Penentuan Pola Pembelian Konsumen pada Indomaret GKB Gresik dengan Metode FP-Growth

Setiawan, Amir; Anugrah, Indra Gita

doi:10.32672/jnkti.v2i2.1564

Cited by 14 publications

(19 citation statements)

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“…Algoritma FP-Growth Pada tahap ini digunakan teknik FP-Tree pada Algoritma FP-Growth agar frequent itemset dapat lebih cepat dicari [12], [13], [14], [15], [17], [18].…”

Section: 4unclassified

Analisis Penerapan Data Mining Dalam Penentuan Tata Letak Barang Menggunakan Algoritma Apriori Dan Fp-Growth

et al. 2022

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Setting the layout of merchandise in each store window greatly affects consumer interest in shopping. To increase self-service sales, a strategy is needed to achieve this, one of which is to systematically arrange the layout of goods on merchandise shelves. The method used for implementing the layout of goods is to compare the performance of the Apriori Algorithm and the FP-Growth Algorithm in the data mining process using the Rapidminer Studio Educational Version 9.10.011 tools to obtain more accurate results. The data sample used is sales data at the Mohare Supermarket, which is tested to understand the association patterns generated by each method. Based on the test results with a minimum support of 20% and a minimum confidence of 70%, the Apriori Algorithm produces 10 rules with a support of 0.32258605 and an accuracy of 12.8%, while the FP-Growth Algorithm produces 78 rules with a support of 2.51612903 with an accuracy of 780%. Thus, the FP-Growth Algorithm can be stated to have a high degree of accuracy in generating association rules when compared to the Apriori Algorithm.

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“…Algoritma FP-Growth Pada tahap ini digunakan teknik FP-Tree pada Algoritma FP-Growth agar frequent itemset dapat lebih cepat dicari [12], [13], [14], [15], [17], [18].…”

Section: 4unclassified

Analisis Penerapan Data Mining Dalam Penentuan Tata Letak Barang Menggunakan Algoritma Apriori Dan Fp-Growth

et al. 2022

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“…Algoritma FP-Growth merupakan pengembangan dari algoritma Apriori [12]. Algoritma Frequent Pattern Growthjuga bisa diartikan sebagai satu alternatif algoritma yang dapat digunakan untuk menentukan himpunan data yang paling sering muncul (frequent itemset) dalam sebuah kumpulan data.…”

Section: Fp-growthunclassified

“…Lift ratio merupakan suatu ukuran mengetahui kekuatan dari aturan asosiasi yang telah terbentuk. Nilai dari lift ratio ini akan digunakan sebagai penentu, apakah aturan asosiasi valid atau tidak valid [12]. Berikut adalah rumus dari perhitungan lift ratio:…”

Section: Lift Ratiounclassified

Penerapan Data Mining Pada Hasil Tracer Study Alumni Untuk Menemukan Pola Asosiasi Dengan Algoritma Fp-Growth

Andika

Nazir

Wulandari

et al. 2022

JNKTI

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Abstrak - Tracer study adalah studi terhadap lulusan lembaga pendidikan tinggi, yang juga merupakan proses untuk mendapatkan informasi terkait transformasi alumni dari masa pendidikan ke dunia industri. Hal ini dilakukan untuk menentukan kelayakan pekerjaan lulusan dan juga penilaian retrospektif, seperti apa pelayanan yang diperoleh selama masa studi. Penelitian ini akan melaksanakan tracer study pada tingkat program studi di Teknik Informatika di UIN Suska Riau dan kemudian hasil studi lanjutan ini akan diolah untuk mendapatkan pola korelasi atau hubungan yang bermakna. Pengolahan data akan menggunakan teknologi data mining dan algoritma fp-growth. Berdasarkan hasil analisa pengujian dan interpretasi dari total data bersih 129 alumni teknik informatika UIN Suska Riau yaitu pola yang dihasilkan dari proses mining dengan minimum support 15% dan minimum confidence 40% adalah sebanyak 608 pola. Peneliti mengambil beberapa pola dengan antecedents berupa ipk, masa tunggu, bidang pekerjaan, gaji, pekerjaan pertama, jenis kelamin untuk diinterpretasikan.Kata kunci: Data Mining, Tracer study, Fp-growth Abstract - Tracer study is a study of graduates of higher education institutions, which is also a process to obtain information related to the transformation of alumni from their education period to the industrial world. This is done to determine the employability of graduates as well as a retrospective assessment, such as what services were obtained during the study period. This research will carry out a tracer study at the study program level in Informatics Engineering at UIN Suska Riau and then the results of this follow-up study will be processed to obtain a significant correlation pattern or relationship. Data processing will use data mining technology and the fp-growth algorithm. Based on the results of the analysis of testing and interpretation of the total net data of 129 alumni of informatics engineering UIN Suska Riau, the patterns generated from the mining process with a minimum support of 15% and a minimum confidence of 40% are 608 patterns. Researchers took several patterns with antecedents in the form of GPA, waiting period, field of work, salary, first job, gender to be interpreted.Keywords: Data Mining, Tracer study, Fp-growth

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“…Teknik mining yang digunakan untuk menemukan pola atau aturan asosiasi antara suatu kombinasi item disebut dengan association rule [17]. Algoritma yang sering digunakan dalam penelitian terkait association rule apriori dan FPgrowth [18]. Pada algoritma FP-growth dapat digunakan untuk menentukan itemset yang paling sering muncul dari suatu data.…”

Section: Association Rulesunclassified

Market Based Analysis Sebagai Peningkatan Penjualan Produk Menggunakan Algoritma K-Medoids Dan Fp-Growth

2022

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The COVID-19 pandemic has greatly affected the economy in Indonesia. The emphasis on the development of covid-19 through Enforcement of Restrictions on Community Activities has an impact on the Micro, Small and Medium Enterprises (MSME) sector, including product sales at the Distro Sextors. Therefore, a strategy is needed to increase sales, one of which is through data mining techniques with market based analysis. Data mining is one of the processes of Knowledge Discovery in Database (KDD) that uses scientific methods in its application. The data used in this study were 6531 sales transactions. The data mining algorithm used in this research is K-Medoids for grouping which aims to find the best cluster. The cluster experiments used are K-3, K-4, K-5, K-6 and K-7 then cluster testing is carried out using the Davies Bouldien Index (DBI) method and the results are cluster K-3 is the best cluster because it has a homogeneous value lowest. Then the association rule uses the FP-Growth algorithm and the minimum support value used is 6% to 7% while the minimum confidence value is 60%. The results of the association rule analysis are product recommendations that can be used as promotions to increase sales in Distro Sextors. The products are SE128, SE131, SE130, SE431, SE792, dan SE804.

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Penentuan Pola Pembelian Konsumen pada Indomaret GKB Gresik dengan Metode FP-Growth

Cited by 14 publications

References 0 publications

Analisis Penerapan Data Mining Dalam Penentuan Tata Letak Barang Menggunakan Algoritma Apriori Dan Fp-Growth

Analisis Penerapan Data Mining Dalam Penentuan Tata Letak Barang Menggunakan Algoritma Apriori Dan Fp-Growth

Penerapan Data Mining Pada Hasil Tracer Study Alumni Untuk Menemukan Pola Asosiasi Dengan Algoritma Fp-Growth

Market Based Analysis Sebagai Peningkatan Penjualan Produk Menggunakan Algoritma K-Medoids Dan Fp-Growth

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