Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal Journal of Applied Business and Technology Luxury: Landscape of Business Administration
Sulaiman Musa
Bayero University, Nigeria
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
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.
Digital Transformation of Goat Milk Supply Chains through an Integrated Smart Cold-Chain Logistics System Nyoto Nyoto; Nicholas Renaldo; Jahrizal Jahrizal; Azridjal Aziz; M. Dalil; Achmad Tavip Junaedi; Yusrizal Yusrizal; Alyauma Hajjah; Sulaiman Musa; Cecilia Cecilia
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/re8a7652

Abstract

The rapid digital transformation of agribusiness has created significant opportunities to improve supply chain efficiency, product quality, and operational sustainability through the integration of smart technologies. However, goat milk supply chains continue to face challenges associated with short product shelf life, inadequate cold-chain infrastructure, limited distribution visibility, and high logistics costs, particularly among small and medium-sized enterprises (SMEs). This study aims to develop and evaluate an Integrated Smart Cold-Chain Goat Milk Logistics System (SCGLMS) as a digital transformation model for goat milk supply chains. A Design Science Research (DSR) methodology was employed, consisting of problem identification, system design, prototype development, pilot implementation, and system evaluation. The proposed SCGLMS integrates four interconnected layers: smart milk processing, intelligent packaging, IoT-enabled cold-chain logistics, and a cloud-based digital management platform that provides real-time monitoring, digital traceability, and logistics analytics. The findings demonstrate that the integrated system significantly improves supply chain visibility, operational coordination, and decision-making by enabling continuous monitoring of temperature, humidity, shipment status, and product quality throughout distribution. The system also enhances logistics efficiency, minimizes product spoilage, extends market accessibility, and strengthens customer confidence through improved transparency and traceability. This study contributes to the literature on digital supply chain management by proposing a comprehensive technological framework specifically designed for goat milk logistics. Furthermore, it provides practical guidance for SMEs, agribusiness practitioners, and policymakers seeking to accelerate digital transformation and sustainable supply chain development within the 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 Alyauma Hajjah Azridjal Aziz Cecilia Cecilia Dodi Sofyan Arief Jahrizal Jaswar Koto Kristy Veronica M. Dalil Marice Br Hutahuruk Nicholas Renaldo Nindy Daviny Nyoto Wilda Susanti Yusrizal Yusrizal