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This study aims to analyze the effectiveness, contribution, and optimization strategies of Restaurant Tax and Hotel Tax in supporting the sustainability of Local Own-Source Revenue (PAD) of Bogor City. The study employed a descriptive method with a quantitative approach supported by qualitative data. The results indicate that the effectiveness levels of Restaurant Tax and Hotel Tax in Bogor City were categorized as highly effective, with average effectiveness ratios of 107.24%, and 117.74%, respectively. However, the contribution of Restaurant Tax to Local Own-Source Revenue was only 13.55%, which falls into the less contributive category, while the contribution of Hotel Tax was 8.03%, categorized as very low contributive. Based on the SWOT analysis, the reccomended optimization strategies include expanding the tax base through taxpayer data collection and updating, optimizing digital-based payment and monitoring systems, enhancing human resource capacity, strenghening tax education and outreach programs, developing risk based supervision, and improving inter agency cooperation. The implementation of these strategies is expected to increase the revenue and contribution of Restaurant Tax and Hotel Tax in supporting the sustainability of Bogor City’s Local Own-Source Revenue.

This study aims to analyze the effect of Gross Regional Domestic Product (GRDP), Local Taxes, and Population on Local Own-Source Revenue (PAD) in 35 regencies/cities of Central Java Province during 2021–2023. The study employs a quantitative approach using secondary data obtained from the Central Java Statistics Agency (BPS) and Local Government Budget Realization Reports (LRA). The sample was selected using a census sampling technique and analyzed using multiple linear regression after classical assumption testing. The results indicate that GRDP and Local Taxes have a significant effect on PAD, while Population does not have a significant effect on PAD. These findings imply that local governments should prioritize regional economic growth and optimize local tax collection to strengthen fiscal capacity rather than relying solely on population growth. The novelty of this study lies in its integrated examination of GRDP, Local Taxes, and Population across all 35 regencies/cities in Central Java during the 2021–2023 period, providing updated empirical evidence to support regional fiscal policy and local revenue optimization.

The rapid growth of Solar Power Plant (PLTS) installations in Indonesia faces efficiency challenges due to the continuously changing position of the sun. Fixed panel systems cannot absorb solar radiation optimally throughout the day. This study aims to evaluate and compare the efficiency of various solar tracking system methods developed over the last five years using a simple literature review approach. The research method was conducted by collecting, screening, and synthesizing secondary data from five reputable scientific journals using a synthesis matrix. The review results indicate that single-axis tracking systems increase power efficiency by 15% to 24.5%. Meanwhile, dual-axis systems achieve higher efficiency, ranging from 30% to 35%, by tracking both horizontal and vertical solar movements. In terms of control systems, astronomical algorithms are found to be more reliable in cloudy weather conditions than pure light sensors. However, the internal power consumption of the actuator motors remains a critical factor that can reduce the system's net energy gain. The implication of this study emphasizes the importance of shifting future research focus toward energy-saving algorithm optimization to maximize net power yield in dynamic solar panel implementations.

This study aims to develop and evaluate a VisualCommunication Design (VCD) strategy for onlinepromotion through Facebook at CV. Cahaya Grafikain Demak. The research applied a design-basedapproach, combined with audience perception testingusing a Likert-scale questionnaire distributed to 30respondents aged 18–44 years. The developmentproduced five main outputs: Facebook photo posts,profile and cover design, group posts, story-formatcontent, and adaptation for printed banner media. Theresults show significant improvement in visualattractiveness, message clarity, brand recognition,and engagement performance. The findings indicatethat systematic visual optimization enhancespromotional effectiveness in both digital and printmedia.

Tomato (Solanum lycopersicum) is a high-value horticultural commodity in Indonesia, yet its cultivation is frequently disrupted by leaf diseases that are difficult to distinguish visually. Diseases such as Bacterial Spot, Early Blight, and Tomato Yellow Leaf Curl Virus often present overlapping visual symptoms, making early and accurate diagnosis a significant challenge for farmers. The manual identification methods currently in use are inefficient and error-prone, ultimately leading to reduced crop yield  and quality. The general objective of this study is to develop software capable of automatically classifying tomato  leaf diseases. Specifically, this research aims to implement the MobileNetV3 Small architecture based on Convolutional Neural  Network (CNN) with ImageNet pre-trained weights to classify 10 types of tomato leaf diseases. The research methodology encompasses dataset collection from Kaggle comprising 10,000 images (1,000 per class), image pre-processing through resizing to 224x224 pixels, and normalization, as well as hyperparameter optimization (optimizer, learning rate, epoch, batch size) via scheduler. Model performance is evaluated using a confusion matrix encompassing accuracy, precision, recall, and F1-score.

