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Digital Transformation Journey of SMEs in Indonesia During the Post Pandemic Era
No full textThe COVID-19 pandemic marked a significant turning point for small and medium enterprises (SMEs) in Indonesia, compelling many to rapidly transi- tion from traditional offline operations to digital business models. This study aims to analyze the digitalization journey of SMEs in the post-pandemic era by examining the opportunities and challenges they encounter during this transformation. A descriptive qualitative approach was employed, using secondary data sourced from official reports such as BPS, the Ministry of Cooperatives and SMEs, and the economy SEA Report, complemented by relevant academic literature. The findings reveal that digitalization has opened substantial oppor- tunities for SMEs, including expanded market reach, improved operational ef- ficiency, and enhanced customer engagement through e-commerce platforms, digital payment systems, and social media marketing. However, persistent bar- riers remain, such as limited digital literacy, uneven internet infrastructure, high implementation costs, and consumer trust issues in online transactions. This study concludes that while digitalization offers a vital pathway for SME growth and resilience, addressing these barriers requires collaborative efforts among business owners, policymakers, and digital platform providers. Future research should employ quantitative methods and explore sector-specific case studies to deepen understanding of digital transformation strategies
Utilizing Generative AI Models in Architectural Design An Innovative Approach
No full textIn the context of modern architectural design that demands innovation, speed, and efficiency, the emergence of generative artificial intelligence (AI) introduces a new paradigm in the creative process. This technology enables architects to explore design ideas more rapidly and extensively through diffusion-based algorithms capable of producing complex architectural visuals in a short amount of time. This study aims to empirically evaluate the effectiveness and efficiency of generative AI models, particularly Stable Diffusion v2.1, in supporting the stages of ideation, sketching, and architectural modeling. The research employs both qualitative and quantitative approaches through a comparative experiment between manual design and AI-assisted design. Measurements were conducted using four main parameters: production time, visual complexity, rendering sharpness, and the number of design iterations. The results indicate that the generative AI model can accelerate production time by up to 35% greater efficiency compared to the manual method. Furthermore, the Visual Complexity Score (VCS) reached 8.5/10 for AI-generated designs and 6.2/10 for manual ones, with an increase in rendering resolution up to 450 PPI. However, limitations were observed in semantic interpretation and the model’s dependence on well-crafted prompts. This study concludes that the integration of generative AI in architectural design not only enhances the efficiency and effectiveness of the design process but also expands the creative potential of architects. The research contributes to the development of sustainable digital architecture and supports the achievement of SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities and Communities). In the context of modern architectural design that demands innovation, speed, and efficiency, the emergence of generative artificial intelligence (AI) introduces a new paradigm in the creative process. This technology enables architects to explore design ideas more rapidly and extensively through diffusion-based algorithms capable of producing complex architectural visuals in a short amount of time. This study aims to empirically evaluate the effectiveness and efficiency of generative AI models, particularly Stable Diffusion v2.1, in supporting the stages of ideation, sketching, and architectural modeling. The research employs both qualitative and quantitative approaches through a comparative experiment between manual design and AI-assisted design. Measurements were conducted using four main parameters: production time, visual complexity, rendering sharpness, and the number of design iterations. The results indicate that the generative AI model can accelerate production time by up to 35% greater efficiency compared to the manual method. Furthermore, the Visual Complexity Score (VCS) reached 8.5/10 for AI-generated designs and 6.2/10 for manual ones, with an increase in rendering resolution up to 450 PPI. However, limitations were observed in semantic interpretation and the model’s dependence on well-crafted prompts. This study concludes that the integration of generative AI in architectural design not only enhances the efficiency and effectiveness of the design process but also expands the creative potential of architects. The research contributes to the development of sustainable digital architecture and supports the achievement of SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities and Communities).
