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All Journal Civil Engineering For Community Development Jurnal Manajemen dan Teknologi Rekayasa Journal of Information Technology
Sherin Ramadhania
Institut Teknologi Sumatera
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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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.
STANDARDISASI PROSES MANUFAKTUR UNTUK MENINGKATKAN DAYA SAING UMKM INDUSTRI GENTING PRINGSEWU LAMPUNG Juniwati Juniwati; Muhammad Suryo Panotogomo Abi Suroso; Sherin Ramadhania; Dian Fajarika; Nia Sastra Permata
Civil Engineering for Community Development (CECD) Vol 5, No 1 (2026): Edisi April 2026
Publisher : Department of Civil Engineering Faculty of Engineering, Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62870/cecd.v5i1.39696

Abstract

Kegiatan pengabdian masyarakat ini bertujuan untuk merancang dan menerapkan standardisasi proses manufaktur guna meningkatkan daya saing UMKM industri genting di Kabupaten Pringsewu, Lampung. Permasalahan utama mitra terletak pada proses produksi tradisional yang sangat bergantung pada intuisi dan kondisi cuaca, sehingga menghasilkan variasi kualitas produk yang tinggi. Metode pelaksanaan menggunakan pendekatan Participatory Action Research (PAR) yang diintegrasikan dengan alat manajemen kualitas SIPOC (Supplier, Input, Process, Output, Customer) dan siklus PDCA (Plan-Do-Check-Act). Hasil pengabdian menunjukkan bahwa pemetaan proses bisnis berhasil mengidentifikasi titik kritis pada tahap pengeringan dan pembakaran yang memicu cacat produk. Melalui penyusunan SOP partisipatif dan pendampingan teknis, terjadi transformasi pola kerja mitra dari sistem berbasis pengalaman individu menuju sistem operasional yang lebih terukur dan semi-terstandar. Kesimpulannya, standardisasi ini efektif meningkatkan konsistensi mutu dan kualitas manajerial UMKM. Meskipun demikian, keterbatasan infrastruktur pengeringan mekanis tetap menjadi tantangan utama. Rekomendasi ke depan difokuskan pada modernisasi teknologi produksi serta digitalisasi pelaporan mutu untuk menjamin keberlanjutan industri di masa depan.
Job Safety Analysis as a Sustainable Solution for Hazard Identification and Control in the Sugarcane Klentek Frame Fabrication Process at PT. XYZ Achmad Samudra Dewantara; Sherin Ramadhania; Nia Sastra Permata; Muhammad Suryo Panatagomo Abi Suroso; Eka Rachmadi Endarta Putra
JUMANTARA: Jurnal Manajemen dan Teknologi Rekayasa Vol 5, No 2 (2026)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/jumantara.v5i2.3613

Abstract

This study applies Job Safety Analysis (JSA) as a qualitative method to manage occupational safety and health (OSH) risks in the production of the Frame Klentek Tebu component at PT. XYZ, an agricultural machinery company. Despite having existing OSH programs, the company continues to face high-frequency incidents of near-misses and Lost Time Injuries, mainly due to heavy machinery use, welding, and painting. The initial analysis suggests that these accidents are caused by procedural lapses and inadequate hazard identification at the task level. The JSA process involved analyzing key tasks Cutting, Fit-Up, Welding, Primer Painting, and Finishing Painting to identify hazards such as falling materials, electric shock and burns during welding, and chemical inhalation during painting. A significant finding was the non-compliance with Personal Protective Equipment (PPE), largely due to operator habits. The research proposed a control hierarchy, prioritizing Elimination (e.g., chamfering sharp edges, replacing forklifts with automated conveyors) and Engineering Controls (e.g., proper grounding systems, installing barriers). This JSA serves as an essential tool for embedding safety into Standard Operating Procedures (SOPs), ensuring business continuity, and creating a safer work environment at PT. XYZ.
Design Of Artificial Neural Network (ANN) Based Analytics Model for Safety Stock and Reorder Point Prediction at Galeri 1 ITERA Guido Immanuel Simanungkalit; Hersa Dwi Yanuarso; Frieska Ariesta Syafnijal; Eka Rachmadi Endarta Putra; Sherin Ramadhania
JUMANTARA: Jurnal Manajemen dan Teknologi Rekayasa Vol 5, No 2 (2026)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/jumantara.v5i2.3992

Abstract

Inventory management in retail environments such as Galeri 1 ITERA faces significant challenges due to fluctuating and irregular demand patterns, which often lead to stockout and overstock conditions. This study proposes an integrated inventory analytics system that combines ABC classification, demand forecasting using Artificial Neural Network (ANN), safety stock (SS) and reorder point (ROP) calculation, as well as interactive dashboard visualization to support data-driven inventory decision-making. The research methodology consists of: (1) ABC analysis to determine inventory control priorities for 56 products; (2) demand forecasting using ANN models trained on historical sales data; (3) calculation of safety stock and reorder point based on forecasting results and lead time variability; and (4) development of an analytics dashboard using Microsoft Power BI. The proposed ANN model utilizes a multilayer feedforward architecture capable of capturing nonlinear demand patterns. Forecasting performance was evaluated using WAPE, RMSE, and MAE metrics. The results indicate that 24 category-A items (42.9%) contribute to 79.45% of the total inventory consumption value, highlighting the need for tighter inventory control. The ANN forecasting model achieved an average accuracy of 88.72% with an average WAPE of 11.28%, while most products showed stable MAE and RMSE values below 2.5 units. Forecasting outputs were subsequently integrated into safety stock and reorder point calculations, producing dynamic inventory control parameters ranging from 3–209 units for safety stock and 3–296 units for reorder points. The main scientific contribution of this study lies in the integration of ABC classification, ANN-based forecasting, and dashboard-based predictive analytics into a unified inventory management framework for retail operations. The resulting dashboard enables interactive monitoring of inventory conditions, stock alerts, demand predictions, safety stock, and reorder point values, thereby improving the effectiveness and responsiveness of inventory control decisions.
Co-Authors Achmad Samudra Dewantara Dian Fajarika, Dian Eka Rachmadi Endarta Putra Eka Yuniar Frieska Ariesta Syafnijal Guido Immanuel Simanungkalit Hersa Dwi Yanuarso Juniwati Juniwati Mas'ud Hermansyah Muhammad Suryo Panatagomo Abi Suroso Muhammad Suryo Panotogomo Abi Suroso Nia Sastra Permata Pascawati Savitri Universitasari Setiawan, Akas Bagus