The development of Artificial Intelligence (AI) has driven significant transformation in hospital management, particularly in operational efficiency, service quality, and patient safety. This study aims to analyze the implementation of AI in hospital management based on recent scientific evidence from 2020 to 2026. The method used was a systematic review guided by the PRISMA 2020 framework. Literature was retrieved from PubMed, ScienceDirect, SpringerLink, Google Scholar, and ProQuest. From 360 identified articles, a stepwise selection process was conducted, resulting in 15 articles that met the inclusion criteria. The findings indicate that AI contributes to improved operational efficiency through patient flow optimization, operating room management, workforce scheduling, and electronic medical record management. AI also enhances service quality through predictive data analytics and supports patient safety through risk detection and early warning systems. In conclusion, AI has strong strategic potential to support modern hospital management. However, its implementation still faces several challenges, including human resource readiness, data security, algorithmic bias, system interoperability, and investment requirements. Therefore, AI implementation should be carried out in a planned, ethical manner and evaluated from a health economics perspective.

This study aims to analyze the effect of artificial intelligence (AI)-based demand forecasting and big data analytics on inventory optimization through prediction accuracy among e-commerce actors or marketplace sellers in Curug. This research employed an explanatory quantitative approach involving 100 respondents selected through purposive sampling based on predefined criteria relevant to the research objectives. Data were collected using a structured Likert-scale questionnaire and analyzed using Structural Equation Modeling Partial Least Squares (SEM-PLS) by evaluating the measurement model, structural model, and mediation effects. The findings reveal that AI-based demand forecasting and big data analytics have a positive and significant effect on prediction accuracy. Furthermore, prediction accuracy has a positive and significant effect on inventory optimization and significantly mediates the relationship between AI-based demand forecasting, big data analytics, and inventory optimization. These findings indicate that the integration of AI and big data analytics contributes to more accurate demand prediction, leading to improved inventory management performance. The implication of this study suggests that e-commerce actors should improve data quality, analytical capability, and the adoption of predictive technologies to support more accurate, efficient, and responsive inventory decisions, thereby enhancing operational performance and competitiveness in responding to dynamic market demand.      

The Society 5.0 era brings major changes in various aspects of life, including the economic and financial systems. The integration of digital technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), Big Data, and Financial Technology (Fintech) has created both opportunities and challenges for the development of the sharia economy. This article aims to: (1) analyze the challenges of the sharia economy in the Society 5.0 era, (2) identify opportunities that can be utilized to strengthen the sharia economy, and (3) formulate strategies for strengthening the sharia economy based on digital transformation and the maqashid sharia. This research uses a qualitative approach through literature study (library research) by analyzing various journals, books, reports of sharia financial institutions, and relevant official documents. The results show that the sharia economy faces challenges in the form of low sharia financial literacy, limited human resources, unequal access to technology, and regulations that are not yet fully adaptive to digital developments. However, Society 5.0 also opens up significant opportunities through the development of Islamic Fintech, the digitalization of the halal industry, the optimization of digital zakat and waqf, and the strengthening of Islamic financial inclusion. Therefore, strategies to strengthen the Islamic economy need to be implemented through increasing Islamic digital literacy, developing an Islamic Fintech ecosystem, strengthening Governance based on the principles of Islamic principles (maqasid) and synergy between the government, academia, industry, and the community.

The Kampung Bahari Nusantara (KBN) Program is one of the Indonesian Navy's community empowerment initiatives aimed at improving the welfare of coastal communities through the development of economic, educational, health, tourism, and defense sectors. Tlocor Hamlet, Kedungpandan Village, Jabon District, Sidoarjo Regency, possesses significant coastal resource potential and marine tourism opportunities; however, the implementation of the program has not yet achieved optimal results. This study aims to analyze the implementation of the Kampung Bahari Nusantara Program and formulate strategies to optimize its contribution to community welfare improvement. The research employed a qualitative descriptive approach through observation, interviews, and documentation involving village officials, community members, and representatives of the Indonesian Navy. The findings indicate that the program has positively impacted infrastructure development, tourism growth, and public service accessibility. Nevertheless, several challenges remain, including limited community participation, inadequate human resources, insufficient educational and health personnel, and weak inter-agency coordination. The study concludes that optimization can be achieved through strengthening stakeholder collaboration, enhancing community empowerment, improving infrastructure maintenance, and expanding educational and health services. These efforts are expected to promote sustainable welfare improvement and strengthen maritime resilience in coastal areas.