Software Based Geotechnical Analysis of Spillway Slope Reinforcement
No full textThe construction of large-scale infrastructure such as dams requires precise planning and implementation to ensure structural stability and long-term operational safety. This study aims to analyze slope protection and embankment reinforcement at the spillway excavation area of the Dam Project in Surian District, located in the 137 km-long Cipunagara River Basin. A quantitative approach was applied, using geotechnical modeling software Plaxis 8.6 and Microsoft Excel for data processing. Three slope reinforcement methods were compared: geomat, shotcrete, and cocomesh. Based on the analysis, slope reinforcement using shotcrete achieved the highest safety factor, with short-term SF = 1.649 (34.50% increase) and long-term SF = 1.514 (23.49% increase). Geomat ranked second with SF = 1.544 (25.94%) and 1.418 (15.66%), while cocomesh obtained SF = 1.540 (25.61%) and 1.355 (10.52%). The unreinforced slope had an SF of 1.226. In terms of cost, shotcrete was the most expensive at IDR 4,331,068,967.23, followed by geomat (IDR 2,734,025,024.50) and cocomesh (IDR 906,745,000.00). However, shotcrete also required the longest construction time (30 days) compared to geomat (20 days) and cocomesh (16 days). Shotcrete provides the best technical performance for slope stability, but geomat and cocomesh are more efficient alternatives in terms of cost and time, offering a balanced solution for slope reinforcement projects.The construction of large-scale infrastructure such as dams requires precise planning and implementation to ensure structural stability and long-term operational safety. This study aims to analyze slope protection and embankment reinforcement at the spillway excavation area of the Dam Project in Surian District, located in the 137 km-long Cipunagara River Basin. A quantitative approach was applied, using geotechnical modeling software Plaxis 8.6 and Microsoft Excel for data processing. Three slope reinforcement methods were compared: geomat, shotcrete, and cocomesh. Based on the analysis, slope reinforcement using shotcrete achieved the highest safety factor, with short-term SF = 1.649 (34.50% increase) and long-term SF = 1.514 (23.49% increase). Geomat ranked second with SF = 1.544 (25.94%) and 1.418 (15.66%), while cocomesh obtained SF = 1.540 (25.61%) and 1.355 (10.52%). The unreinforced slope had an SF of 1.226. In terms of cost, shotcrete was the most expensive at IDR 4,331,068,967.23, followed by geomat (IDR 2,734,025,024.50) and cocomesh (IDR 906,745,000.00). However, shotcrete also required the longest construction time (30 days) compared to geomat (20 days) and cocomesh (16 days). Shotcrete provides the best technical performance for slope stability, but geomat and cocomesh are more efficient alternatives in terms of cost and time, offering a balanced solution for slope reinforcement projects
Impact of Digital Innovations on Business Competitiveness and Sustainability – A Data-Driven Approach
No full textThis study examines the critical interplay between digital innovations, business competitiveness, and sustainability, providing empirical evidence on their inter- connections. Digital innovations, encompassing technologies such as artificial intelligence (AI), blockchain, and the Internet of Things (IoT), have emerged as transformative forces driving efficiency and enabling sustainable practices. The research employs a quantitative design, utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze data from diverse organi- zations. Results confirm that digital innovations significantly enhance busi- ness competitiveness, which in turn mediates their positive impact on sustain- ability outcomes. The findings underscore the necessity for strategic align- ment of technological adoption with business objectives to achieve competitive differentiation and meet sustainability goals. Key insights reveal that sector- specific strategies are essential for maximizing the benefits of digital transfor- mation. Manufacturing benefits from predictive maintenance, retail achieves supply chain transparency, finance leverages blockchain for ESG reporting, and technology focuses on scalable, sustainability-integrated solutions. The study also highlights the role of supportive regulatory frameworks and cross-sector collaboration in fostering innovation and sustainability. These insights con- tribute to academic discourse and provide actionable guidance for policymak- ers and industry leaders. Future research should explore longitudinal impacts and cross-industry dynamics to deepen the understanding of digital innovations’ role in sustainable economic growth
Optimizing Decision-Making for Placement and Quantity of Tower Cranes in High-Rise Building Projects
