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All Journal International Journal of Evaluation and Research in Education (IJERE) snimed Jurnal Teknoin SMARTICS Journal Jurnal Nasional Komputasi dan Teknologi Informasi JMIE (Journal of Madrasah Ibtidaiyah Education) Jurnal Ilmiah Sinus Indonesian Journal of Business Intelligence (IJUBI) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) IJIIS: International Journal of Informatics and Information Systems Jurnal Teknik Industri Terintegrasi (JUTIN) International Journal of Computer and Information System (IJCIS) Jurnal Informatika Teknologi dan Sains (Jinteks) Khazanah: Jurnal Mahasiswa Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Journal of Computing Innovations and Emerging Technologies
Dhina Puspasari Wijaya, Dhina Puspasari
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Analyzing the Effectiveness of Apriori and ECLAT Algorithms in Frequent Itemset Mining Dhina Puspasari Wijaya; Pipit Febriana Dewi; Nurul Mega Saraswati
Journal of Computing Innovations and Emerging Technologies Vol. 1 No. 1 (2025): Volume 1 No 1
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v1i1.4

Abstract

Consumer transaction data recorded through the point of sale (POS) system contains purchase patterns that can be used to support marketing strategies. In the context of convenience stores that have high transaction volumes and large product variations, product association analysis is important to uncover customers' shopping habits. This study compares the effectiveness of a priori and ECLAT algorithms in conducting frequent itemset mining on transaction data of daily necessities. Both algorithms were evaluated based on the number of rules generated, support value, confidence, lift, and execution time efficiency. The dataset used is Groceries, which represents actual transactions in the retail environment. The results showed that although a priori and ECLAT produced 25 rules. ECLAT was superior in execution times four times faster than Apriori without compromising the quality of the rules. Most rules have a confidence between 30–50% and a lift above 1.5, signifying a meaningful association. This study concludes that ECLAT is more suitable for use in complex and dynamic minimarket transaction data scenarios, and is recommended as the basis for the development of product recommendation systems and association-based bundling strategies.
Co-Authors Adhien Kenya Anima Estetikha Aditya, Muhammad Fahrul Ahmad Subhan Yazid Amalia, Ade An-Nisa Apriani Andari, Rachma Yuni Andi Wahyudi Andri Pramuntadi Andri Pramuntadi Anni Karimatul Fauziyah Asdistia, Sindy Avrillaila Akbar Harahap Dadang Heksaputra Dadang Heksaputra Dadang Heksaputra Deden Hardan Gautama Deviani Titi Nautami Dita Danianti Dita Danianti Doni Harisandi Emiliana, Emiliana Febriani, Hilda Amalia Guatama, Deden Hardan Gutama , Deden Hardan Gutama, Deden Hardan Gutama, Deden Hardan Handy Eka Putra Anwar Haq, Fariz Abdillah Heksaputra, Dadang Imam Ihsani Insih Wilujeng Iskandar, Reza Fauzan Nur Iskarini, Ariska istiqomah istiqomah izzah, lathifatul Izzati Muhimmah Juansa, Andra KHOIRUL ANAM M. J. U. Haris Bahrudin Muhamad Irfanudin Muhammad Nur Wangid Musthofa Galih Pradana Nurona, Atmim Nurul Mega Saraswati Oktaviana, Agfa Permana, Candra Pipit Febriana Dewi Pramuntadi, Andri Prastowo, Wahit Desta Pujo Hari Saputro Putri, Mufida Awalia Ragil Satria Wicaksana Rahadian Kurniawan Ramadan, Muchammad Ilham Rahmad Rianita, Risti Nindi Riskiah, Putri Maulidatur Safitri, Eli Safitri, Nurul Aulia Salis Nizar Qomaruzaman Sari, Icha Puspita Sari, Mega Purnama Setiawan P, Andi Sri Kusumadewi Sri Nilawati Sri Widodo Sumanto Sumanto Syamsiar, Syamsiar Wahyu A, Nova Laila Wasilaturrohmah, Wasilaturrohmah Yahya, Abdullah Yanuar Wicaksono Yusinta Dwi Ariyani Yusnita, Shella