Article Per Year (5 Year)
p-Index From 2020 - 2025
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All Journal Journal of Elektronik Sistem InformasI International Journal of Applied Information Systems and Informatics
Nur Rachman, Andi
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Implementation of Apriori Algorithm to Analyze Sales Transaction Patterns in Official E-Commerce Adam Nugraha, Yuki Rizki; Maftahuhillah, Alma Ariz; Nur Rachman, Andi; Fitriani, Euis Nur; Tarempa, Genta Nazwar
JESII: Journal of Elektronik Sistem InformasI Vol 3 No 1 (2025): JOURNAL ELEKTRONIK SISTEM INFORMASI (JUNE)
Publisher : Departement Information Systems Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/jesii.v3i1.4127

Abstract

The growth of e-commerce in Indonesia has driven dynamic changes in consumer behavior, especially in the purchasing patterns of fashion products. However, most businesses have not optimally utilized transaction data to design targeted marketing strategies. One of the main challenges is the inability to systematically recognize customer purchase patterns from complex and large transaction data. This research aims to apply the Apriori algorithm, specifically the FP-Growth method, in identifying recurring purchase patterns based on product combinations that are often purchased together at the Qeela Official store, an e-commerce-based fashion business. The data used includes 20,000 transactions during the period January to April 2024, which were sampled into 10,000 transactions according to the RapidMiner system limitations. The research stages include data transformation to binary format, conversion of attributes to binominal, application of the FP-Growth algorithm, and formation of association rules using minimum support parameters of 0.001 and minimum confidence of 0.5. The results show the existence of strong association patterns, such as SHORT PARASUT → SHORT CARGO with a confidence of 82.1% and a lift of 3.197. The insights provide a strong basis for decision-making in product bundling strategies, cross-selling implementation, automated recommendations, and stock management. The data mining approach used is proven to be relevant and applicable to improve marketing effectiveness and operational efficiency in e-commerce businesses, especially in the highly competitive fashion industry.
Analysis Of Twitter User Sentiment To Tiktok Shop Using Naïve Bayes And Decision Tree Algorithms Jafar Sidiq, Soleh; Nur Rachman, Andi
Journal of Applied Information System and Informatic (JAISI) Vol 1, No 1 (2023): November 2023
Publisher : Deparment Information System, Siliwangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jaisi.v1i1.8990

Abstract

The growth of internet users is fantastic, before the pandemic the figure was only 175 million. While the latest data from the Asosiasi Penyelenggaraan Jasa Internet Indonesia (APJII), in 2022 internet users in Indonesia will reach around 210 million. One of the influences on the increasing number of internet users in Indonesia is the increasing number of buying and selling activities through internet media. At this time there are various kinds of e-commerce applications. One of the latest e-commerce in Indonesia is Tiktok Shop. Tiktok shop is a new feature of the Tiktok application which was established on April 17, 2021. The development of Tiktok shop cannot be separated from the people who use this feature. Many people give opinions about Tiktok Shop on one of the social media, namely Twitter. Twitter is a place to get data expressed by the public through tweets posted to the timeline. The data used are tweets in Indonesian with a dataset of 1000 tweets. The data is then processed to be analyzed for knowledge. The analysis is done with Naïve Bayes and Decision Tree methods. The accuracy results of the Naïve Bayes algorithm are 90% and the Decision tree algorithm is 93%, so the Decision Tree algorithm is better for classifying sentiment analysis of twitter users towards Tiktok Shop with a data division of 90%. 
Co-Authors Adam Nugraha, Yuki Rizki Fitriani, Euis Nur Genta Nazwar Tarempa Jafar Sidiq, Soleh Maftahuhillah, Alma Ariz