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This internship report aimed to describe the results of the analysis of employee performance at the Secretariat of the DPRD of Sikka Regency, particularly in providing administrative support for the budgeting function and achieving organizational accountability. The internship was conducted for four months, from August 11 to December 12, 2025, and took place in the Budgeting and Oversight Division. Data collection methods used in preparing this report included participatory observation of bureaucratic activities, structured interviews with the Head of the Budgeting and Oversight Division, and review of internal documents such as the 2024 Performance Report (LAKIP). The analysis focused on identifying operational obstacles and formulating effective problem-solving strategies.The results of the analysis showed that although administrative governance had generally been implemented in accordance with procedures, significant constraints were still found, including limited human resource competencies, low levels of work discipline, and inadequate facilities and infrastructure. These issues affected delays in the completion of financial accountability reports (LPJ), which in turn influenced the credibility of budget accountability. As a solution, this report formulated optimization strategies through strengthening periodic technical guidance (Bimtek), increasing staff supervision, and improving the effectiveness of internal communication patterns. The implementation of these strategies is expected to ensure the creation of work efficiency and sustainable budget accountability within the Secretariat of the DPRD of Sikka Regency.

This study investigates the optimization of claims under the Indonesian Case-Based Groups (INA-CBGs) payment mechanism within the National Health Insurance Program (Jaminan Kesehatan Nasional) at Hospital X in Serang. The research aims to analyze the influence of managerial competence, digital adoption, and organizational behavior on claim effectiveness, with policy implementation serving as a moderating variable. Employing a quantitative explanatory approach, data were collected from 144 respondents comprising management staff, casemix teams, and medical record officers through questionnaires and structured interviews. Structural Equation Modeling–Partial Least Square (SEM-PLS) was applied to examine the relationships among variables. Findings reveal that managerial competence, digital adoption, and organizational behavior significantly and positively affect the optimization of INA-CBGs claims. Furthermore, the implementation of the National Health Insurance Program policy strengthens these relationships, enhancing claim efficiency and equity. The results align with the Resource-Based View theory, highlighting the strategic role of internal resources in achieving organizational performance. Managerial implications include capacity building for managers, integration of hospital information systems (SIMRS) with e-Claim platforms, and fostering collaborative organizational culture. Overall, the study underscores the importance of managerial, technological, and behavioral factors, moderated by policy implementation, in improving hospital claim optimization under the INA-CBGs system.

This research investigates stakeholder perspectives on Digital Twin technology integration for enhancing operational efficiency in Indonesian port operations, focusing on maritime education institution viewpoints. Employing qualitative-dominant mixed methods, the study gathered insights through Focus Group Discussions, interviews, and surveys with thirty participants comprising maritime students, lecturers, and veteran practitioners at Sekolah Tinggi Ilmu Pelayaran Jakarta. Thematic analysis revealed overwhelmingly positive perceptions regarding Digital Twin operational benefits (mean score: 4.41/5.0), with particular emphasis on real-time decision support, predictive maintenance, and yard management optimization. However, stakeholders identified multifaceted implementation challenges including technological infrastructure limitations (28%), financial constraints (24%), and human capital competency gaps (23%). Findings emphasize that successful Digital Twin integration requires comprehensive frameworks addressing technological deployment, systematic competency development, and institutional readiness simultaneously. The research highlights maritime education institutions' strategic role in advancing technology adoption through curriculum innovation, specialized training programs, and industry-academia collaboration. Results provide actionable insights for developing contextually appropriate Digital Twin integration frameworks that position Indonesian ports competitively within global maritime networks while addressing emerging economy implementation realities.

This study aims to analyze the effect of the implementation of e-Samsat and progressive tax on motor vehicle taxpayer compliance at the Bandung City Samsat Office III Soekarno-Hatta. The background of this study is based on the fact that the level of taxpayer compliance has not yet reached an optimal level, despite the government’s efforts to implement technology-based tax services and fair taxation policies. This research employs a quantitative approach, with data collected through questionnaires distributed to motor vehicle taxpayers. The collected data were analyzed using statistical methods to examine both partial and simultaneous effects of the variables.The results indicate that the implementation of e-Samsat has a positive and significant effect on motor vehicle taxpayer compliance. The convenience, speed, and efficiency provided by the e-Samsat system enhance taxpayers’ awareness and discipline in fulfilling their tax obligations. Furthermore, progressive tax is also proven to have a positive effect on taxpayer compliance. The application of higher tax rates based on the number of vehicles owned encourages a sense of responsibility and fairness among taxpayers. Simultaneously, the implementation of e-Samsat and progressive tax positively influences motor vehicle taxpayer compliance and contributes to the optimization of regional tax revenue. Therefore, continuous improvement of the e-Samsat system, along with the fair and transparent implementation of progressive tax policies, is essential to sustainably increase taxpayer compliance and support local revenue growth.

