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All Journal Jurnal Sisfotek Global The International Journal of Studies in Social Sciences and Humanities (IJOSSH) Journal of Information Technology
Eka Yuniar
Politeknik Negeri Jember
Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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Segmentation and Prediction of Store Performance on the Shopee Marketplace Using a Hybrid Clustering Approach, Spatial Analysis, and Feature Importance Eka Yuniar; Sherin Ramadhania; Pascawati Savitri Universitasari; Mas'ud Hermansyah; Akas Bagus Setiawan
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2256

Abstract

Marketplace platforms have become a central component of digital commerce, particularly in Southeast Asia where Shopee has emerged as one of the dominant e-commerce ecosystems. The increasing number of sellers on the platform intensifies competition and requires data-driven approaches to understand store performance patterns. This study aims to analyze and predict the performance of Shopee stores using a hybrid data mining approach that integrates clustering, spatial analysis, and feature importance evaluation. The dataset consists of 655 Shopee stores collected on February 18, 2026, including attributes such as number of products, chat response rate, follower count, store rating, store tenure, promotional activity, and seller address. K-Means clustering is applied to segment store performance, while spatial analysis examines the geographic distribution of clusters across Indonesian provinces. Furthermore, a Random Forest classifier is used to predict performance categories and identify influential features affecting store competitiveness. The clustering results reveal three distinct store performance groups representing low, medium, and high activity levels. Spatial analysis indicates that provinces with stronger digital ecosystems, particularly West Java and Jakarta, contain a higher concentration of active stores. Feature importance analysis shows that promotional activity, chat responsiveness, and follower count significantly influence store performance classification. The findings contribute to the development of hybrid data mining frameworks for marketplace analysis and provide practical insights for improving seller competitiveness in digital commerce ecosystems.
Design and Construction of an Accounting Information System at the Nutrition Care Center of Jember State Polytechnic Fitriya Andriyani; Lintang Anis Bena Kinanti; Eka Yuniar; Berlina Yudha Pratiwi; Dinu Saadilah
International Journal of Studies in Social Sciences and Humanities Vol. 3 No. 1 (2026): July
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/ijossh.v3i1.6698

Abstract

This research urgency of effective, efficient, and accountable financial management in the institution’s nutrition service unit. Currently, financial records are still managed manually and lack proper structure, leading to potential risks such as recording errors, difficulties in transaction tracking, and delays in financial report preparation. The main objective of this study is to design and develop an accounting information system using Microsoft Excel to facilitate transaction recording, account classification, and the automatic preparation of financial reports in an integrated manner. The system is expected to improve data accuracy, accelerate the reporting process, and enhance transparency and accountability in financial management at the Nutrition Care Center (NCC). The research applies the Research and Development (R&D) method with a system development model encompassing needs analysis, system design, implementation, verification, and maintenance. Data collection was conducted through observation, interviews with NCC managers, and documentation. The information system developed is easy to access, familiar to users, and has adequate features for building small to medium scale accounting systems. The output of this research is a prototype accounting information system ready for use by NCC, as well as a research report that can serve as the basis for developing advanced application-based systems. Furthermore, the results of this research are also expected to contribute to improving the financial governance of academic-based business units at vocational colleges.
Extended LRFM-Based K-Means Clustering for Gym Member Segmentation and Engagement Analysis Mas'ud Hermansyah; Eka Yuniar; Muhammad Bahanan; Fatimatuzzahra Fatimatuzzahra; Khen Dedes
JURNAL SISFOTEK GLOBAL Vol 16, No 2 (2026): JURNAL SISFOTEK GLOBAL
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/sisfotek.v16i2.17024

Abstract

The increasing need for data-driven member management in the fitness center industry makes member segmentation important for identifying differences in activity, engagement, and financial contribution and supporting more targeted retention strategies. This study aims to group members of GYM JGM (Jaya Makmur Gym), Jember, based on Length, Recency, Frequency, and Monetary (LRFM) characteristics. The dataset used consists of 150 member data. The research process includes data preprocessing, LRFM feature selection, standardization using Z-Score, and the application of the K-Means algorithm. Determining the optimal number of clusters is done by comparing the Elbow Method, Davies-Bouldin Index (DBI), and Silhouette Score on candidate clusters k = 2 to k = 9. The evaluation results show that three clusters are the optimal configuration because they produce the lowest DBI value of 0.825 and the highest Silhouette Score of 0.430, and show a clear change point in the Elbow Method. The segmentation results produce three groups of members, namely Shorter-Term Moderate-Activity Members, High-Value Active Members, and Long-Term Low-Engagement Members. The segmentation shows that the K-Means algorithm based on LRFM features is able to identify differences in member characteristics and provide an analytical basis for developing retention strategies, increasing engagement, and reactivating GYM JGM members.
Co-Authors Berlina Yudha Pratiwi Dinu Saadilah Fatimatuzzahra Fatimatuzzahra Fitriya Andriyani Khen Dedes Lintang Anis Bena Kinanti Mas'ud Hermansyah Muhammad Bahanan Pascawati Savitri Universitasari Setiawan, Akas Bagus Sherin Ramadhania