No full textThe optimal placement and selection of tower cranes are critical in high-rise construction projects due to their significant impact on operational efficiency, costs, and safety. Misplacement or an inadequate number of cranes can lead to extended project duration, higher expenses and increased safety risks. This study investigates the factors influencing optimal tower crane placement, evaluates existing methodologies, and introduces a simulation-based approach for enhanced decision-making. Using a case study method, data were collected through site observations, interviews with project managers, and computer simulations. Findings reveal that simulation tools can optimize crane productivity, minimize operational costs, and enhance workplace safety. Integrating advanced technologies such as Building Information Modeling (BIM) further supports accurate placement and operational efficiency. These results underline the importance of leveraging technology to address challenges in high-rise construction management. The study concludes with insights on the generalizability of findings and recommendations for future research, emphasizing real-time monitoring integration
Influence of Incentives and Work Motivation on Employee Productivity at PT Panelindo Graha Nusantara
No full textThis study investigates the impact of incentives and work motivation on em- ployee productivity at PT Panelindo Graha Nusantara, a company operating in the electrical panel manufacturing sector. The importance of employee produc- tivity in achieving organizational goals motivates this research, particularly in the context of human resource management strategies. The objective is to analyze both the partial and simultaneous effects of incentives and motivation on sample of 72 employees. Data were collected using structured questionnaires and analyzed through classical assumption tests, multiple linear regression, and determination coefficient analysis using SPSS software. The results indicate that incentives and work motivation both have significant positive effects on employee productivity individually and simultaneously. The partial tests show significance with t-values exceeding the critical t-value, and the simultaneous test reveals a significant F-value well above the threshold. The coefficient of determination suggests that 65.3% of the productivity variance can be explained by these two variables. In conclusion, the findings emphasize the critical role of properly designed incentive systems and motivation strategies in enhancing employee productivity. These insights provide valuable guidance for managers aiming to optimize workforce performance in similar industrial setting
Implementasi CNN dan MediaPipe dalam Peningkatan Efektivitas Stretching pada Olahraga Futsal: Implementation of CNN and MediaPipe in Increasing the Effectiveness of Stretching in Futsal Sports
Full text linkThis study aims to develop an effective Convolutional Neural Network (CNN) model in recognizing stretchingmovements that are often performed by futsal players, with the aim of reducing the risk of injury. The dataset usedconsists of 3000 images covering five types of movements: High Knees, Jumping Jacks, Lunge, Side Lunge, and ButtKicks. The data was taken from YouTube videos and processed to produce landmarks through MediaPipe technology. The CNN model was trained using the ”Adam” optimizer, with 50 epochs, a batch size of 8, and a learning rate of 0.001. The training results showed an accuracy of 94%, with the best performance on the Lunge and Jumping Jack movements, and adequate performance on other movements. The implementation of this model allows real-time monitoring of stretching movements, provides direct feedback to users, and helps futsal players in stretching with the right technique to avoid injury. This study shows that the CNN-based approach for stretching motion recognition in futsal is effective and reliable. Furtherresearch is suggested to increase the amount of training data and explore different model architectures to strengthen the model’s generalization.Penelitian ini bertujuan untuk mengembangkan model Convolutional Neural Network (CNN) yang efektif dalam mengenali gerakan stretching yang sering dilakukan oleh pemain futsal, dengan tujuan untuk mengurangi risiko cedera. Dataset yang digunakan terdiri dari 3000 gambar yang mencakup lima jenis gerakan: High Knees, Jumping Jacks, Lunge, Side Lunge, dan Butt Kicks. Data diambil dari video YouTube dan diproses untuk menghasilkan landmarkmelalui teknologi MediaPipe. Model CNN dilatih dengan menggunakan optimizer ”Adam”, dengan epochs sebanyak 50, batch size 8, dan learning rate 0.001. Hasil pelatihan menunjukkan akurasi sebesar 94%, dengan performa terbaik pada gerakan Lunge dan Jumping Jack, serta performa yang memadai pada gerakan lainnya. Implementasi model ini memungkinkan pemantauan gerakan stretching secara real-time, memberikan umpan balik langsung kepada pengguna, dan membantu pemain futsal dalam melakukan stretching dengan teknik yang tepat untuk menghindari cedera. Penelitian ini menunjukkan bahwa pendekatan berbasis CNN untuk pengenalan gerakan stretching dalam olahraga futsal efektif dan dapat diandalkan. Penelitian selanjutnya disarankan untuk meningkatkan jumlah data pelatihan dan mengeksplorasi arsitektur model yang berbeda untuk memperkuat generalisasi model
Evaluasi Keberhasilan Implementasi Sistem Informasi Manajemen Proyek dalam Meningkatkan Efektivitas Tim Kerja: Evaluating the Success of Project Management Information System Implementation to Enhance Work Team Effectiveness