This study aims to analyze the role of the tally man at PT. Gelora Perkasa Belawan in supporting the effectiveness and smoothness of loading and unloading processes at Belawan Port. As the front line in cargo supervision, the tally man bears a crucial responsibility for recording the quantity, condition, and types of goods, which serve as the primary data for company reports. The research method employed is descriptive qualitative, with data collection techniques involving field observations, interviews with operational staff, and literature reviews conducted during the internship period. The findings indicate that while the tally man performs a vital function as a supervisor and data recorder, their performance is often hindered by high workloads and a limited number of personnel. The main obstacles identified include dual responsibilities, which lead to decreased data accuracy and risks of operational delays due to physical fatigue. Optimization efforts are carried out by increasing personnel during peak hours and improving accuracy in validating cargo documents. The study concludes that strengthening the tally man's role through better workload management is essential for ensuring logistical reliability and the efficient flow of goods at the port.

This research explores maritime supply chain resilience challenges in the ASEAN region, examining stakeholder perspectives on pandemic-related disruptions and strategic recovery models. Employing qualitative-dominant mixed methods, the study gathered insights through Focus Group Discussions, interviews, and surveys with thirty-eight participants comprising maritime students, lecturers, and veteran practitioners at Sekolah Tinggi Ilmu Pelayaran Jakarta. Thematic analysis revealed severe pandemic disruptions (mean severity: 4.26/5.0) with moderate recovery effectiveness (mean: 3.31/5.0), producing significant resilience gaps particularly in container equipment availability (gap: 1.9) and labor continuity (gap: 1.2). Supply chain visibility and real-time information sharing emerged as highest resilience priorities (24%), followed by workforce flexibility (21%) and supplier diversification (18%). Findings demonstrate that pandemic experiences fundamentally challenged efficiency-oriented supply chain paradigms, with 94% of stakeholders affirming that future strategies must integrate resilience as co-equal priority alongside cost optimization. The research emphasizes maritime education institutions' critical role in resilience-building through curriculum transformation, competency development, and collaborative partnerships preparing future professionals for disruption-prone supply chain environments.

Focusing on Yuli Bakery, this study aims to examine in depth how production planning budgets and cost effectiveness influence business profitability, particularly in the food and bakery industry sector. This research is based on the assumption that effective production planning can optimize the utilization of available resources, minimize raw material waste, and establish more efficient and structured business operational performance. The study explores the implementation of labor management, raw material management, and production process control oriented toward cost efficiency without reducing product quality, thereby enabling sustainable profitability improvement. The results of the analysis show that Yuli Bakery successfully reduced its cost of goods sold (COGS) by IDR 12,800,000, which directly contributed to an increase in operating profit and gross profit. This cost reduction did not affect overall production volume nor generate additional expenses in other cost components. These findings indicate that even in the absence of significant revenue growth, cost optimization through proper production planning can make a substantial contribution to improving Yuli Bakery’s profitability.

This study aims to analyze the role of the digital economy in increasing the competitiveness of MSMEs in Medan City. The rapidly developing digital transformation provides opportunities for MSMEs to expand their markets, improve operational efficiency, and strengthen their competitive position. However, the use of digital technology among MSMEs in Medan remains limited due to low digital literacy, limited capital, lack of training, and minimal use of digital platforms such as e-commerce, social media, and Google My Business. This study uses a descriptive qualitative method through a literature review of research conducted in 2024–2025. The results of the analysis indicate that the digital economy plays a significant role in increasing the competitiveness of MSMEs, particularly through digital marketing, technological innovation, and optimization of digital platforms that can increase visibility and interaction with customers. Based on the Resource-Based View (RBV) approach, the competitive advantage of MSMEs is determined by the quality of internal resources, especially digital literacy, innovation capabilities, and technology management. With the support of training and government policies, the implementation of the digital economy has the potential to strengthen the growth and sustainability of MSMEs in Medan City.

