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All Journal Sisforma: Journal of Information Systems
Rinabi Tanamal
SINTA ID : 5980678, Scopus ID: 57211608584, Sistem Informasi, Universitas Ciputra, Surabaya
Computer Science & IT Education Engineering Library & Information Science

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Product Demand Analysis Using the XGBoost Algorithm at PT Atmadjaya Sembada Anugerah Okky Julian Atmajaya Tarmoko; Rinabi Tanamal
SISFORMA Vol 13, No 1: May 2026
Publisher : Soegijapranata Catholic University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/sisforma.v13i1.13650

Abstract

PT Atmadjaya Sembada Anugerah is a frozen food manufacturing company that faces challenges in stock management due to unpredictable daily fluctuations in product demand. Inaccurate demand forecasting can lead to inefficiencies in distribution and storage operations. This study aims to apply the Extreme Gradient Boosting (XGBoost) algorithm to forecast product demand using historical daily sales data. The process involves exploratory data analysis, data cleaning, feature engineering for time and statistical variables, and time-based data splitting. The model is trained using features selected through Recursive Feature Elimination and optimized using hyperparameter tuning with Optuna. Evaluation is conducted through TimeSeriesSplit cross-validation and assessed using three standard performance metrics. The results indicate that the model effectively captures seasonal patterns and general demand trends, although it remains limited in responding to sudden demand spikes. These findings support the use of XGBoost as a foundational approach for demand forecasting systems in stock planning, with potential for further improvement through the integration of external data and expanded feature sets.
Development of a Family Meal Plan Application with Voice Recognition, Siri Integration, and Nutrition Management Using Data-Driven Approach Livanty Efatania Dendy; Celinka Eira Jove; Rinabi Tanamal
SISFORMA Vol 13, No 1: May 2026
Publisher : Soegijapranata Catholic University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/sisforma.v13i1.14489

Abstract

Maintaining a healthy diet has become increasingly important as fast-food consumption rises and nutritional balance is often overlooked. This research proposes a technology-based Family Meal Plan Application equipped with voice recognition, Siri integration, nutrition management, and automated grocery lists to assist home cooks in planning healthier meals more efficiently. The application was designed using Figma, developed with Flutter, and integrated with the Siri API to provide a seamless hands-free experience during cooking. A nutrition prediction model was also developed using a Kaggle dataset and deployed locally to generate real-time nutritional analysis. The final results of this study show that the application prototype successfully meets user needs in simplifying weekly meal planning, providing accurate voice-based cooking guidance, and offering automated nutrition calculations for each family member. User testing involving home cooks indicated increased efficiency in meal preparation, reduced cognitive load during recipe execution, and improved awareness of daily nutritional intake. The voice command system operated with high accuracy and responsiveness, while the automated grocery list feature significantly streamlined weekly shopping activities. Overall, the application demonstrates strong potential to support healthier family eating habits through an intelligent, data-driven, and user-friendly solution.
Co-Authors Celinka Eira Jove Livanty Efatania Dendy Okky Julian Atmajaya Tarmoko