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All Journal Jurnal Teknoin OPSI Jurnal Sistem dan Manajemen Industri Journal of Appropriate Technology for Community Services International Journal of Industrial Optimization (IJIO) Journal of Industrial Engineering and Halal Industries TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Khasanah, Annisa Uswatun
Universitas Islam Indonesia
Religion Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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Analysis of consumer characteristics on retail business with clustering analysis method and association rule for selling improvement strategy recommendations Khasanah, Annisa Uswatun; Baihaqie, Muhammad Rafly Qowi
OPSI Vol 17 No 1 (2024): ISSN 1693-2102
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v17i1.11411

Abstract

In the highly competitive retail industry, companies must continually innovate and develop unique business strategies to enhance their sales performance. The ABC Store, a mini market in Yogyakarta, has experienced fluctuating sales over the past year, failing to meet its targets. This study aims to analyze consumer purchasing behavior at the ABC Store and provide strategic recommendations to boost sales. The data analyzed in this study comprises three months of transaction records. The methods used include Association Rule - Market Basket Analysis (AR-MBA) with the FP-Growth algorithm and Clustering Analysis with K-Means. The clustering analysis identified four distinct customer segments: Mid-Morning Moderates, Diverse Afternoon Buyers, Evening Moderates, and High-Value Customers. Cluster 2, comprising Diverse Afternoon Buyers, was selected for AR analysis due to its relatively high transaction value and the variety of products purchased, indicating its potential to evolve into a High-Value Customers cluster. The analysis yielded 104 rules. The findings can inform marketing strategies to increase sales, including product bundling and customer loyalty programs such as a point system.In the highly competitive retail industry, companies must continually innovate and develop unique business strategies to enhance their sales performance. The ABC Store, a mini market in Yogyakarta, has experienced fluctuating sales over the past year, failing to meet its targets. This study aims to analyze consumer purchasing behavior at the ABC Store and provide strategic recommendations to boost sales. The data analyzed in this study comprises three months of transaction records. The methods used include Association Rule - Market Basket Analysis (AR-MBA) with the FP-Growth algorithm and Clustering Analysis with K-Means. The clustering analysis identified four distinct customer segments: Mid-Morning Moderates, Diverse Afternoon Buyers, Evening Moderates, and High-Value Customers. Cluster 2, comprising Diverse Afternoon Buyers, was selected for AR analysis due to its relatively high transaction value and the variety of products purchased, indicating its potential to evolve into a High-Value Customers cluster. The analysis yielded 104 rules. The findings can inform marketing strategies to increase sales, including product bundling and customer loyalty programs such as a point system.
The implementation of text mining to improve google classroom performance based on user review Anggita Yekti Pawestri; Annisa Uswatun Khasanah
TEKNOSAINS : Jurnal Sains, Teknologi dan Informatika Vol 13 No 1 (2026): TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Publisher : LPPMPK- Universitas Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37373/tekno.v13i1.1479

Abstract

The internet is developing rapidly, especially in education (e-learning). Google Classroom is an e-learning platform that has been widely used for online learning. Google Classroom gets an average rating of 3.5 out of 5 on Google Play, so it is necessary to research to increase the application's rating based on user reviews with text mining. The data used in this study was 77,454 user reviews from Google Play. Data processing uses the Textblob and Naïve Bayes Classifier methods to determine user sentiment. The sentiment analysis results with a 70/30 split data yield an accuracy of 92.39%. Data with negative sentiments is then processed with the Association Rules method to find words used as problem keywords, which include keywords' assignment', 'upload,' 'submit,' 'file,' 'class,' 'notification,' and 'dark.' The word is then analyzed using a fishbone diagram to find the root cause. The root of the problems includes problems uploading files that are integrated with Google Drive, there is no setting to change the display to 'dark mode,' The user has a good internet connection, but the file upload process is slow, and so on. The recommendations for improvements are synchronizing file integration with Google Drive, changing the display to dark mode according to user preferences, and updating the application server so that the file upload process can be done more quickly.
Co-Authors Adelia Febriyanti Adji Chandra Kurniawan Amalia Syaharani Ibnu Anak Agung Ngurah Perwira Redi Anak Agung Ngurah Perwira Redi Andika Julianto Putra Anggita Yekti Pawestri Baihaqie, Muhammad Rafly Qowi Fairuz Kumari Fanya Nindha Al-Qurani Fiki Rohmatul Maula Hakim, Sulton Nur Jundi Nourfateha Elquthb Kumari, Fairuz Kurniawan, Adji Chandra Lulu Riesta Nugroho Maula, Fiki Rohmatul Nanda Ruswandi Natasha Utami Syaveyenda Permanawati, Zaenab Fitria Putra, Andika Julianto Rizki Rahmatullah Ruswandi, Nanda Sulton Nur Hakim Suryani, Dewi Amanatun Syaveyenda, Natasha Utami Wicaksono, Z Arifin