The retail industry generates an extremely large and continuously growing volume of transactional data along with the advancement of digital technology, thereby requiring sophisticated and systematic data analysis approaches to support effective and evidence-based business decision-making. This study aims to analyze retail sales data by utilizing the Retail Sales Dataset obtained from the Kaggle platform, which consists of 100,000 transaction records and broadly represents the characteristics of retail transactions. The main focus of this study is to classify product categories and predict customer segments, including the identification of high-spending customers (high spenders), based on demographic attributes such as age and gender, as well as various transaction-related features. The research methodology includes data preprocessing, label encoding, and feature engineering to generate additional variables, including Age_Group, Is_Holiday, and Spender_Group, which are expected to enhance the predictive capability of the models. Several machine learning algorithms, namely Decision Tree, Random Forest, and XGBoost, were implemented and evaluated to compare their respective performance. The experimental results indicate that multiclass product category classification achieves relatively low accuracy, ranging from 27% to 34%. These findings suggest the high complexity of retail data and highlight the need for further model optimization, class balancing techniques, and feature refinement to improve predictive performance in future studies.

The rapid development of the digital era requires human resources to not only master software, but also understand hardware infrastructure. This community service program aims to improve the technical competence and digital literacy of the people of Pati Regency through a PC assembly and optimization workshop. The method used is the Participatory Action Learning Sistem (PALS), in collaboration with "Pati Tech" as an industry partner, and utilizing the campus computer laboratory as a training center. Activities consisted of orientation, planning, and hands-on practice in PC assembly. The results of the community service program showed a significant increase in the technical skills of participants, as reflected in the average pre-test score of 52.5 which increased to 86.4 in the post-test. Participants are now able to assemble and optimize sistem independently. The implication of this activity is the formation of independent digital talents who can manage digital infrastructure, which is expected to support the development of the local creative economy in Pati Regency. This program contributes to improving technological skills relevant to the needs of the digital market.

Gross motor development in infancy is a crucial aspect of child growth that requires early and continuous stimulation. Limited maternal knowledge and skills in providing appropriate stimulation may hinder the achievement of infants’ gross motor milestones. This program aimed to empower mothers to stimulate infants’ gross motor development through tummy time and baby yoga at PMB Lina Contesa. A community empowerment approach with a one-group pretest–posttest design was applied. The participants were mothers with infants aged 3–12 months. The intervention included health education, demonstrations, hands-on practice, and mentoring in performing tummy time and baby yoga. Evaluation was conducted by assessing maternal knowledge and skills before and after the program. The results showed an increase in the mean knowledge score from 56.4 ± 8.7 to 85.2 ± 6.9 after the intervention. In addition, the proportion of mothers who were able to correctly perform gross motor stimulation techniques increased from 27.8% to 88.9%. These findings indicate that empowering mothers through education and training in tummy time and baby yoga is effective in improving maternal knowledge and skills and has the potential to support the optimization of infants’ gross motor development.

Inflammation is a biological response that occurs as a defense mechanism of the body against tissue damage, infection, or exposure to harmful stimuli. Excessive or prolonged inflammation can lead to various chronic diseases and negatively affect overall health. Natural compounds derived from medicinal plants have gained attention as alternative anti-inflammatory agents due to their relatively lower side effects compared to synthetic drugs. Jatropha curcas is a plant traditionally used in herbal medicine and is known to contain various bioactive compounds. This study aims to examine the effect of Jatropha curcas leaf extract on inflammatory responses. The research method used was an experimental laboratory approach with extract preparation through maceration techniques. The anti-inflammatory effect was evaluated based on changes in inflammatory indicators observed during the treatment process. The results showed that Jatropha curcas leaf extract demonstrated potential anti-inflammatory activity, which was indicated by a reduction in inflammatory signs. The presence of secondary metabolites such as flavonoids, tannins, and saponins is suspected to contribute to this effect. These findings suggest that Jatropha curcas leaf extract has promising potential as a natural anti-inflammatory agent. Further research is recommended to explore dosage optimization and toxicity levels for safe therapeutic use.  