Full text linkThis research aims to} evaluate the success of the implementation of the Project Management Information System (SIMP) in increasing the effectiveness of work teams, by utilizing key performance indicators (KPI) as a measuring tool. In the era of digitalization, the use of SIMP is increasingly widespread in various organizations to manage projects and optimize team collaboration. However, although many companies adopt SIMP, implementation results often vary, depending on the effectiveness of using the system in daily practice. The approach used in this research is quantitative descriptive, with data collection through questionnaire surveys to team members who have actively used SIMP in their work for a minimum of six months. Some of the main performance indicators evaluated include time efficiency, quality of work results, team satisfaction, and effectiveness of communication between team members. Data analysis was carried out descriptively to assess the performance of each indicator and identify roles SIMP in supporting overall team performance. The research results show that SIMP plays a significant role in increasing the efficiency of task completion times, improving the quality of project results by reducing errors, increasing team member satisfaction with better collaboration, and facilitating more effective communication between members. Based on these results, it is concluded that SIMP implementation is able to support the effectiveness of the work team as a whole, although there are challenges such as the need for initial adaptation and training. Recommendations are given that companies carry out regular training and system maintenance to ensure the continued effectiveness of SIMP use. It is hoped that this research will provide guidance for other organizations in evaluating and optimizing use SIMP at their workplace.Penelitian ini bertujuan untuk mengevaluasi keberhasilan implementasi Sistem Informasi Manajemen Proyek (SIMP) dalam meningkatkan efektivitas tim kerja, dengan memanfaatkan indikator kinerja utama (Key Performance Indicators/KPI) sebagai alat ukur. Dalam era digitalisasi, penggunaan SIMP semakin meluas dalam berbagai organisasi untuk mengelola proyek dan mengoptimalkankolaborasi tim. Namun, meskipun banyak perusahaan mengadopsi SIMP, hasil implementasinya sering kali bervariasi, bergantung pada efektivitas penggunaan sistem tersebut dalam praktik sehari-hari. Pendekatan yang digunakan dalam penelitian ini adalah deskriptif kuantitatif, dengan pengumpulan data melalui survei kuesioner kepada anggota tim yang secara aktif menggunakan SIMP dalam pekerjaan mereka selama minimal enam bulan. Beberapa indikator kinerja utama yang dievaluasi mencakup efisiensi waktu, kualitas hasil kerja, kepuasan tim, dan efektivitas komunikasi antar anggota tim. Analisis data dilakukan secara deskriptif untuk menilai performa setiap indikator dan mengidentifikasi peran SIMP dalam mendukung kinerja tim secara keseluruhan. Hasil penelitian menunjukkan bahwa SIMP berperan signifikan dalam meningkatkan efisiensi waktu penyelesaian tugas, memperbaiki kualitas hasil proyek dengan mengurangi kesalahan, meningkatkan kepuasan anggota tim terhadap kolaborasi yang lebih baik, serta memfasilitasi komunikasi yang lebih efektif di antara anggota tim. Berdasarkan hasil ini, disimpulkan bahwa implementasi SIMP mampu mendukung efektivitas tim kerja secara keseluruhan, walaupun terdapat tantangan seperti kebutuhan adaptasi awal dan pelatihan. Rekomendasi diberikan agar perusahaan melakukan pelatihan berkala dan pemeliharaan sistem untuk memastikan keberlanjutan efektivitas penggunaan SIMP. Penelitian ini diharapkan dapat memberikan panduan bagi organisasi lain dalam mengevaluasi dan mengoptimalkan penggunaan SIMP di tempat kerja mereka
The Role of RDI and Technology Emphasis on Operation and Sales Performance that Implies Financial Performance
No full textCompetition among companies, particularly in the technology and telecommunications sectors, demands a high level of innovation to meet consumer needs. The use of advanced technology and investment in research and development (R&D) are expected to be key factors in creating a competitive advantage amid intense business competition. The use of narratives in corporate reports is also expected to build a positive image among stakeholders, foster trust, and attract market attention. This study aims to examine the effect of R&D intensity and technology emphasis on financial performance through the mediation of operational and sales performance in technology and telecommunications companies. The research gap identified is the limited number of studies related to R&D in technology and telecommunications companies, especially those utilizing a content analysis approach. The research uses panel data and is conducted on three technology and telecommunications companies listed on the Indonesia Stock Exchange (IDX) during the 2019–2023 period. The samples were selected using a purposive sampling method. The results show that technology emphasis and operational performance have a significant influence on a company’s financial performance, while the relationships among other variables were found to be insignificant. Therefore, companies are encouraged to strengthen their technology emphasis and operational efficiency to enhance productivity and improve financial performance.Competition among companies, particularly in the technology and telecommunications sectors, demands a high level