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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.

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 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.

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.

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.

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

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

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.

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 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 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.

Document-level sentiment analysis assigns a single polarity label to an entire review, often obscuring opinion diversity within multi-sentence submissions. This limitation is particularly evident in reviews of multi-service platforms, where users frequently express heterogeneous opinions toward different aspects of the platform in the same review. To address this challenge, this study proposes a sentence-level sentiment analysis framework for Indonesian Gojek app reviews collected from the Google Play Store. The proposed framework introduces a two-stage segmentation strategy that combines punctuation-aware rules with conjunction-aware splitting based on coordinating and adversative conjunctions (e.g., tapi [but], padahal [even though]) to identify opinion boundaries and decompose mixed-sentiment reviews into independently classifiable sentence units. A total of 14,730 raw reviews collected between May and July 2025 were subjected to data cleaning and quality filtering, resulting in 7,187 valid reviews that were further segmented into 14,187 sentence-level instances. Each instance was manually annotated by three annotators using a four-class labeling scheme consisting of app-positive, app-negative, app-neutral, and service categories. Sentiment-level inter-annotator agreement, computed on the subset of instances unanimously categorized as app-related by all three annotators (n = 4,384), achieved substantial agreement (Fleiss'  = 0.636). Hyperparameter optimization was conducted using Optuna with the Tree-structured Parzen Estimator (TPE) sampler across four experimental scenarios. The best performance was achieved by IndoBERTweet under Stratified K-Fold evaluation, attaining an accuracy of 0.751 and a macro F1-score of 0.729, outperforming all IndoBERT configurations. The results demonstrate the effectiveness of domain-adaptive pre-training on informal Indonesian text and highlight the value of conjunction-aware segmentation for preserving fine-grained opinion structures in mixed-sentiment reviews. These findings suggest that domain-aligned language representations provide a practical and effective solution for sentence-level sentiment analysis of Indonesian app reviews.

Breast cancer remains a major global health challenge, with treatment effectiveness often limited by drug resistance and the toxic side effects of chemotherapy on normal cells. The exploration of bioactive compounds from natural sources through a medicinal chemistry approach offers a promising alternative strategy. This study aims to examine the molecular mechanisms of action and Structure-Activity Relationships (SAR) of various natural compound scaffolds as potential breast anticancer agents. The method employed was a systematic narrative literature review of 15 recent scientific articles evaluating computational parameters, including molecular docking, as well as in vitro and in vivo activities. The results indicate that polyphenols, flavonoids such as quercetin and EGCG, and curcumin possess strong cytotoxic activity and high binding affinity toward cancer-related target macromolecules. SAR analysis demonstrates that key structural features, including the number and position of free phenolic hydroxyl groups, the presence of gallate ester groups, and conjugated diketone systems, play a crucial role in determining ligand receptor complex stability. These interactions are supported by hydrogen bonding, hydrophobic interactions, and favorable steric compatibility within receptor binding sites. Computational findings further suggest that structural optimization can enhance ligand selectivity and improve pharmacokinetic properties. This study concludes that natural phytochemical scaffolds have significant potential as lead compounds and provide a rational basis for Computer-Aided Drug Design in developing more potent, selective, multi-target, and safer breast anticancer therapies.

Cutaneous fungal infections caused by Malassezia furfur remain a significant health concern in tropical regions, highlighting the need for safe and effective alternative therapies. Moringa oleifera L. leaves are reported to contain various secondary metabolites, including flavonoids, tannins, and saponins, which exhibit potential antifungal activity. This study aimed to develop a topical suspension formulation of Moringa oleifera leaf extract and to evaluate the effect of varying extract concentrations on the physical characteristics of the preparation. An experimental method was employed using four formulations: F0 as the control, and F1, F2, and F3 containing 9%, 10%, and 11% extract, respectively. Evaluation parameters included organoleptic properties, homogeneity, pH, viscosity, and adhesiveness, as well as stability testing using a cycling test method for 12 days. The results demonstrated that all formulations exhibited good organoleptic characteristics, homogeneity, and viscosity. The pH values remained within the acceptable range for skin preparations, although slight fluctuations were observed during storage. Adhesiveness also showed variations across several testing cycles. Overall, the formulations met acceptable physical quality criteria and demonstrated potential for further development as topical suspensions; however, formulation optimization is still required to enhance stability during storage.

