalam memberikan kenyaman berbelanja, terutama kemudahan dalam pemilihan barang yang disesuaikan dengan tata letak atau penempatan barang. Dengan menerapkan association rule pada data transaksi akan memudahkan pemilik dalam mengelolah informasi penjualan dan mencari itemset. Oleh karena itu, penelitian ini Melakukan analisis pola data transaksi penjualan dengan menerapkan metode asosiasi pada data mining. Selanjutnya dengan melakukan perbandingan algoritma Fp-Growth dan Eclat dengan minimum support dan confidence sebesar 0.01% untuk menentukan jumlah aturan yang terbentuk sebagai bahan pengambil keputusan yang ditunjukan untuk frekuensi keranjang belanja.

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