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This study applies the Mean Variance model, which aims to form an optimal portfolio composition in the health, property, and cyclical consumer sectors and combine the three sectors into one portfolio, then visualize its efficient frontier. This study analyzes the return profiles and compares the risks of each portfolio using alternative risk measures such as the Coefficient of Variation (CV), Value at Risk (VaR), and Conditional Value at Risk (CVaR). Daily closing price data for the three sectors listed on the Indonesia Stock Exchange (IDX) from March 2, 2020, to March 3, 2025, were used in this study. Stock selection was conducted using purposive sampling, followed by selecting seven stocks for optimization based on the lowest Coefficient of Variation (CV) value. Portfolio optimization analysis was conducted using the Python programming language with Visual Studio Code software. The findings of this study indicate that the combined portfolio incorporating the three sectors is the most efficient, with an expected return of 0.104%, standard deviation of 0.007, and alternative risk measures such as Coefficient of Variation (CV) 6.9328, Value at Risk (VaR) of -0.99%, and Conditional Value at Risk (CVaR) of -1.44%, which are lower than those of single-sector portfolios. Visualization of the efficient frontier curve confirms that the combined portfolio offers better results in terms of risk and return. The results of this study indicate that cross-sector diversification can significantly reduce risk and prevent significant losses.

Lovebysisdaisy is a micro, small, and medium enterprise (MSME) producing handmade phone charm accessories and operating as a home-based business with limited digital marketing capabilities. This community service program aimed to strengthen digital marketing through Instagram optimization and the utilization of Shopee as an e-commerce platform. A participatory approach was applied, involving the business owner from problem identification to evaluation. Methods included observation, interviews, and digital documentation, followed by assistance in content development and Shopee store management. The results show that structured Instagram content improved visual consistency and audience engagement, while the establishment of a Shopee store enabled organized product catalogs and initial customer interactions. These outcomes indicate increased digital awareness and readiness of the MSME to implement sustainable online marketing strategies and expand market access.

Background: The development of modern manufacturing systems requires production scheduling strategies that not only improve productivity but also optimize energy utilization. Multi-machine production systems with job-shop configurations exhibit high complexity due to dynamic interactions between machines, job queues, and varying processing times, making conventional scheduling methods less effective in handling changing operational conditions. Objective: This study aims to develop and evaluate a reinforcement learning based production scheduling approach to improve production efficiency while reducing energy consumption in multi-machine manufacturing systems. Methods: This research employs a job-shop based multi-machine production simulation model as the experimental environment. The scheduling problem is formulated as a Markov Decision Process, enabling the implementation of reinforcement learning algorithms, namely Q-learning and Deep Q-Network, to learn optimal scheduling policies through interaction with the simulation environment. Energy consumption parameters are incorporated into the reward function so that the learning agent can consider energy efficiency in the scheduling decision-making process. System performance is evaluated using three main metrics, namely energy consumption, throughput, and makespan. Results: The experimental results show that the reinforcement learning based scheduling approach achieves better performance compared to conventional scheduling methods, resulting in lower energy consumption, higher job completion rates, and shorter production completion times within the multi-machine manufacturing system.

This study aims to analyze the optimization of the use of Village Funds in efforts to develop Pethung Park tourism in Belik Village, Trawas District, Mojokerto Regency, using  the New Public Governance (NPG) theoretical approach  from Osborne and Gabler. NPG theory emphasizes the importance of collaboration, participation, networking, decentralization, as well as transparency and accountability in public governance. This study uses a qualitative descriptive method by collecting data through interviews, observations, and documentation involving the village government, BUMDes, Pokdarwis, and the local community. The results of the study show that the Belik Village government has succeeded in making optimal use of the Village Fund as an instrument of local economic development and a means of community empowerment. The Village Fund is directed to tourism infrastructure development, human resource training, and digital-based tourism promotion. Collaboration between village governments, communities, and the private sector through BUMDes and Pokdarwis is able to create participatory and sustainable development governance. The real impact can be seen from the increase in the number of tourist visits, the growth of MSMEs, and the increase in the village's original income. This study concludes that NPG-based governance practices in Belik Village have resulted in development effectiveness and strengthened village economic independence. The results of this study can be a model for other villages in implementing the management of Village Funds for the tourism sector based on collaboration and community participation towards sustainable village development.

