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All Journal Aptisi Transactions on Technopreneurship (ATT) ADI Journal on Recent Innovation (AJRI) IAIC Transactions on Sustainable Digital Innovation (ITSDI) Startupreneur Business Digital (SABDA Journal) International Transactions on Artificial Intelligence (ITALIC)
Anwar, Muhammad Rehan
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Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Social Sciences

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The Role of Business Incubators in Developing Local Digital Startups in Indonesia Anwar, Muhammad Rehan; Yusup, Muhamad; Millah, Shofiyul; Purnama, Suryari
Startupreneur Business Digital (SABDA Journal) Vol. 1 No. 1 (2022): Startupreneur Business Digital (SABDA)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1278.017 KB) | DOI: 10.33050/sabda.v1i1.69

Abstract

The development of the internet causes the flow of information to move quickly without knowing geographical boundaries. Likewise with the development of the digital industry, although the condition of the digital industry in Indonesia is still in its early phase, where infrastructure and ecosystem support is still very minimal, the optimism from digital industry players in Indonesia is very strong, both from the startup side and from investors. Problems arise when investors, both local and foreign, wish to invest in local digital startups in Indonesia, namely the unpreparedness of local startups to receive relatively large amounts of funding for business development. This raises doubts for investors whether startups can manage the funds raised and generate future profits for investors. Therefore, an initiative was born from investors and stakeholders in the digital technology industry to activate business incubators, with the aim of being able to prepare local startups to be able to develop more optimally. The results of this study reveal various tangible benefits received by local startups to increase their capacity.
Integrating Artificial Intelligence and Environmental Science for Sustainable Urban Planning Anwar, Muhammad Rehan; Sakti, Lintang Dwi
IAIC Transactions on Sustainable Digital Innovation (ITSDI) Vol 5 No 2 (2024): April
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/itsdi.v5i2.666

Abstract

The rapid urbanization of modern cities presents significant challenges in sustainable development. To address these challenges, there is a growing integration of Artificial Intelligence (AI) and Environmental Science to enhance urban planning processes. This research aims to assess the impact and utility of AI techniques within the framework of Geographic Information Systems (GIS) for sustainable urban planning. Specifically, it investigates how AI-enhanced GIS tools can be employed to improve urban development strategies and enhance sustainability assessments. Employing Spatial Analysis with GIS, this study analyzes data on land use, population density, and environmental indicators across several metropolitan areas. The methodology incorporates machine learning algorithms to predict and simulate urban growth patterns, enabling the assessment of various urban planning scenarios. The findings reveal that AI-enhanced GIS tools significantly improve the precision of development forecasts and sustainability assessments. These tools facilitate more informed decision-making in urban planning by enabling precise predictions about urban expansion and its environmental impacts. The integration of AI with environmental science not only enhances the efficiency of urban planning processes but also contributes to the resilience and sustainability of urban environments. The study provides urban planners and policymakers with advanced tools to forecast and mitigate the environmental impacts of urbanization, thereby setting a benchmark for future studies in the realm of sustainable urban planning. This research demonstrates the practical application of AI in enhancing the capabilities of GIS for complex spatial analyses, contributing significantly to the field of urban planning.
The Future of Adaptive Machine Learning Algorithms in High-Dimensional Data Processing Wilson, Anne; Anwar, Muhammad Rehan
International Transactions on Artificial Intelligence Vol. 3 No. 1 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v3i1.656

Abstract

This study investigates the potential of adaptive machine learning algorithms for processing high-dimensional data across various fields, directly supporting the advancement of the United Nations Sustainable Development Goals (SDGs) such as healthcare, economic growth, and sustainable cities. The core objectives are to critically review existing methods, tackle the challenges posed by large datasets, and project future developments in adaptive machine learning technologies. Through a comprehensive analysis of diverse algorithms including autoencoders, deep learning, reinforcement learning, and ensemble methods this research evaluates their efficacy in managing the complexities of large-scale data. Results demonstrate that while deep learning models provide the highest accuracy, they also demand considerable computational resources. Conversely, ensemble methods and autoencoders show competitive performance with greater efficiency, although reinforcement learning exhibits adaptability at the cost of reduced scalability. The findings advocate for enhanced focus on improving the efficiency, generalization capabilities, and interpretability of these algorithms to better accommodate the increasing complexity of data-driven environments. Promising applications identified include enhancing diagnostic accuracy in healthcare, optimizing financial analytics, and advancing autonomous system technologies. The study concludes that significant progress in adaptive machine learning will be crucial for achieving SDGs by enabling more effective and efficient data analysis solutions, thereby fostering sustainable development across multiple domains.
Hash Algorithm In Verification Of Certificate Data Integrity And Security Anwar, Muhammad Rehan; Apriani, Desy; Adianita, Irsa Rizkita
Aptisi Transactions On Technopreneurship (ATT) Vol 3 No 2 (2021): September
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v3i2.212

