[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-id-113":3,"doc-seo-140154-113":53,"doc-detail-140154-id":128},{"code":4,"msg":5,"data":6},0,"success",[7,13,17,21,25,29,33,37,41,45,49],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},55,"Document","Agama & Spiritualitas",60,"religion-spirituality",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":11,"slug":16},48,"Cerita & Novel","story-novel",{"id":18,"doc_module":4,"doc_module_name":9,"category_name":19,"show_sort_weight":11,"slug":20},56,"Gaya Hidup","lifestyle",{"id":22,"doc_module":4,"doc_module_name":9,"category_name":23,"show_sort_weight":11,"slug":24},51,"Komik","comic",{"id":26,"doc_module":4,"doc_module_name":9,"category_name":27,"show_sort_weight":11,"slug":28},53,"Layanan Kesehatan","healthcare",{"id":30,"doc_module":4,"doc_module_name":9,"category_name":31,"show_sort_weight":11,"slug":32},54,"Penelitian & Laporan","research-report",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":11,"slug":36},49,"Sastra","literature",{"id":38,"doc_module":4,"doc_module_name":9,"category_name":39,"show_sort_weight":11,"slug":40},52,"Teknologi","technology",{"id":42,"doc_module":4,"doc_module_name":9,"category_name":43,"show_sort_weight":11,"slug":44},50,"Ujian","exam",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":47,"show_sort_weight":11,"slug":48},57,"Umum","general",{"id":50,"doc_module":4,"doc_module_name":9,"category_name":51,"show_sort_weight":4,"slug":52},181,"Formulir","formulir",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":121,"head_meta":123,"extra_data":125,"updated_unix":127},113,"id","comparison-of-apriori-and-fp-growth-algorithms-discovering-association-patterns-in-retail-product-sales-data-in-an-it-store","Perbandingan Algoritma Apriori dan FP-Growth - Menemukan Pola Asosiasi pada Data Penjualan Produk Ritel di Toko IT","","Penelitian ini membandingkan Algoritma Apriori dan FP-Growth dalam menemukan pola asosiasi dari data transaksi penjualan produk teknologi informasi (IT) di toko ritel. Dataset memuat 7.496 transaksi dengan maksimum 20 item dan rata-rata 3,91 item per transaksi. Sebelum analisis, terdapat 137 nama produk berbeda; setelah preprocessing dan standarisasi, tersisa 75 produk yang memenuhi minimum support 1% dan diuji dengan confidence minimum 30%. Kedua algoritma menghasilkan 253 itemset sering muncul dan 63 aturan asosiasi, dengan nilai lift tertinggi pada kartu SanDisk Ultra 64GB dan 128GB microSDXC.",{"@graph":63,"@context":120},[64,81,103],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,72,75,78],{"item":68,"name":69,"@type":70,"position":71},"https://docshare.wps.com","Home","ListItem",1,{"item":73,"name":9,"@type":70,"position":74},"https://docshare.wps.com/id/document/",2,{"item":76,"name":31,"@type":70,"position":77},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":79,"name":59,"@type":70,"position":80},"https://docshare.wps.com/id/document/comparison-of-apriori-and-fp-growth-algorithms-discovering-association-patterns-in-retail-product-sales-data-in-an-it-store/140154/",4,{"url":79,"name":59,"@type":82,"image":83,"author":88,"headline":59,"publisher":91,"fileFormat":94,"inLanguage":57,"description":61,"dateModified":95,"datePublished":96,"encodingFormat":94,"isAccessibleForFree":97,"interactionStatistic":98},"DigitalDocument",{"url":84,"@type":85,"width":86,"height":87},"https://docshare.wps.com/thumbnails/comparison-of-apriori-and-fp-growth-algorithms-discovering-association-patterns-in-retail-product-sales-data-in-an-it-store/140154.png","ImageObject",300,407,{"name":89,"@type":90},"Quinn