This research aims to deeply analyze the process mechanism for the management and issuance of the National Pollution Prevention Certificate (SNPP) by PT. Multi Jaya Samudera Belawan at the Port Authority and Harbormaster’s Office (KSOP) Belawan. The SNPP is a crucial document that certifies a vessel’s compliance with technical and administrative requirements regulated by national laws and international conventions, specifically concerning the prevention of marine environmental pollution. The research methodology employed is a qualitative descriptive case study, focusing on direct observation of the procedural stages and interviews with relevant parties within the shipping agency and the KSOP institution. Data were collected through field observation during the fieldwork period and literature study relevant to ship certification procedures. The findings indicate that the SNPP issuance process involves a series of structured stages, beginning with the application submission, verification of supporting document completeness, followed by a physical vessel inspection by KSOP inspection officers to ensure that all pollution prevention installations and systems function according to the required standards. The final stage involves issuing the certificate once all requirements are met. Despite the established procedures, this study identified several key challenges, including: (1) Administrative Delays, which can interfere with the processing schedule; (2) Lack of Understanding among business actors regarding the latest detailed procedures, often resulting in incomplete documentation; and (3) Limited Technical Support, encompassing digital information systems or other supporting facilities. The optimization of SNPP issuance heavily relies on solid coordination between the shipping agency, ship’s crew, and the regulator (KSOP). The implications of these findings highlight the importance of regular dissemination and socialization of the latest regulations and procedures, as well as enhancing the efficiency of digital administration systems to minimize waiting times and ensure vessel compliance with marine pollution prevention standards.  

Optimizing human resource (HR) performance through a business process reengineering (BPR) approach is a crucial strategy in a competitive and dynamic industrial environment. This study qualitatively examines through a literature review how BPRs radically redesign business processes to improve productivity, quality, time efficiency, and reduce operational costs, with a focus on HR aspects such as recruitment, training, performance appraisal, and career development. The BPR theory by Michael Hammer and James Champy emphasizes the principles of results-based workflow reorganization, the integration of information technology such as ERP and AI, and the empowerment of HR to eliminate task redundancy. In the Indonesian industrial context, the integration of BPR with digital transformation supports Industry 4.0, where HR acts as a catalyst for innovation through adaptive skills and cross-functional collaboration. The study results show that BPR implementation strategies including as-is process analysis, to-be design, change management, and continuous evaluation increase employee motivation, retention, and sustainable competitive advantage. Case studies such as PT Telkom Indonesia and PT Cahaya Mega Group demonstrate efficiency increases of up to 100%. This approach not only streamlines operations but also builds an organization resilient to market and technological disruption.

This study aims to examine the application of mocaf flour as a partial or complete substitute for wheat flour in the preparation of dadar gulung, a traditional Indonesian delicacy. The research is grounded in the potential of mocaf, which is gluten-free, rich in dietary fiber, and supports efforts to diversify food sources based on local commodities. A quantitative experimental method was employed with three flour formulations 50% wheat : 50% mocaf, 25% wheat : 75% mocaf, and 100% mocaf evaluated through organoleptic testing by 25 panelists with varying levels of experience. The assessment parameters included color, aroma, taste, texture, and overall perception. The findings indicate that the formulation containing 100% achieved the highest overall score, followed by the 75% mocaf and 25% wheat blend, while the 50% mocaf and 50% wheat formulation ranked lowest. These results suggest that a dominant proportion of mocaf can maintain the sensory quality of the product, although full substitution requires further optimization to enhance consumer acceptance. This research contributes to the development of healthier innovations in traditional foods, offers a gluten-free alternative for individuals with dietary restrictions, and promotes the sustainable utilization of local resources.

Digital transformation in the supply chain is the main driver of increasing industrial competitiveness in the modern era. This study aims to analyze the influence of supply chain digitalization on industrial production efficiency through information integration, process optimization, and strengthening collaboration between supply chain actors. The method used is a qualitative approach based on literature studies on various previous studies that discuss the application of digital technologies, such as the Internet of Things (IoT), big data analytics, blockchain, and Enterprise Resource Planning (ERP), in industrial supply chain management. The results of the study show that digitalization increases the visibility of supply chain activities in real-time, allowing companies to monitor the movement of raw materials, production processes, and distribution more accurately. Digital integration also strengthens the company's internal and external coordination, thereby accelerating decision-making and increasing the effectiveness of collaboration with supply chain partners. In addition, the application of digital technology encourages the optimization of production processes through automation, more precise planning of material needs, and reduction of uncertainty of demand and supply. A number of studies report a decrease in lead-time, operational cost efficiency, and improved product quality as a positive impact of digitalization. However, the literature also identifies implementation challenges, such as technology gaps, resistance to organizational change, and significant initial investment needs. Overall, this study concludes that supply chain digitalization is a crucial strategy to improve production efficiency, operational resilience, and company responsiveness to market dynamics, as well as provide a basis for the development of industrial digitalization strategies and advanced research.