Abstract

The hash function is the most important cryptographic primitive function and is an integral part of the blockchain data structure. Hashes are often used in cryptographic protocols, information security applications such as Digital Signatures and message authentication codes (MACs). In the current development of certificate data security, there are 2 (two) types of hashes that are widely applied, namely, MD and SHA. However, when it comes to efficiency, in this study the hash type SHA-256 is used because it can be calculated faster with a better level of security. In the hypothesis, the Merkle-Damgård construction method is also proposed to support data integrity verification. Moreover, a cryptographic hash function is a one-way function that converts input data of arbitrary length and produces output of a fixed length so that it can be used to securely authenticate users without storing passwords locally. Since basically, cryptographic hash functions have many different uses in various situations, this research resulted in the use of hash algorithms in verifying the integrity and authenticity of certificate information.
Implementation Design In the Creation of Companies In the 4.0 Technology Era Anwar, Muhammad Rehan; Sari, Siti Nurindah; Maesaroh, Siti; Haryanto; Widada, Sugeng
Aptisi Transactions On Technopreneurship (ATT) Vol 4 No 1 (2022): March
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v4i1.244

Abstract

Industry 4.0 has been referred to as a new modern stage in which various developing technologies come together to deliver continuous innovation. After all, there is no understanding of how the organization does this innovation. After all, no one knows how the company performs this type of innovation. In this way, we expect to gain a better understanding of how Industry 4.0 ideas are accepted in assembly plants. We present a theoretical framework for this advancement, which we divide into two categories: front-end and base innovation. Front-end innovation considers four factors: smart manufacturing, smart products, a smart inventory network, and smart work, whereas basic advancement considers four factors: the internet of things, cloud administration, big data, and investigation. The sampling method, variable definition, sample and variance method, and data analysis are all used in this study. To focus on adopting these advancements, we reviewed 92 assembly organizations. Our findings imply that Industry 4.0 is linked to a fundamental embrace of front-end innovations, with Smart Manufacturing taking center stage. Our findings also suggest that implementing basic innovations is putting the organization to the test, given the sample in question still has very little knowledge and testing. We propose the creation of an Industry 4.0 innovation layer and demonstrate the extent of adoption of these advancements as well as their company-building ideas.
Optimizing Stock Accuracy with AI and Blockchain for Better Inventory Management Putra, Fengki Eka; Khasanah, Miftakhul; Anwar, Muhammad Rehan
ADI Journal on Recent Innovation Vol. 6 No. 2 (2025): March
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v6i2.1200

Abstract

The optimization of stock Opname accuracy is crucial for effective inventory management and improved sales outcomes. Traditional inventory management systems often struggle with accuracy due to manual processes and lack of real time data, leading to inefficiencies and sales losses. This study examines the impact of integrating AI and Blockchain technology to enhance stock Opname accuracy, optimize inventory management, and improve sales performance in business operations. A quantitative research approach was employed, utiliz- ing AI algorithms for predictive analytics and Blockchain for secure, transpar- ent recordkeeping. The results indicate a 30% improvement in stock Opname accuracy, a 60% reduction in inventory discrepancies, and a 50% decrease in reconciliation time. Additionally, businesses implementing AI and Blockchain experienced a 15% increase in sales performance and a 67% reduction in stock- out issues. These findings highlight that the combination of AI and Blockchain significantly reduces human error, enhances real time tracking, and provides tamper proof records, leading to more efficient inventory management and in- creased sales outcomes. This study contributes to the field by providing empirical evidence on the effectiveness of AI and Blockchain in inventory manage- ment, offering a framework for businesses seeking to improve stock Opname accuracy and optimize operational efficiency. The integration of these technologies presents a promising solution for modern inventory management, enabling businesses to respond to demand fluctuations with greater precision.
Co-Authors Adianita, Irsa Rizkita Desy Apriani HARYANTO Miftakhul Khasanah Millah, Shofiyul Muhamad Yusup Putra, Fengki Eka Sakti, Lintang Dwi Siti Maesaroh siti Nurindah Sari Sugeng Widada Suryari Purnama Wilson, Anne