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All Journal Journal of Applied Business and Technology Reflection: Education and Pedagogical Insights Luxury: Landscape of Business Administration
Jaswar Koto
Ocean and Aerospace Research Institute, Japan
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences Other

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Smart Processing Machines and Business Efficiency in Goat Milk Agro-Enterprises Achmad Tavip Junaedi; Harry Patuan Panjaitan; Nicholas Renaldo; Nyoto Nyoto; Jahrizal Jahrizal; M Dalil; Jaswar Koto; Sulaiman Musa; Nabila Wahid; Kristy Veronica; Umar Faruq
Luxury: Landscape of Business Administration Vol. 3 No. 2 (2025): Luxury: Landscape of Business Administration
Publisher : First Ciera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61230/luxury.v3i2.137

Abstract

The increasing demand for functional and health-oriented dairy products has positioned goat milk agro-enterprises as a promising business sector, particularly in emerging economies. Despite this potential, many goat milk businesses face persistent challenges related to production inefficiency, high operational costs, and limited scalability. This study aims to examine the impact of smart processing machines on business efficiency in goat milk agro-enterprises. Using a quantitative approach, data were collected from small and medium-sized goat milk processing enterprises and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results reveal that smart processing machine adoption has a positive and significant effect on business efficiency, including cost efficiency, productivity, and operational effectiveness. The findings indicate that smart processing machines function not merely as technological tools but as strategic business resources that enhance operational performance and competitiveness. This study contributes to the business and agribusiness literature by providing empirical evidence at the production-machine level and highlighting the strategic value of smart manufacturing technologies in small-scale agro-enterprises. The findings offer practical insights for business owners, policymakers, and technology developers in promoting sustainable and efficient goat milk processing businesses.
Big Data Analytics for Demand Forecasting in the Mushroom Supply Chain Nicholas Renaldo; Kristy Veronica; Achmad Tavip Junaedi; Suhardjo Suhardjo; Amries Rusli Tanjung; Sri Indrastuti; Wilda Susanti; Jaswar Koto; Sulaiman Musa; Nabila Wahid
Luxury: Landscape of Business Administration Vol. 4 No. 1 (2026): Luxury: Landscape of Business Administration
Publisher : First Ciera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61230/luxury.v4i1.138

Abstract

The mushroom industry plays an increasingly important role in the agri-food sector due to rising demand for nutritious, functional, and sustainable food products. However, the mushroom supply chain faces significant challenges related to perishability, short shelf life, and demand uncertainty, which often result in inventory losses and inefficiencies. This study examines the role of big data analytics capability in enhancing demand forecasting accuracy and its impact on supply chain performance within the mushroom industry. Using a quantitative explanatory research design, data were collected through a structured questionnaire survey of mushroom supply chain actors, including producers, processors, distributors, and retailers. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results reveal that big data analytics capability has a significant positive effect on demand forecasting accuracy and supply chain performance. Furthermore, demand forecasting accuracy partially mediates the relationship between big data analytics capability and supply chain performance. These findings highlight the strategic importance of data-driven forecasting in managing demand uncertainty and improving operational efficiency in perishable agribusiness supply chains. This study contributes to the literature by extending big data analytics and demand forecasting research to the mushroom industry, providing both theoretical insights and practical implications for enhancing supply chain sustainability and competitiveness.
Green Digital Education Model with AI and IoT Integration in Sustainable Goat Farming Curriculum Arih Dwi Prihastomo; Nicholas Renaldo; Achmad Tavip Junaedi; Marice Br Hutahuruk; Muhammad Pringgo Prayetno; Umar Faruq; Jaswar Koto; Jahrizal Jahrizal; Nyoto Nyoto; Luciana Fransisca
Reflection: Education and Pedagogical Insights Vol. 3 No. 1 (2026): Reflection: Education and Pedagogical Insights
Publisher : First Ciera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61230/reflection.v3i1.149

Abstract

The rapid advancement of Artificial Intelligence (AI) and Internet of Things (IoT) technologies has transformed agricultural production systems; however, their integration into agricultural education remains limited. This study develops and evaluates a Green Digital Education Model that integrates AI, IoT, and Material Flow Cost Accounting (MFCA) into a Sustainable Goat Farming Curriculum. Using a Research and Development (R&D) approach, the study followed four phases: needs analysis and curriculum mapping, system development and technological integration, pilot implementation, and evaluation. IoT sensors were deployed to collect real-time environmental and livestock data, which were integrated into a cloud-based dashboard and an AI-driven Decision Support System (DSS). An MFCA module was incorporated to enable environmental cost analysis and greenhouse gas emission calculations based on standardized methodologies. Pilot implementation in selected university courses demonstrated significant improvements in students’ digital literacy, sustainability awareness, and analytical decision-making skills, as evidenced by pre-test and post-test comparisons. Qualitative findings indicated increased engagement, motivation, and interdisciplinary collaboration. The model transforms conventional livestock education into a technology-driven “living laboratory,” aligning agricultural curricula with Education 4.0 principles and sustainability reporting standards. The study contributes a scalable framework for integrating green technology and digital innovation into higher education, supporting environmentally responsible and data-driven agricultural practices.
AI-Based Digital Twin Development for Optimizing Hydrogen Production, Distribution, and Business Profitability Nicholas Renaldo; Jaswar Koto; M. Dalil; Dodi Sofyan Arief; Sulaiman Musa; Nindy Daviny; Cecilia Cecilia; Kristy Veronica
Journal of Applied Business and Technology Vol. 7 No. 1 (2026): Jounal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/s04p2v22