The increasingly complex growth of urban mobility requires an analytical approach that can optimize transportation networks effectively and sustainably. Graph theory is one of the mathematical methods widely used in modeling road network structures and analyzing inter-node connectivity to obtain more efficient routing and network optimization solutions. This study aims to systematically review the development of graph theory application in urban transportation network optimization through the Systematic Literature Review (SLR) method. The review was conducted following the PRISMA 2020 protocol for articles published between 2020 and 2025. A total of ten articles met the inclusion criteria and were analyzed in depth. The review results show that graph algorithms such as Dijkstra, Bellman–Ford, Floyd–Warshall, Minimum Spanning Tree (MST), Graph Neural Network (GNN), and the hybrid Dijkstra–A* method can improve route efficiency, reduce travel time, improve navigation accuracy, and strengthen congestion prediction capabilities. In general, graph theory has proven to be an effective and adaptive approach in supporting urban transportation network planning and management. Further research is recommended to integrate graph theory with real-time traffic data and artificial intelligence technology to improve the accuracy and responsiveness of modern transportation systems.

The integration of big data in the financial sector has increasingly attracted scholarly attention, particularly in areas such as risk management, fraud detection, algorithmic trading, and investment optimization. Given the rapid development of this field, it is essential to map research trends and identify emerging directions that shape the future of financial innovation. This study applies a bibliometric approach using 3,829 articles retrieved from the Scopus database from 1981 to 2025, with data processed through R Studio and the Bibliometrix-Biblioshiny application. The objective is to explore the intellectual landscape of big data finance and reveal research frontiers as well as thematic evolution. The results show a sharp increase in publications after 2015, alongside the growth of fintech and artificial intelligence applications, with dominant themes including blockchain integration, risk analytics, and predictive modelling. Cross-disciplinary and cross-regional collaborations continue to expand. These findings provide a comprehensive overview of how big data has shaped financial studies and offer insights for potential future research directions.

This study aims to analyze the management process of the Cargo Ship Safety Construction Certificate by PT. Putra Andalas Samudera at the Port Authority and Harbormaster Office (KSOP) Class I Dumai. This certificate is a vital document to ensure ship seaworthiness in accordance with the standards of the Directorate General of Sea Transportation. The research method used is descriptive qualitative with data collection techniques through field observations, in-depth interviews, and documentation studies during the internship period. The results indicate that the management procedure is currently conducted digitally through the Shipping and Seafarers Information System (SIMKAPEL). Although the system aims for efficiency, several main obstacles were found, namely: data entry errors by staff, excessive digital document file sizes that hinder system verification, and internet network instability causing disruptions to the central server. Optimization efforts carried out by the company include increasing the accuracy of document validation before uploading and strengthening coordination between the agent, ship owner, and KSOP authorities. This study concludes that optimal certificate management is crucial to prevent financial losses due to ship operational delays and to ensure the safety of the crew and cargo.

The massive adoption of Internet of Things (IoT) devices is expanding the cyberattacks surface, particularly by the Mirai botnet, which exploits the dynamic characteristics of data traffic. This research proposes a Mirai detection approach based on a Recurrent Neural Network (RNN) optimized using Bayesian Optimization to improve prediction accuracy on sequential data. Unlike previous studies, this research utilizes the latest CIC IoT-DIAD 2024 dataset and applies probabilistic optimization to the hyperparameter space, including RNN units, dropout, and learning rate. The experiment was conducted on 201,021 valid data points, with dimensionality reduction using PCA as the optimal point to represent essential features without redundancy. The results show a significant increase in accuracy from 97.95% to 99.69%, accompanied by an 84% decrease in False Negatives, an 86% decrease in False Positives, and an AUC value of 0.9999. These findings confirm that integrating RNN and Bayesian Optimization not only improves numerical performance but also strengthens the reliability of the intrusion detection system for modern IoT ecosystems with controlled computational loads.

