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The popular association rule algorithms are Apriori and fp-growth; both of these algorithms are very familiar among data mining researchers; however, there are some weaknesses found in the association rule algorithm, including long dataset scans in the process of finding the frequency of the item set, using large memory, and the resulting rules being sometimes less than optimal. In this study, the authors made a comparison of the fp-growth, Apriori, and TPQ-Apriori algorithms to analyze the rule results of the three algorithms. TPQ- Apriori is an algorithm developed from the Apriori algorithm. For experiments, the Apriori and fp-growth algorithms use RapidMiner and Weka tools, while the TPQ-apriori algorithm uses self-built application programs. The dataset used is the sales data for the Kopegtel NTB department store, which has been uploaded on the Kaggle site. As for the results of testing the base rules from the overall results of testing the rules with the good Kopegtel dataset for 100%, 50%, and 25% of the total volume of the dataset, a conclusion can be drawn that the larger the dataset to be processed, the results will be more optimal when using the fp-growth algorithm RapidMiner, but not optimal if the dataset to be processed is small. It is different from using the Apriori and Weka FP-growth algorithms, where the resulting rules are less than optimal if the dataset used is large and optimal if the dataset is small. Several rules do not appear in the fp-growth and Apriori Weka algorithms because the two algorithms do not have a tolerance value in Weka's tools for the support of the rules that will be displayed. Meanwhile, the TPQ- Apriori algorithm that has been developed is capable of producing optimal rules for both large datasets and small datasets.

Perkembangan teknologi memberikan dampak yang besar terhadap perkembangan dunia bisnis. Persaingan dalam dunia bisnis sangat ketat sehingga perlu melihat potensi transaksi penjualan produk dan memiliki strategi penjualan produk yang tepat. HNI Business Center 2 merupakan salah satu toko yang menjual produk herbal. Dengan banyaknya permintaan obat herbal pada saat covid-19 maka diperlukan stok obat guna meningkatkan penjualan produk. Maka diperkulan suatu anlisis untuk membantu pimpinan HNI Business Center 2 mengetahu produk yang harus ditingkatkan stoknya sesuai dengan data transaksi pelanggan. Maka dari permasalahan tersebut konsep yang bisa terapkan adalah data mining. Data yang di analisis nantinya menggunakan data mining adalah sejumlah 50 transaksi penjualan. Dengan menggunakan Minimum Support = 50% dan Maximum Support = 50%. Hasil penelitian ini dapat digunakan oleh HNI Businss Center 2 untuk mengambil keputusan menyetok kesediaan poroduk herbal.

Copyright of Techno.com is the property of Universitas Dian Nuswantoro, Fakultas Ilmu Komputer and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Techno.com is the property of Universitas Dian Nuswantoro, Fakultas Ilmu Komputer and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Perilaku konsumen dalam membeli suatu produk memang sangat beragam. Ada pembeli yang gemar membeli produk yang telah dipaket, tetapi ada juga pembeli yang membeli produk yang mendapat diskon, dan masih banyak lagi. Keragaman perilaku konsumen dalam memilih barang yang ingin dibeli tentunya menjadi permasalahan tersendiri bagi para penjual jika ingin meningkatkan penjualannya. Algoritma FP-Growth sendiri merupakan hasil perbaikan dari algoritma Apriori. FP-Growth membangkitkan struktur data tree atau yang lebih dikenal sebagai frequent pattern tree. FP tree memerlukan dua kali scanning database untuk menentukan frequent itemset sehingga membuatnya lebih efisien dibandingkan Apriori. Hasil dari association rules menggunakan FP-Growth dijadikan rekomendasi bagi para penjual / retailer dalam memberikan paket penjualan barang bagi konsumennya dan hasil dari penelitian ini adalah ditemukan dua pasangan item barang yakni kopi, gula dan teh, susu yang memiliki support sebesar 30% dan confidence sebesar 70%.

The use of gas in Indonesia is a more profitable alternative, one of which is the oil conversion program, LPG (liquefied petroleum gas). UD.Maju Bersama, which is a distribution agent for 3 Kg LPG gas for household needs, so far because of the many requests, of course LPG agents like this need to forecast the frequency of purchases to find out if the sales have been sold as optimally as possible and stocks can be provided well and supplies adequate for consumer demand. this problem can be solved by applying one of the Datamining techniques which is using the FP-Growth Algorithm method to find out the Frequency of Purchase of 3 Kg LPG Gas. Frequent Pattern Growth (FP-Growth) can be used to determine the set of data that most often appears (frequent itemset) in a data set. The results of data processing purchases at the Elpiji UD base. Forward Together the most sold or purchased values ​​at week 1 and 2 on each month with the highest value support 66.67% confidence 100.00%. the results can help base owners to make decisions on gas supply so that they can be used to increase the amount of supply from distributors to agents and increase profits with support and confidence.

Copyright of Techno.com is the property of Universitas Dian Nuswantoro, Fakultas Ilmu Komputer and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)