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All Journal ADI Bisnis Digital Interdisiplin (ABDI Jurnal) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi International Transactions on Artificial Intelligence (ITALIC)
Lily Maria
Pandawan Incorporation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Library & Information Science Other

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Implemetation of ROP In Stock Control to Minimize Losses Due to Expiry Lukmanul Hakim Aziz; Richard Andre Sunarjo; Muhammad Ramdani; Qurotul Aini; Elisa Ananda Natalia; Lily Maria
ADI Bisnis Digital Interdisiplin Jurnal Vol 6 No 2 (2025): ADI Bisnis Digital Interdisiplin (ABDI Jurnal)
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/abdi.v6i2.1337

Abstract

Managing inventory with a limited shelf life is a crucial challenge in the supply chain, particularly in sectors where products are susceptible to rapid quality deterioration. Inaccuracies in ordering timing often lead to excess stock, which leads to financial losses due to product destruction, increased storage costs, and negative environmental impacts. This situation demands the implementation of more integrated and data-driven inventory control methods to optimize the procurement cycle sustainably. This study aims to analyze the effectiveness of implementing the Reorder Point (ROP) method integrated with historical demand and lead time data in minimizing the percentage of expired items. The main focus of the study is to establish ROP as a precise ordering timing mechanism, so that Safety Stock (SS) functions as an emergency buffer against uncertainty, rather than as excess inventory at risk of expiring. The research methodology includes analytical calculations of ROP, SS to mitigate demand and lead time variability, and Economic Order Quantity (EOQ) to determine the most economical order quantity. In addition, a literature review on the implementation of First Expired, First Out (FEFO) and First In, First Out (FIFO) systems is used as internal operational standards to ensure optimal stock rotation. The analysis results show that accurate ROP implementation is a key pillar in preventing expired goods. An optimal strategy requires synergy between prevention through precise ordering timing, internal control through strict stock rotation, and risk mitigation through proactive discount programs for products nearing expiration. The integration of ROP, SS, and EOQ has proven effective in reducing operational losses and supporting modern, efficient and sustainable inventory management practices.
Integrating Emerging Technologies to Strengthen Montessori Preschool Learning Mardhalia Saitakela; Dimas Aditya Prabowo; Lily Maria; Triyono
Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Vol 4 No 2 (2026): March
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/mentari.v4i2.1070

Abstract

In the context of rapidly evolving educational technologies, Montessori preschool learning requires developmentally appropriate innovations that preserve hands-on exploration while enhancing children’s cognitive and sensorial growth. This study aims to investigate the integration of emerging technologies such as AI-assisted learning applications, multimodal digital storytelling, and sensor-based interactive materials within Montessori environments to understand how these tools can support autonomy, concentration, and individualized learning rhythms. The approach focuses on a qualitative case study involving classroom observations, teacher interviews, and analysis of children’s learning behaviours, with a total of 206 participants, to capture how technology is adopted and adapted in authentic preschool contexts. Findings indicate that technology aligned with Montessori principles increases engagement with sensorial tasks, enriches exploration, strengthens differentiated learning pathways, and provides teachers with accurate insight into developmental progress, although excessive digital exposure may reduce children’s interaction with concrete materials. Therefore, the study concludes that thoughtfully integrated emerging technologies can serve as a natural extension of Montessori learning when applied moderately, grounded in pedagogical intention, and designed to enhance rather than replace physical and self-directed experiences, ultimately demonstrating that balanced integration offers meaningful benefits for young learners.
Vision-Based Pattern Recognition Models for Intelligent Human Robot Interaction in Smart Spaces Muhamad Faizal Fazri; Konita Lutfiyah; Lukita Pasha; Lily Maria
International Transactions on Artificial Intelligence Vol. 4 No. 2 (2026): May
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i2.1101

Abstract

The rapid expansion of smart spaces has increased the need for robotic systems capable of interpreting visual cues, recognizing human behavior, and responding safely in real time. However, existing vision-based models often struggle with occlusion, lighting variation, latency constraints, and limited contextual understanding in dynamic human-centered environments. This study develops a hybrid vision-based pattern recognition framework that integrates Convolutional Neural Networks (CNNs), Transformer-based attention mechanisms, multi-scale feature fusion, supervised learning, and reinforcement learning. The model is trained and validated using publicly available human–robot interaction datasets and simulated smart space scenarios involving gesture recognition, object detection, activity recognition, and intention prediction. The objective is to enhance intelligent human–robot interaction by improving visual perception accuracy, contextual interpretation, adaptive decision-making, and real-time responsiveness in smart environments. The proposed framework achieves stronger performance than baseline CNN-only and Vision Transformer models, with improved accuracy in gesture recognition, object detection, activity recognition, and intention prediction while maintaining low-latency inference suitable for real-time robotic interaction. The model also demonstrates better adaptability under dynamic lighting, occlusion, and multi-person interaction scenarios. This study concludes that combining CNN-based local feature extraction, Transformer-based global attention, and reinforcement learning-based policy optimization provides a reliable, adaptive, and context-aware framework for intelligent robotic systems. The findings support safer and more efficient human–robot collaboration in healthcare, smart homes, collaborative workplaces, and smart city environments.
Co-Authors Dimas Aditya Prabowo Elisa Ananda Natalia Konita Lutfiyah Lukita Pasha Lukmanul Hakim Aziz Mardhalia Saitakela Muhamad Faizal Fazri Muhammad Ramdani Qurotul Aini Richard Andre Sunarjo Triyono