This study uses a qualitative method of a descriptive approach with an interpretive paradigm to gain an in-depth understanding of the process of planning, implementing, and evaluating broadcast programs. This research was carried out at the Communication and Information Office of Bogor Regency, Jalan Tegar Beriman Number 1, Pakansari, Cibinong District, Bogor Regency, West Java 16914. The data collection techniques used are direct observation of the broadcast process, in-depth interviews with broadcasters and program managers, and documentation to support the validity of research data. The results of this study show that broadcasting strategies are implemented through content planning that suits the needs of listeners, the implementation of interactive broadcasts involving listeners and routine evaluation to maintain the quality of the information that has been delivered. In addition, the use of digital media as a streaming platform and interaction through social media also supports a wider reach to listeners. The conclusion of the broadcasting strategy implemented by Radio Teman 95.3 FM is considered effective in maintaining listener loyalty and attracting new audiences, so that it can be an example for other local radio stations in facing the current digital media competition.technological optimization, audience engagement, and adaptation to the development of new media.

Traffic congestion is a persistent challenge in urban areas in Indonesia, where increasing vehicle density creates the need for intelligent traffic monitoring systems. This study aims to develop a real-time vehicle parking system using the YOLOv8 object detection model to provide efficient traffic analysis from live CCTV broadcasts and recorded videos. This study uses a quantitative experimental approach with the implementation of the YOLOv8m model using the Ultralytics library in Python, tested on data collected from CCTV cameras A TCS Dishub Medan and additional footage from mobile devices. Vehicles are detected and counted in two directions up (Up) and down (Down) using virtual detection lines on the video frame. The system performance is evaluated by automatic detection counting with manually recorded ground truth data. The results show that on live CCTV broadcasts, the YOLOv8m model achieves an average precision of 98.96%, a recall of 96.59%, and an F1 score of 97.74% for upstream traffic, while for downstream traffic it achieves 100% precision, 95.64% recall, and an F1 score of 97.730/0. On the other hand, on high-quality recorded videos, all performance metrics achieve 100%, indicating perfect detection accuracy. These findings confirm the effectiveness of YOLOv8 in real-time traffic monitoring, but also indicate that video quality and stream stability affect detection performance. In conclusion, the developed system shows strong potential to support smart city traffic management solutions. Future research should focus on performance optimization under low-resolution live streaming conditions to improve accuracy in practical applications.  

Employee welfare is an important element in the success and sustainability of an organization. This study aims to analyze the influence of leadership style, communication, and organizational culture on employee welfare at PT Bintang Teknik Nyata Sukodono, Sidoarjo. Leadership style emphasizes the leader's ability to create a clear and strategic vision of the future to empower employees. Communication in an organization includes the effectiveness of information delivery, openness, and two-way communication between leaders and employees. Meanwhile, organizational culture includes shared values, behavioral norms, and adaptation to change that shape interaction patterns within the organization. This study uses a quantitative approach with a survey method, involving all employees of PT Bintang Teknik Nyata as respondents. Data were collected through a questionnaire designed to measure indicators of leadership style, communication, organizational culture, and employee welfare. The analysis was carried out using multiple linear regression to identify the partial and simultaneous effects of independent variables on employee welfareThis study is expected to provide theoretical and practical insights. Theoretically, the results of this study contribute to the development of human resource management science, especially related to leadership style, communication, and organizational culture. Practically, this study can be a reference for PT Bintang Teknik Nyata in formulating strategies to improve employee welfare through leadership optimization, communication improvement, and the formation of a supportive organizational culture.

This study aims to analyze the potential and business development strategies of Warung Misop Kampung Yani Mbotz in Tanjung Morawa District as a representative of traditional culinary MSMEs. The main problems identified are limited capital, simple business management, and the lack of digital promotion. This research employs a qualitative descriptive method with a case study approach, using direct observation, interviews with the business owner, and document analysis. Data were analyzed using the SWOT (Strengths, Weaknesses, Opportunities, Threats) framework to identify internal and external factors, and the Business Model Canvas (BMC) to formulate development strategies. The results reveal that the main strengths of the business lie in authentic taste, affordable prices, and customer loyalty. Weaknesses include limited menu innovation, underutilized digital promotion, and the absence of systematic financial recording. Opportunities arise from the growing public interest in traditional culinary products and government support for MSMEs, while threats come from competition with modern food outlets, fast-food chains, and rising raw material prices. The strategies formulated through the BMC framework include product innovation, service diversification, the optimization of social media marketing, and the strengthening of business networks. The study concludes that applying BMC-based strategies can enhance the competitiveness of Warung Misop Kampung Yani Mbotz while preserving traditional culinary heritage. These findings are expected to serve as a reference for other culinary MSMEs in developing their businesses sustainably.