of innovation to meet consumer needs. The use of advanced technology and investment in research and development (R&D) are expected to be key factors in creating a competitive advantage amid intense business competition. The use of narratives in corporate reports is also expected to build a positive image among stakeholders, foster trust, and attract market attention. This study aims to examine the effect of R&D intensity and technology emphasis on financial performance through the mediation of operational and sales performance in technology and telecommunications companies. The research gap identified is the limited number of studies related to R&D in technology and telecommunications companies, especially those utilizing a content analysis approach. The research uses panel data and is conducted on three technology and telecommunications companies listed on the Indonesia Stock Exchange (IDX) during the 2019–2023 period. The samples were selected using a purposive sampling method. The results show that technology emphasis and operational performance have a significant influence on a company’s financial performance, while the relationships among other variables were found to be insignificant. Therefore, companies are encouraged to strengthen their technology emphasis and operational efficiency to enhance productivity and improve financial performance
Sustainable Digital Business Model Innovation through Learning Factory and AI
No full textThe digital era compels organizations to continuously innovate in creating sustainable digital business models that integrate technology, human resources, and environmental sustainability. Within this transformation, the Learning Factory emerges as an innovative concept that connects theoretical learning with practical application, enabling students and industry practitioners to co-create solutions through real-world, technology-based projects. Simultaneously, Artificial Intelligence (AI) enhances analytical, predictive, and adaptive capabilities, driving efficiency, innovation, and data-driven decision-making across organizational processes. This study aims to explore the synergy between LearningFactory and AI as a strategic driver of innovation in competitive and sustainable digital business models aligned with the Sustainable Development Goals (SDGs), particularly SDG 4 (Quality Education), SDG 8 (Decent Work and Economic Growth), and SDG 9 (Industry, Innovation, and Infrastructure). Through a qualitative methodology combining literature review and case study analysis of technology-based organizations and higher education institutions implementing the Learning Factory framework, the research identifies how AI integration strengthens learning outcomes, accelerates digital transformation, and promotes sustainability-driven innovation. The findings reveal that this synergy fostersadaptability, enhances human resource competencies, and generates economic,social, and environmental value. Furthermore, it encourages universities and industries to co-develop agile ecosystems that nurture startupreneurship, continuous learning, and inclusive innovation. Ultimately, this study provides strategic recommendations for designing adaptive, competitive, and sustainable digital business models that empower human potential while advancing organizational resilience in the era of rapid technological disruption.The digital era compels organizations to continuously innovate in creating sustainable digital business models that integrate technology, human resources, and environmental sustainability. Within this transformation, the Learning Factory emerges as an innovative concept that connects theoretical learning with practical application, enabling students and industry practitioners to co-create solutions through real-world, technology-based projects. Simultaneously, Artificial Intelligence (AI) enhances analytical, predictive, and adaptive capabilities, driving efficiency, innovation, and data-driven decision-making across organizational processes. This study aims to explore the synergy between Learning Factory and AI as a strategic driver of innovation in competitive and sustainable digital business models aligned with the Sustainable Development Goals (SDGs), particularly SDG 4 (Quality Education), SDG 8 (Decent Work and Economic Growth), and SDG 9 (Industry, Innovation, and Infrastructure). Through a qualitative methodology combining literature review and case study analysis of technology-based organizations and higher education institutions implementing the Learning Factory framework, the research identifies how AI integration strengthens learning outcomes, accelerates digital transformation, and promotes sustainability-driven innovation. The findings reveal that this synergy fosters adaptability, enhances human resource competencies, and generates economic, social, and environmental value. Furthermore, it encourages universities and industries to co-develop agile ecosystems that nurture startupreneurship, continuous learning, and inclusive innovation. Ultimately, this study provides strategic recommendations for designing adaptive, competitive, and sustainable digital business models that empower human potential while advancing organizational resilience in the era of rapid technological disruption