This research is motivated by the development of injection motorcycle engine management system technology that demands increased performance through optimization of the Electronic Control Unit (ECU). This study aims to analyze the performance comparison of the Yamaha MX King 180 cc using a standard ECU and a racing ECU on engine power and torque. The method used is an experiment with engine dynotest testing in the range of 6000-10000 RPM. The research sample is a 2015 Yamaha MX King unit that has been modified to 180 cc. Data were collected through direct measurements of power (HP) and torque (Nm), then analyzed descriptively in the form of tables and graphs. The results show that the racing ECU is able to produce a higher maximum power of 24.9 HP at 10000 RPM compared to the standard ECU of 22.2 HP at the same engine speed. In terms of torque, both types of ECU reach peak values ​​at 8000 RPM with a relatively small difference (18.18 Nm on the standard ECU and 18.15 Nm on the racing ECU). However, at low to mid-range revs, the racing ECU tends to produce slightly more torque, although at very high revs, it experiences a greater decrease compared to the standard ECU. The racing ECU excels at increasing high-rev power, while the standard ECU exhibits more consistent torque stability. Therefore, the choice of ECU type must be tailored to the performance requirements and the characteristics of the vehicle's use.

Humanitarian law plays a significant role in shaping military professionalism, particularly in developing leadership character that upholds human values during military operations. Marine Corps officers are frequently involved in operational assignments that directly interact with civilian populations, both in war operations and military operations other than war. Therefore, a comprehensive understanding of International Humanitarian Law is essential for supporting professional and ethical decision-making. This study aims to analyze efforts to optimize humanitarian law understanding in order to develop a humanistic character among Marine Corps officer cadets. The research employed a qualitative descriptive method using observation, interviews, documentation studies, and literature reviews. Data were collected from Marine Corps educational institutions and legal experts within the Indonesian Naval Academy. The findings indicate that humanitarian law understanding among officer cadets remains constrained by limited instructional time, insufficient practical simulations, and the need for stronger curriculum integration. However, humanitarian law significantly contributes to developing integrity, responsibility, empathy, respect for human dignity, and professionalism in military leadership. Optimization efforts can be achieved through curriculum enhancement, practical case-based learning, continuous legal education, leadership ethics training, and strengthening organizational culture. The study concludes that strengthening humanitarian law education is essential for producing professional, humanistic, and globally competent Marine Corps officers.

Urban plant care is increasingly important for sustainable living, but many users face inconsistent watering, insufficient care knowledge, unsuitable plant selection and delayed disease recognition. This study presents Easy Grow Plants, an integrated web and Internet of Things (IoT) ecosystem that connects plant care guidance, soil-moisture monitoring, automated watering, plant recommendation, image-based plant health assistance, marketplace functions, community interaction and plant exchange. The prototype was implemented using a React frontend, Django REST backend, SQLite database and an Arduino UNO R4 WiFi smart pot with a soil moisture sensor, relay module and DC water pump. Functional, interface, API, IoT connectivity, sensor calibration, watering control and LAN deployment tests were conducted. The results show that the core modules operated together as an integrated academic prototype. The system demonstrates a practical foundation for smart urban gardening, although cloud deployment, multi-device testing and stronger AI validation remain future improvements