Cutaneous fungal infections caused by Malassezia furfur remain a significant health concern in tropical regions, highlighting the need for safe and effective alternative therapies. Moringa oleifera L. leaves are reported to contain various secondary metabolites, including flavonoids, tannins, and saponins, which exhibit potential antifungal activity. This study aimed to develop a topical suspension formulation of Moringa oleifera leaf extract and to evaluate the effect of varying extract concentrations on the physical characteristics of the preparation. An experimental method was employed using four formulations: F0 as the control, and F1, F2, and F3 containing 9%, 10%, and 11% extract, respectively. Evaluation parameters included organoleptic properties, homogeneity, pH, viscosity, and adhesiveness, as well as stability testing using a cycling test method for 12 days. The results demonstrated that all formulations exhibited good organoleptic characteristics, homogeneity, and viscosity. The pH values remained within the acceptable range for skin preparations, although slight fluctuations were observed during storage. Adhesiveness also showed variations across several testing cycles. Overall, the formulations met acceptable physical quality criteria and demonstrated potential for further development as topical suspensions; however, formulation optimization is still required to enhance stability during storage.

Bank stock price prediction is an important topic in the application of information technology because stock price movements are dynamic, sequential, and influenced by historical market patterns. This study aims to predict Indonesian banking stock prices using the Long Short-Term Memory method and evaluate the effect of Bayesian Optimization on model performance. The data used in this study consists of daily historical stock data of BBCA, BBNI, BBRI, BBTN, and BMRI from May 4, 2020, to May 4, 2026, obtained from Yahoo Finance. The input features include opening price, highest price, lowest price, closing price, and trading volume, while the prediction target is the stock closing price. The results show that the baseline model produced MAPE values ranging from 1.892% to 3.147%. The best baseline performance was obtained on BBCA with an R² value of 0.933, followed by BBTN with an R² value of 0.902. After optimization, performance improvement occurred on BBTN, with MAPE decreasing from 3.147% to 2.482% and R² increasing from 0.902 to 0.935. For BMRI, MAPE decreased from 2.385% to 2.206%, and R² increased from 0.687 to 0.743. This study concludes that Long Short-Term Memory can be used to predict Indonesian banking stock prices, while Bayesian Optimization can selectively improve model performance depending on the characteristics of each stock dataset.

The improvement of village road infrastructure plays an important role in enhancing community mobility and supporting local socioeconomic development. This study aims to analyze changes in mobility conditions before and after road improvement, explore community perceptions and experiences, identify changes in activity and economic patterns, and examine factors that support or hinder mobility optimization in Trayeman Village, Slawi District, Tegal Regency. A mixed-method approach was employed involving 98 respondents selected from a population of 5,229 residents using the Slovin formula with a 10% margin of error, along with 15 key informants for qualitative data collection. Data were gathered through observations, in-depth interviews, documentation studies, and Likert-scale questionnaires. Quantitative data were analyzed descriptively, while qualitative data were examined using the Miles and Huberman interactive analysis model. The findings indicate that road improvement has significantly enhanced community mobility, with an overall mean score of 3.89, categorized as high. Access to services recorded the highest score (4.06), followed by economic mobility (4.00) and social mobility (3.99). Community perceptions were largely positive, viewing the improved road as a catalyst for development and improved accessibility. Road improvement also encouraged broader economic opportunities, lower transportation costs, business growth, and increased income. Supporting factors included private vehicle ownership and supportive village policies, whereas high fuel prices, limited transportation options, and uneven local road conditions remained major challenges. Further improvements in supporting infrastructure and transportation services are recommended to maximize mobility benefits.

Indonesia’s Free Nutritious Meal Program (MBG) requires an efficient and adaptive supply chain system to ensure timely distribution, cost efficiency, and adequate nutritional delivery for a large number of beneficiaries. However, conventional supply chain approaches are generally static and unable to respond effectively to dynamic demand, supply uncertainty, and logistical constraints. This study proposes a Multi-Objective Reinforcement Learning (MORL) model to optimize the MBG supply chain by simultaneously considering distribution cost, delivery timeliness, service level, nutritional adequacy, and food waste reduction. The model is developed using a simulation-based environment representing real-world supply chain conditions, including demand variability, transportation limitations, and kitchen capacity constraints. The results show that the proposed approach achieves cost reductions of 15–22%, improves delivery timeliness by 18–25%, maintains a service level above 90%, increases nutritional fulfillment by 12–18%, and reduces food waste by 10–15% compared to baseline methods. Sensitivity analysis further demonstrates the robustness of the model, with minimal performance degradation under disruption scenarios. These findings indicate that Reinforcement Learning provides a scalable and adaptive solution for optimizing large-scale public food distribution systems. The proposed model contributes both theoretically by integrating multi-objective optimization within an RL framework and practically by supporting data-driven decision-making for improving the effectiveness of the MBG program in Indonesia.