Baitul Maal wat Tamwil (BMT) is an Islamic microfinance institution that plays an essential role in expanding financial access for low-income communities and supporting sustainable economic development. This article aims to analyze the role of BMT as an inclusive monetary instrument within the framework of the Sustainable Development Goals (SDGs). The study employs a library research approach with descriptive-analytical methods based on secondary data from books, academic journals, research reports, and official documents. The findings show that BMT significantly contributes to financial inclusion through real-sector-based Islamic financing, especially for micro-enterprises and low-income households. Moreover, BMT’s social function—realized through the management of zakat, infaq, and sadaqah—strengthens income redistribution, poverty alleviation, and community welfare. BMT supports several SDG targets, particularly in reducing poverty, creating decent employment, developing MSMEs, and minimizing socioeconomic inequality. However, the optimization of BMT’s role still faces challenges such as limited capital, governance constraints, digital transformation, and institutional strengthening. Therefore, synergy between BMT, the government, regulators, and the community is needed to enhance BMT’s contribution as an inclusive, sustainable, and Sharia-compliant economic development agent.

Gestational Diabetes Mellitus (GDM) is a pregnancy-related metabolic disorder that poses health risks to both mother and fetus if not detected early, requiring accurate prediction methods for early screening and clinical decision-making. This study applies the Random Forest algorithm to detect GDM risk using clinical data from the Pima Indian Dataset. Data preprocessing included handling missing values, standardization, feature engineering, and a 70:30 train–test split. Two models were developed: a baseline and an optimized model using GridSearchCV hyperparameter tuning, validated with 5-fold cross-validation. Performance was assessed using a classification report, confusion matrix, and ROC–AUC. Results show that the optimized model outperforms the baseline, achieving 88% accuracy, an AUC of  93%, and average recall of 81%–85%. Compared to previous studies, this approach demonstrates improved predictive performance. The findings indicate that combining Random Forest with comprehensive preprocessing, feature engineering, and model optimization is effective and feasible for developing a medical decision support system for early GDM risk screening.

Manual quality assessment of Platelet Concentrate (TC) is highly subjective and inconsistent, necessitating an objective, automated classification system. This study aims to develop a computationally efficient, low-cost model for TC quality classification using Histogram Features extracted from grayscale images combined with the K-Nearest Neighbor (KNN) algorithm. The methodology employed critical preprocessing steps, including StandardScaler for normalization and SMOTE for balancing the training data, followed by optimization across K=1 to K=30. The optimal model achieved a maximum accuracy of 69.23% at K=6, with an F1-Score of 71.43%, confirming robust performance on the imbalanced testing set. The results validate the effectiveness of the Histogram-KNN approach as a consistent and reliable decision support system for rapid TC quality screening in resource-limited settings.

Physical fitness is a fundamental requirement for Marine Corps Cadets, both during their education at the Naval Academy and in their future military service. The role of a Platoon Commander requires excellent physical condition, adequate muscular strength, high endurance, and strong mental readiness. This study aims to describe and analyze the optimization of weight training as a physical conditioning method to improve the physical capabilities of Marine Corps Cadets. The research employed a descriptive qualitative approach using questionnaires, interviews, and documentation as data collection techniques. The subjects consisted of Marine Corps Cadets who participated in weight training programs at the Naval Academy, involving 11 questionnaire respondents and key informants from the Physical Training Department. The results indicate that well-planned, structured, and supervised weight training programs significantly enhance muscular strength, physical endurance, agility, coordination, and self-confidence. Furthermore, weight training contributes to improved physical fitness test scores and the development of a more proportional body posture. Therefore, the optimization of weight training can be considered an essential component of physical training programs to prepare Marine Corps Cadets to become professional and mission-ready Platoon Commanders.