The rapid advancement of Internet of Things (IoT) technologies has transformed conventional residential environments into intelligent smart housing systems capable of enhancing energy efficiency, security, comfort, healthcare services, and environmental sustainability. However, existing studies remain fragmented, focusing on specific technologies or applications rather than providing a comprehensive understanding of the smart housing ecosystem. Therefore, this study aims to systematically review the major technologies employed in IoT-based smart housing systems, examine their contributions to residential benefits, and identify the challenges and future opportunities associated with their implementation. This research adopted a Systematic Literature Review (SLR) approach using the Scopus database as the primary source of literature. The review process followed a structured screening and selection procedure, resulting in the inclusion of 26 studies that met the predefined eligibility criteria. The findings indicate that Artificial Intelligence, Blockchain, Edge Computing, Fog Computing, Cloud Computing, Digital Twin, Smart Grid technologies, renewable energy systems, and advanced sensor infrastructures constitute the primary technological foundations of modern smart housing systems. The reviewed studies indicate that these technologies contribute to energy optimization, cybersecurity enhancement, intelligent automation, healthcare monitoring, safety improvement, and sustainable resource management. However, challenges related to security, privacy, interoperability, scalability, computational complexity, and implementation costs remain important barriers to widespread adoption. In conclusion, IoT-based smart housing systems have evolved into integrated and intelligent residential ecosystems, and future development is expected to be driven by the convergence of Artificial Intelligence, distributed computing, Digital Twin technologies, and sustainable smart infrastructure solutions.  

Braking performance is an important aspect of motorcycle safety because it relates to the vehicle’s ability to reduce speed and stop properly. In a hydraulic disc brake system, braking is influenced by mechanical and hydraulic factors, especially disc brake diameter and master cylinder pressure. Disc brake diameter determines the effective braking radius, while master cylinder pressure generates hydraulic pressure that is transmitted to the caliper. This study aims to analyze the effect of disc brake diameter and master cylinder pressure variations on the braking distance and braking time of a 110 cc motorcycle. This research used a quantitative experimental method with a Honda Revo Injection 110 cc motorcycle as the test object. The disc brake diameter variations were 190 mm, 220 mm, and 260 mm, while the master cylinder pressures were 2 bar, 4 bar, and 6 bar. Each treatment combination was tested three times at an initial speed of 40 km/h on a dry and flat asphalt road. Braking distance was obtained from front wheel rotation data recorded by a magnetic digital counter, while braking time was determined from video recordings. The data were analyzed using Two-Way ANOVA and response optimization. The results showed that increasing disc brake diameter and master cylinder pressure reduced braking distance and braking time. The best combination was obtained at a 260 mm disc brake diameter and 6 bar master cylinder pressure, producing a braking distance of 12.8 m and a braking time of 2.3 s.

This study investigates the integration of digital twin technology and machine learning for predictive analysis in smart mechanical systems. The research emphasizes the role of intelligent computational frameworks in improving industrial monitoring, predictive maintenance, and operational efficiency within Industry 4.0 environments. A qualitative content analysis approach was employed by reviewing scientific literature, industrial reports, and previous studies related to digital twins, artificial intelligence, and predictive analytics. The findings indicate that digital twin architectures supported by machine learning algorithms can significantly enhance real-time monitoring, fault prediction accuracy, and maintenance optimization. The integration of IoT devices, cloud computing, and intelligent analytics also improves industrial sustainability, reduces operational downtime, and supports data-driven decision-making processes. Furthermore, the study identifies several technological challenges, including cybersecurity risks, data integration complexity, and computational limitations. Overall, the proposed intelligent digital twin framework provides a promising approach for future industrial innovation and sustainable smart mechanical system management

The rapid development of information technology has created new challenges for military organizations, including the increasing accessibility of online gambling among military personnel. This study aims to analyze the optimization of mental development programs for Indonesian Navy personnel at the Naval Academy as an effort to prevent online gambling behavior. The study employed a descriptive qualitative approach using interviews, observations, documentation, and questionnaires. Data were collected from military leaders, military police personnel, mental development officers, and Naval Academy personnel. The findings indicate that mental development programs have been implemented through religious guidance, counseling, lectures, and character-building activities, which generally receive positive responses from personnel. However, several obstacles remain, including limited participation due to operational activities, rapid technological advancement, easy internet access, and the absence of specific educational materials concerning online gambling. Factors influencing personnel involvement in online gambling include low self-control, unproductive use of leisure time, environmental influences, and personal economic problems. The study concludes that mental development can effectively prevent online gambling when implemented continuously, supported by leadership involvement, digital literacy enhancement, supervision of technology use, and specific educational materials addressing the dangers of online gambling.