Holloway","Person",{"url":68,"name":92,"@type":93},"DocShare","Organization","application/pdf","2026-09-19","2026-08-24",true,{"@type":99,"interactionType":100,"userInteractionCount":102},"InteractionCounter",{"@type":101},"ViewAction",6,{"@type":104,"mainEntity":105},"FAQPage",[106,112,116],{"name":107,"@type":108,"acceptedAnswer":109},"Apa tujuan penelitian ini dalam membandingkan Apriori dan FP-Growth?","Question",{"text":110,"@type":111},"Penelitian ini bertujuan mengidentifikasi pola pembelian produk TI, membandingkan efisiensi komputasi kedua algoritma, serta memberi saran strategis berdasarkan hasil analisis aturan asosiasi.","Answer",{"name":113,"@type":108,"acceptedAnswer":114},"Bagaimana pemrosesan data dilakukan sebelum menjalankan algoritma?",{"text":115,"@type":111},"Data mentah berisi 137 nama produk berbeda. Dilakukan preprocessing dan standarisasi nama hingga tersisa 75 produk yang memenuhi minimum support 1%.",{"name":117,"@type":108,"acceptedAnswer":118},"Parameter apa yang digunakan untuk membentuk aturan asosiasi?",{"text":119,"@type":111},"Penelitian menggunakan minimum support 1% dan minimum confidence 30% untuk menghasilkan itemset sering muncul dan aturan asosiasi.","https://schema.org",{"og:url":79,"og:type":122,"og:title":59,"og:site_name":92,"og:description":61},"article",{"robots":124,"canonical":79},"index,follow",{"doc_id":126,"site_id":56},140154,1787569427,{"code":4,"msg":5,"data":129},{"doc_id":126,"user_id":130,"nickname":89,"user_avatar":131,"doc_module":4,"category_id":30,"category_name":31,"doc_title":59,"doc_description":61,"doc_content":132,"file_id":133,"file_url":134,"file_type":135,"file_size":136,"view_count":102,"is_deleted":4,"is_public":71,"is_downloadable":71,"audit_status":71,"page_count":137,"language":138,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":139,"faqs":140,"seo_title":141,"seo_description":61,"update_tm":127,"read_time":142},2336474466712,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Perbandingan Algoritma Apriori dan FP-Growth dalam Menemukan Pola Asosiasi pada Data Penjualan Produk Ritel di Toko IT  \nAmaliyah Dwi Ardiani¹, Margaretha Pereta Kein², Siti Marfuah³*, Novita Wanti  \nHallatu⁴  \n¹ -⁴ Teknik Informatika, Fakultas Teknik, Universitas Papua, Indonesia  \n*Penulis Korespondensi: [sitimarfuahh439@gmail.com](sitimarfuahh439@gmail.com)  \nAbstract. A method known as data mining is used to discover hidden patterns in very large data sets. Shopping cart analysis, also known as \"shopping cart analysis,\" is one of the most common techniques in the retail industry that utilizes association rules. The focus of this study is to discover association patterns between the Apriori Algorithm and FP-Growth on sales transaction data of information technology (IT) products in a retail store. The dataset used consists of 7,496 transactions, with a maximum of 20 items and an average of 3.91 items, respectively. The raw data before analysis contained 137 different product names. After preprocessing and name standardization, 75 products met the minimum support threshold of 1%. They were also tested with a minimum support parameter of 1% and a minimum confidence level of 30%. Both algorithms generated 253 frequently occurring itemsets and 63 association rules. The SanDisk Ultra 64GB and SanDisk Ultra 128GB microSDXC cards had the highest lift score of 3.4225. By requiring only two database scans, FP-Growth excels in computational efficiency. One can use these results to create cross-selling and reordering strategies.  \nKeywords: Apriori Algorithm; Association Rule Processing; FP-Growth Algorithm; IT Retail Store; Shopping Cart Analysis.  \nAbstrak. Metode yang dikenal sebagai data mining merupakan proses sistematis untuk menemukan polatersembunyi dalam kumpulan data yang sangat besar. Analisis keranjang belanja, atau \"market basket analysis\", adalah salah satu teknik yang paling umum di industri ritel untuk menggunakan aturan asosiasi. Fokus penelitian ini adalah untuk menemukan pola hubungan antara Algoritma Apriori dan FP-Growth pada data transaksi penjualan produk teknologi informasi (IT) di sebuah toko ritel. Dataset yang digunakan terdiri dari 7.496 transaksi, dengan masing-masing 20 item maksimum dan 3,91 item rata-rata. Data mentah sebelum analisis mengandung 137 nama produk yang berbeda. Setelah tahap preprocessing dan standarisasi nama, 75 produk memenuhi batas minimum dukungan 1% . Mereka juga diuji dengan parameter dukungan minimum 1% dan tingkat keyakinan minimum 30% . Kedua algoritma menghasilkan 253 itemset yang sering muncul dan 63 aturan asosiasi. Kartu SanDisk Ultra 64GB dan SanDisk 128GB Ultra microSDXC memiliki nilai lift tertinggi 3,4225 . Dengan hanyamembutuhkan dua kali pemindaian database, FP-Growth unggul dalam efisiensi komputasi. Seseorang dapat menggunakan hasil ini untuk membuat strategi penjualan silang dan pemesanan ulang.  \nKata kunci: Algoritma Apriori; Algoritma FP-Growth; Analisis Keranjang Belanja; Pemrosesan Aturan Asosiasi; Toko Ritel TI.  \n1. LATAR BELAKANG  \nDengan meningkatnya persaingan dalam bisnis ritel online, bisnis perlu memahami pola transaksi pelanggan untuk mendukung strategi pemasaran mereka. Analisis keranjang belanja dapat digunakan untuk mengidentifikasi pola pembelian untuk menentukan produk yang paling sering dibeli; ini dapat membantu perusahaan membuat rencana promosi dan meningkatkan loyalitas pelanggan. Proses menemukan pola, korelasi, dan informasi tersembunyi dalam kumpulan data yang sangat besar dikenal sebagai penambangan data. Di sektor ritel, salah satu teknik yang banyak digunakan adalah analisis aturan asosiasi, yang bertujuan untuk mengidentifikasi hubungan antar item dalam suatu transaksi, yang dikenalsebagai analisis keranjang belanja.  \nDua algoritmautama dalampenambangan aturan asosiasi adalahAlgoritmaApriori dan FP-Growth. Algoritma Apriori memanfaatkan pendekatan generate-and-test dengan prinsipantimonotonik untuk menemukan itemset yang sering kali mu","cbCaif2uPX6uUylB","https://ap.wps.com/l/cbCaif2uPX6uUylB","pdf",589510,16,"Indonesian","# Latar Belakang\n# Kajian Teoritis\n## Data Mining\n## Market Basket Analysis","[{\"question\":\"Apa tujuan penelitian ini dalam membandingkan Apriori dan FP-Growth?\",\"answer\":\"Penelitian ini bertujuan mengidentifikasi pola pembelian produk TI, membandingkan efisiensi komputasi kedua algoritma, serta memberi saran strategis berdasarkan hasil analisis aturan asosiasi.\"},{\"question\":\"Bagaimana pemrosesan data dilakukan sebelum menjalankan algoritma?\",\"answer\":\"Data mentah berisi 137 nama produk berbeda. Dilakukan preprocessing dan standarisasi nama hingga tersisa 75 produk yang memenuhi minimum support 1%.\"},{\"question\":\"Parameter apa yang digunakan untuk membentuk aturan asosiasi?\",\"answer\":\"Penelitian menggunakan minimum support 1% dan minimum confidence 30% untuk menghasilkan itemset sering muncul dan aturan asosiasi.\"}]","Perbandingan Algoritma Apriori dan FP-Growth - Menemukan Pola Asosiasi pada Data Penjualan Produk Ritel di Toko IT | PDF",25]