Graph theory as a branch of discrete mathematics has experienced significant development in its application to modern complex network systems, particularly in digital social networks and transportation systems. This research aims to analyze fundamental concepts of graph theory, examine characteristics of cycle detection algorithms along with their computational complexity, investigate their application in digital social network analysis, and explore their implementation in digital transportation system optimization. The research method employs a qualitative approach with library research focusing on scientific literature from 2020-2025 period from accredited academic databases such as Scopus, Web of Science, and IEEE Xplore, utilizing thematic analysis techniques to identify meaningful patterns from the examined literature. Research findings indicate that fundamental graph theory concepts including vertices, edges, and graph classifications form the foundation for relational structure modeling. Cycle detection algorithms such as Depth-First Search, Union-Find, and Tarjan demonstrate effectiveness with O(V+E) complexity for large-scale graphs. Applications in digital social networks facilitate community identification through Multi-View Clustering, centrality analysis for influencer detection, and understanding viral information dissemination patterns. Implementation in digital transportation systems demonstrates route planning optimization using Dijkstra and Bellman-Ford algorithms, vulnerability analysis through articulation point and bridge identification, and bottleneck detection with betweenness centrality. The research concludes that integration of graph theory in discrete mathematics education enhances critical thinking skills and real-world application understanding, with recommendations for algorithm development for massive dynamic graphs and machine learning integration in graph algorithm optimization.

Empowering Posyandu cadresis one of the important strategies in improving the effectiveness of community-based health promotion,especially in the digital era. However, the limited skills of cadres in utilizing digital visual media are still anobstacle in creating interesting and communicative health information. This community service activity aims to improve the knowledge and skills of Posyandu cadres in Pondok Petir Village, Depok City, through seminars and training on the use of the Canva application as a medium for developing health promotion. The activity was conducted offline through lectures, discussions, and hands-on practice with intensive guidance. There were 25 participants, consisting of cadres and members of the local PKK. The results of the activity showed that most participants were able to understand and apply Canva's features to create health promotion media such as Posyandu schedule posters, immunization materials, and nutrition education. The enthusiasm of the participants during the practice sessions and their ability to produce informative and attractive visual designs demonstrated the success of this activity. Thus, Canva training has proven effective as a means of empowering Posyandu cadres in improving the quality of health communication and supporting the optimization of Posyandu services.

Motorcycles are one of the main modes of transportation widely used by the Indonesian people due to their practicality, efficiency, and ability to support daily mobility. However, many motorcycle users complain about the suboptimal engine power, especially when the vehicle is used on extreme terrains outside urban areas, such as uphill roads, rocky paths, or long distances. This situation has driven the development of various innovations in the automotive field to improve engine performance. One widely applied method is the oversize technique on the piston and piston block, which aims to increase the combustion chamber capacity, thereby maximizing the power output. This study aims to analyze the significant comparison of engine performance after the application of the oversize method on the KVY engine type, as well as to examine the factors influencing the optimization of engine performance based on the philosophy and needs of the vehicle owner. The testing method used a dynamometer to measure the increase in torque and engine power. The test results showed that the daily oversize application resulted in a torque increase of 1.19 Nm and a power increase of 0.85 Hp compared to the standard condition. Meanwhile, the oversize for competition needs showed more significant improvements, with a torque increase of 7.97 Nm and a power increase of 8.12 Hp. These findings demonstrate that the oversize method can significantly enhance engine performance according to its intended use.

This community service activity aims to strengthen the marketing strategy of FUNN Maumere as a culinary business operating in an increasingly competitive market environment. The program was conducted using a qualitative descriptive approach through field observations, interviews with business owners and employees, and direct mentoring in implementing more effective marketing strategies. The results indicate that FUNN Maumere has applied a combination of traditional and digital marketing strategies, including brochure distribution and active promotion through social media platforms such as Instagram, TikTok, and Facebook. The mentoring activities focused on optimizing digital marketing, improving service quality, and strengthening brand consistency. These efforts contributed positively to increased brand awareness, customer interest, and wider promotional reach. However, sales performance remains fluctuative due to external factors, such as weather conditions and differences in outlet location characteristics. Therefore, this community service emphasizes the importance of continuous product innovation, enhancement of customer experience, and optimization of digital marketing strategies to reduce dependency on external conditions. This activity is expected to serve as a practical reference and mentoring model for other culinary MSMEs in developing adaptive, innovative, and sustainable marketing strategies to improve competitiveness and business continuity.

