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p-Index From 2021 - 2026
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All Journal Journal of Integrated System
Gina Rahayu Wardiani
Program Studi Teknik Industri, Fakultas Teknik, Universitas Ma’soem, Bandung, Indonesia
Engineering Industrial & Manufacturing Engineering

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Comparison of Random Forest and Gradient Boosting Methods for Multi-Product Demand Forecasting at CV Healfit Pangan Sehat (DietGo Kitchen) with Different Demand Characteristics Nisa Noviani Sudarman; Gina Rahayu Wardiani; Ladzwina Mahardini
Journal of Integrated System Vol. 9 No. 1 (2026): Journal of Integrated System Vol. 9 No. 1 (June 2026)
Publisher : Universitas Kristen Maranatha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jis.v9i1.15125

Abstract

Accurate demand forecasting is a crucial factor in culinary industry supply chain management. This study compares the performance of two ensemble learning methods, namely Random Forest and Gradient Boosting, in predicting demand for six culinary products with different demand characteristics. The data used consists of monthly sales data at CV Healfit Pangan Sehat (DietGo Kitchen). Models were evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results show that Random Forest delivered superior performance for 5 out of 6 products with an average MAPE of 31.61%, compared to Gradient Boosting with an average MAPE of 35.43%. Random Forest proved more robust in handling products with stable demand patterns (Sei Ayam: MAPE 19.76%, Sei Sapi: MAPE 21.38%) and intermittent demand (Sei Domba: MAPE 26.88%). Feature importance analysis revealed that lag-3, lag-6, and trend were the strongest predictors in both models. Gradient Boosting outperformed Random Forest on only one product (Sambal Bawang: MAPE 37.16%). High-volatility products such as Baked Grill Chicken yielded a MAPE of 32.98%. This study provides a practical contribution in the form of a forecasting method selection framework based on product demand characteristics, along with recommendations for implementation in the culinary industry.
A Halal-Sustainable Circular Economy Model Based on Management Information System in Clothing Small Medium Enterprises Using Soft System Methodology Gina Rahayu Wardiani; Nisa Noviani Sudarman
Journal of Integrated System Vol. 9 No. 1 (2026): Journal of Integrated System Vol. 9 No. 1 (June 2026)
Publisher : Universitas Kristen Maranatha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jis.v9i1.15143

Abstract

This study aims to develop a sustainable halal circular economy model based on a management information system (MIS) in the halal clothing Small and Medium Enterprises (SMEs) in Indonesia. The main challenge faced is integrating circular economy principles with halal values, particularly in the SMEs sector. The approach used was the seven stage Soft System Methodology (SSM). Data were collected through in-depth interviews, field observations, and documentation studies. The results of the study identified gaps between existing practices and ideal conditions in aspects of halal supply chain management, production waste management, raw material traceability systems, and halal integration in business operations. The developed model contains six main pillars, namely the 6R principle (Reduce, Reuse, Recycle, Redesign, Recover, Remanufacture) which is aligned with halal requirements, an integrated management information system, a digital technology-based traceability mechanism, and a multi-stakeholder collaboration framework. The designed information system includes a halal management module, a sustainability module, an integration module, and a decision support system. This research provides a theoretical contribution by integrating SSM, halal practices, and sustainability as a blueprint for implementing a circular economy in the halal apparel sector, while also serving as a foundation for developing halal-sustainable industrial policies in Indonesia.
Co-Authors Ladzwina Mahardini Nisa Noviani Sudarman Nisa Noviani Sudarman