Abstract

The transition toward a low-carbon energy system has increased interest in green hydrogen as an energy carrier for renewable energy integration, industrial applications, and sustainable transportation. However, the economic competitiveness of hydrogen remains constrained by the complexity of coordinating renewable-energy availability, electrolyzer operation, hydrogen storage, distribution, market demand, and profitability. This study proposes an AI-Based Hydrogen Business Digital Twin (HBDT) to optimize hydrogen production, distribution, and business profitability through an integrated digital decision-making framework. The research employs a simulation-based development approach that combines Digital Twin technology, Artificial Intelligence, predictive analytics, multi-objective optimization, and techno-economic analysis. Several machine-learning models, including Random Forest, Support Vector Regression, XGBoost, Artificial Neural Network, and Long Short-Term Memory (LSTM), are evaluated for predictive performance. The simulation results indicate that LSTM provides the strongest performance, achieving an MAE of 0.041, RMSE of 0.068, and R2 of 0.981. Scenario analysis demonstrates that profitability increases from 12.5% under fixed production and distribution to 32.4% under the integrated AI, Digital Twin, and optimization scenario. The techno-economic simulation further indicates reductions in hydrogen production cost, levelized cost of hydrogen, and distribution costs, accompanied by improvements in renewable-energy utilization, revenue, ROI, and payback period. These findings demonstrate that the proposed HBDT can transform hydrogen management from a static and reactive process into a predictive, prescriptive, and potentially autonomous business ecosystem. The study contributes to the emerging concept of Hydrogen Business 4.0, in which technical operations and economic decisions are continuously optimized through AI and Digital Twin technologies.
Beyond Cold Storage and Smart Milk Bank as a Disruptive Supply Chain Innovation for Strengthening National Dairy Industry Resilience Nicholas Renaldo; Achmad Tavip Junaedi; Suhardjo Suhardjo; Azridjal Aziz; M. Dalil; Erpiani Siregar; Jahrizal Jahrizal; Umar Faruq; Jaswar Koto; Kristy Veronica
Journal of Applied Business and Technology Vol. 7 No. 2 (2026): Jounal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/b277se34

Abstract

The national dairy industry faces persistent challenges related to fluctuating milk supply, quality deterioration, limited traceability, and inefficiencies in conventional milk collection and storage systems. This study proposes the Smart Milk Bank (SMB) as a disruptive supply chain innovation that moves beyond the conventional function of cold storage by integrating rapid cooling, cold-chain management, real-time quality monitoring, digital traceability, inventory management, and an intermediary business model. An applied research and technology development approach is employed, involving system requirement analysis, prototype development, functional testing, industrial implementation, and performance evaluation. Data are obtained from milk quality measurements, sensor records, production and inventory data, operational observations, and stakeholder feedback. The proposed system is designed to transform the milk bank into an active supply-chain buffer capable of coordinating milk quality and volume according to industrial demand. Performance evaluation focuses on milk losses, quality consistency, supply stability, operating costs, traceability, and system reliability. The proposed targets include reducing damaged milk from approximately 10–15% to 6–8%, improving managed supply stability by 20–30%, and reducing operating costs by 15–25%. The study positions Smart Milk Bank as a systemic innovation in which technological integration and business-model innovation jointly strengthen dairy supply chain resilience. The findings provide a conceptual and practical foundation for developing a scalable, digitally enabled milk supply system that can support the competitiveness, sustainability, and resilience of the national dairy industry. 
From Waste to Assets through Value Creation Strategies in Goat Farming Nicholas Renaldo; Achmad Tavip Junaedi; Wilda Susanti; Jahrizal Jahrizal; M. Dalil; Kristy Veronica; Marice Br Hutahuruk; Sulaiman Musa; Jaswar Koto; Cecilia Cecilia
Luxury: Landscape of Business Administration Vol. 4 No. 2 (2026): Luxury: Landscape of Business Administration
Publisher : First Ciera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61230/luxury.v4i2.159

Abstract

This study examines the transformation of livestock waste into economic and environmental value through innovation in goat farming. The study applies a qualitative case study approach using observation, interviews, and documentation of a Smart Composting System. The system utilizes leftover fermented corn-stalk and leaf silage, goat manure, horse manure, EM4, and brown sugar, supported by Internet of Things (IoT)-based monitoring of temperature, moisture, and NPK. The findings indicate that the innovation enables organic waste to be transformed into compost while generating operational data that can support more effective management decisions. The integration of environmental accounting further enables the identification of processing costs, potential economic value, and environmental benefits. The study demonstrates that waste management can be repositioned from a cost-oriented activity into a value creation strategy through the integration of resource recovery, digital technology, and environmental-economic measurement. The proposed approach provides a practical foundation for developing circular and sustainable business practices in goat farming.
Co-Authors Achmad Tavip Junaedi Amries Rusli Tanjung Arih Dwi Prihastomo Azridjal Aziz Cecilia Cecilia Dodi Sofyan Arief Erpiani Siregar Harry Patuan Panjaitan Jahrizal Kristy Veronica Luciana Fransisca M. Dalil Marice Br Hutahuruk Marice Br Hutahuruk Muhammad Pringgo Prayetno Nabila Wahid Nicholas Renaldo Nindy Daviny Nyoto Sri Indrastuti Suhardjo Suhardjo Sulaiman Musa Sulaiman Musa Umar Faruq Umar Faruq Wilda Susanti