The increasing popularity of futsal in Jayapura City has created a growing demand for efficient access to information regarding available sports facilities. However, the lack of an integrated digital platform leads to inefficiencies in locating and booking futsal fields, often forcing users to rely on manual communication or generic navigation services that lack real-time data. This study aims to develop an Android-based application that optimizes the search for the nearest futsal field using the Greedy Algorithm (GA). The system integrates the Google Maps API, the Firebase Realtime Database, and Kotlin/Java to provide real-time field availability, route visualization, and online booking features. The research employed the Waterfall development model, with data collected through observation, interviews, and a literature review, involving 19 futsal fields in Jayapura City. System testing was conducted using Black-Box Testing and User Acceptance Testing (UAT). The results show that implementing the Greedy Algorithm increased route efficiency by 22%, reduced search time by 55%, and achieved 93.2% user satisfaction. All core functions passed functionality testing without errors. This study contributes both practically and theoretically by demonstrating the potential of heuristic-based optimization in sports facility management and promoting smart-city digitalization in urban sports infrastructure.

Digital transformation in public service delivery encourages government to implement service innovations that are effective, efficient, transparent, and accountable. This transformations is realized through the adoption of innovations capable of responding to public needs in a timely and accurate manner to improve service quality. The Regional Civil Service Agency of East Java Province developed the Rumah ASN Application as a digital-based personnel service innovation to support the needs of civil servants of the East Java Provincial Government and the general public. This study aims to analyze and describe the implementation of the Rumah ASN Application as an innovation in personnel services at the Regional Civil Service Agency of East Java Province. This research employs a qualitative descriptive. The analytical framework is based on the public sector process innovation theory proposed by Khodadad-Saryazdi (2022), which consists of seven key success factors: strategic alignment, governance, leadership, culture, information technology and information system, process actors, and performance evaluation. Data were collected through interviews, observations, and documentation. The findings indicate that the implementation of the Rumah ASN Application has generally been conducted well, but it has not yet reached optimal. Challenges identified for optimizing this service including the needs for continuous user socialization during system updates, optimization of service features for civil servant capacity building, strengthening administrative capacity and cross-sectoral coordination, and the developing the application into a mobile application version.

This study explores the integration of deep learning based approaches in real time video content analysis for intelligent human computer interaction (HCI) in multimedia systems. Traditional video analysis techniques, such as rule-based methods and offline processing, struggle with real time performance and adaptability to complex video data. In contrast, the deep learning model used in this research, particularly Convolutional Neural Networks (CNNs), provides high accuracy in object detection, feature extraction, and real time processing. The integration of CNNs with interactive visualization modules enables dynamic adjustments to video content based on user interactions, ensuring a seamless and engaging user experience. The system was benchmarked in terms of its processing speed, accuracy, and responsiveness, showing significant improvements over traditional approaches in real time video analysis. Moreover, the study demonstrates that combining deep learning with real time visualization enhances the efficiency of interactive multimedia applications, making it suitable for dynamic environments such as surveillance, security monitoring, and interactive media. Despite the system's strong performance, challenges such as computational demands in high-resolution video processing were identified, highlighting the need for further optimization. Future work will focus on optimizing the system for different hardware platforms, incorporating multimodal inputs, and refining deep learning models to address computational bottlenecks. This research contributes to advancing HCI by providing insights into the integration of deep learning for real time video content analysis, which is pivotal for enhancing the interactivity and adaptability of intelligent multimedia systems.

This study explores the integration of adaptive streaming models with edge computing to optimize multimedia delivery, particularly in real-time applications such as video conferencing, live streaming, and virtual reality. The proposed model leverages adaptive compression techniques, including scalable video coding (SVC) and hybrid adaptive compression (HAC), which adjust video quality based on real-time network conditions. The use of edge computing further enhances the model by processing and delivering content closer to the user, reducing latency and optimizing bandwidth usage. The research demonstrates that the edge computing-based adaptive streaming model significantly improves latency by up to 30%, reduces bandwidth consumption, and ensures higher visual quality during video playback, even under fluctuating network conditions. This model addresses key challenges in multimedia streaming, such as maintaining video quality in bandwidth-constrained environments and minimizing buffering times. Furthermore, it enhances the overall Quality of Experience (QoE) for users by providing smoother interactions and real-time responsiveness. The study highlights the potential impact of this model on various fields, including remote education, entertainment, and interactive applications, where low-latency content delivery and high-quality streaming are critical. The findings suggest that integrating AI algorithms for even more efficient compression and expanding edge computing infrastructures will further optimize multimedia streaming in the future, ensuring reliable and high-quality user experiences in increasingly connected environments.