This study investigates the role of Baitul Maal wa Tamwil (BMT) in empowering Micro, Small, and Medium Enterprises (MSMEs) through the integration of Maqashid Sharia principles and financial inclusion strategies. Despite the strategic importance of MSMEs in economic growth and poverty reduction, many face challenges in accessing formal financial services. BMT, with its dual function of social (maal) and commercial (tamwil) activities, offers a unique platform to bridge this gap. Using a qualitative systematic literature review guided by PRISMA, this study analyzes 30 relevant articles to examine BMT operational models, implementation of Maqashid Sharia objectives, financial inclusion practices, and their impact on MSME performance. Findings indicate that BMT effectively supports MSMEs in capital access, income generation, and business resilience, but inconsistencies in balancing social and commercial objectives limit. holistic empowerment. Integrating Maqashid Sharia principles enhances ethical and sustainable outcomes, while financial inclusion expands outreach to underserved entrepreneurs. Optimization strategies, including strengthening institutional capacity, combining financial and non-financial support, and context-specific interventions, are essential for maximizing BMT effectiveness. This study contributes a comprehensive framework linking ethical, financial, and empowerment dimensions, offering practical guidance for policymakers and BMT managers in promoting inclusive and sustainable MSME development.

This study aims to analyze the effect of the Open Unemployment Rate (OUR), Human Development Index (HDI), and zakat on poverty in Mandailing Natal Regency. The study employed a quantitative approach using the Vector Error Correction Model (VECM) based on quarterly secondary data from 2015 to 2025. The analytical procedures included stationarity testing, optimal lag selection, Johansen cointegration testing, VECM estimation, Granger causality testing, as well as Impulse Response Function (IRF) and Variance Decomposition (VD) analyses. The findings indicate that all variables were stationary at the second difference level, with an optimal lag length of four, and exhibited a long-run cointegration relationship. The VECM estimation reveals that, in the long run, the Open Unemployment Rate has a positive and significant effect on poverty, while zakat has a negative and significant effect in reducing poverty. In contrast, the Human Development Index does not significantly affect poverty, which may be explained by the presence of a development time lag and employment mismatch between workforce competencies and labor market demands. The Granger causality test indicates a one-way causal relationship from the Open Unemployment Rate to the Human Development Index. Furthermore, the Variance Decomposition analysis shows that poverty variation is predominantly explained by its own shocks (96.25%), reflecting the persistence of structural poverty. Therefore, policy synergy through employment expansion and the optimization of productive zakat programs based on business empowerment is recommended to accelerate poverty alleviation in Mandailing Natal Regency.

This study aims to analyze the implementation of the Government Employees with Work Agreements (PPPK) orientation program at the Jambi Province Human Resources Development Agency (BPSDM). The research focuses on George C. Edward III's 1980 policy implementation theory, which encompasses communication, resources, disposition, and bureaucratic structure. The research method used is descriptive qualitative. Data collection techniques were conducted through observation, interviews, and documentation, with informants determined using a purposive sampling technique. The results of the study indicate that the implementation of the PPPK Orientation Program has run quite well and has been able to improve participants' understanding of the duties, functions, and core values ​​of the State Civil Apparatus (ASN). However, several obstacles remain in its implementation, namely internet and electricity network constraints, the lack of effective online learning, lack of participant discipline during the activity, and limited supervision and number of committee members. These obstacles affect the optimization of the learning process and the effectiveness of the online orientation activities. Therefore, efforts are needed to improve the quality of program implementation through the development of more interactive learning methods, the provision of adequate facilities, and further evaluation and mentoring to improve the competence and professionalism of ASN.

XYZ is a manufacturing company engaged in steel pipe production. In the production process of non-American Petroleum Institute (API) steel pipes, the company still experiences various types of waste, resulting in an inefficient production process. The identified wastes include defects, waiting, transportation, and non-value-added activities, which contribute to increased production time and reduced productivity. This study aims to analyze the major wastes occurring in the non-API steel pipe production process and propose improvements using the Lean Manufacturing approach. The methods employed in this study include Value Stream Mapping (VSM), Value Stream Analysis Tools (VALSAT), Process Activity Mapping (PAM), and Failure Mode and Effect Analysis (FMEA). Data were collected through direct observation, interviews, and documentation of the production process. The results indicate that the dominant wastes affecting the production process are defects, waiting, and transportation. PAM analysis shows that non-value-added activities remain relatively high, leading to production time inefficiencies. Based on the FMEA results, the main causes of waste are machine conditions, work methods, and operator skills. Proposed improvements include periodic machine maintenance, production quality control, work method improvement, and the optimization of material flow.