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All Journal Jurnal Pengabdian Masyarakat (ABDIRA) Journal of Information Technology
Pascawati Savitri Universitasari
Politeknik Negeri Jember
Aerospace Engineering Computer Science & IT Languange, Linguistic, Communication & Media Nursing Social Sciences Other

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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.
Penguatan Pengelolaan Tenaga Lepas melalui Digitalisasi di UPA Pertanian Terpadu Ratih Puspitorini Yekti Ambarkahi; Dyah Kusuma Wardani; Fredy Eka Ardhi Pratama; Ponti Primastuti Aulia Nugraheni; Pascawati Savitri Universitasari; Dhanang Eka Putra
Jurnal Pengabdian Masyarakat (ABDIRA) Vol 6, No 2 (2026): Abdira
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/abdira.v6i2.1997

Abstract

The 2025 Community Service Program was carried out at the Integrated Agriculture Academic Support Unit (UPA Pertanian Terpadu) of Politeknik Negeri Jember, which oversees four Teaching Factory (TEFA) units: TEFA Smart Green House, TEFA Livestock, TEFA Innovation Garden, and TEFA Chrysanthemum. The partner's main problem was the manual management of 25 temporary workers, which led to inefficiencies, delays in task distribution, and weak performance monitoring. The program was implemented through needs assessment and analysis; design of a simple application-based digital system; user training; intensive mentoring; and the provision of fingerprint attendance machines. The results show that the digital system and fingerprint attendance machines improved the efficiency of task distribution, accelerated recording and reporting processes, and made it easier to monitor worker performance. Digital literacy among temporary workers increased by an average of 40% based on pre–post tests, and delays in monthly production reports decreased noticeably. Worker satisfaction also increased due to clearer task allocation, transparent working hours, and more accurate incentive recording.
Co-Authors Dyah Kusuma Wardani Eka Yuniar Fredy Eka Ardhi Pratama Mas'ud Hermansyah Ponti Primastuti Aulia Nugraheni Putra, Dhanang Eka Ratih Puspitorini Yekti Ambarkahi Setiawan, Akas Bagus Sherin Ramadhania