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Population mobility is a dynamic phenomenon that plays an important role in regional development processes. Population movement is influenced by various interconnected factors, particularly social, economic, and environmental aspects. This study aims to analyze the factors influencing population mobility using a Systematic Literature Review (SLR) approach. Data were collected from relevant national and international scientific literature published within the last ten years. The results indicate that economic factors are the primary determinants driving population mobility, particularly in relation to employment opportunities and income improvement. Meanwhile, social factors such as education and social networks act as supporting elements in the mobility process. Environmental factors also show significant influence, especially in the context of climate change, natural disasters, and environmental degradation. The interaction among these factors demonstrates that population mobility is a multidimensional phenomenon that requires a comprehensive analytical approach. This study is expected to contribute to the development of population studies and serve as a reference for sustainable development policy formulation.

The increasing sophistication of malware has rendered traditional signature-based detection methods insufficient, necessitating behavior-driven and adaptive analytical frameworks. This study presents a sequential deep learning framework that models system-level API call sequences as structured linguistic representations for behavioral malware detection. Unlike conventional comparative studies, this work systematically evaluates recurrent and attention-based architectures under controlled experimental conditions, with a particular focus on generalization performance and overfitting mitigation. Two neural architectures, a Long Short-Term Memory (LSTM) network and a Transformer-based attention model, are trained on publicly available API call sequence data for binary classification of malicious and benign executables. Beyond standard accuracy metrics, the study further examines model stability, convergence behavior, and the impact of long-range dependency modeling on detection robustness. Experimental results demonstrate that the Transformer architecture achieves superior performance, attaining 95.54% classification accuracy and consistent improvements in precision, recall, and F1-score, indicating a stronger ability to capture complex behavioral dependencies. These findings highlight the effectiveness of attention mechanisms in behavioral malware modeling and provide empirical evidence that NLP-inspired architectures offer a robust and scalable approach for real-world cybersecurity applications.

Inconsistent terminology across cybersecurity frameworks undermines global governance and interoperability. The National Institute of Standards and Technology Cybersecurity Framework (NIST CSF 2.0) and ISO/IEC 27001:2022 share similar objectives but diverge semantically in defining risk, control, and resilience. This semantic gap causes difficulties in compliance mapping and automated policy translation. Research Objectives: This study aims to analyze the semantic similarity and divergence between NIST and ISO/IEC 27000 terminologies, identify conceptual structures influencing interoperability, and propose an AI-assisted foundation for harmonizing cybersecurity language globally. Methodology: A mixed-method semantic comparative design integrates Natural Language Processing (NLP) and ontology mapping. Using the nist_glossary.csv dataset and ISO vocabularies, terms were normalized and analyzed via cosine similarity using sentence-transformer embeddings. Ontological alignment was visualized through the Semantic Threat Graph (STG) and validated by certified experts using Cohen’s Kappa reliability tests. Results: From 672 term pairs, results show 40.9% high semantic equivalence, 38.8% partial overlap, and 20.3% semantic divergence. Strongest alignment appears in “Protect” and “Identify” domains, while divergences occur in governance and recovery-related terms. Ontology mapping revealed three conceptual clusters—Risk Governance, Technical Safeguards, and Organizational Readiness. Conclusions: Findings confirm a 79.7% total semantic alignment, indicating strong potential for harmonizing global cybersecurity standards. The study contributes an empirical model combining computational linguistics and AI-based ontology mapping to establish semantic interoperability, enabling unified cybersecurity governance and AI-driven compliance automation. Keywords: Semantic Interoperability; Ontology Mapping; Cybersecurity Frameworks; Terminology Alignment; AI Harmonization

Educational institutions increasingly face competitive pressures in attracting and maintaining student enrollment, particularly within the private school sector. This study examines educational marketing strategies implemented to increase student enrollment in Catholic private schools. The research focuses on two institutions, namely SMA Strada Santo Thomas Aquino and SMA Katolik Lia Stephanie, which demonstrate different enrollment trends and marketing approaches. A qualitative exploratory method was employed to understand institutional strategies and marketing practices in their natural contexts. Data were collected through observation, semi-structured interviews, and documentation conducted between August and December 2025. Data validity was ensured through source and technique triangulation, while analysis followed the Miles, Huberman, and Saldana model consisting of data condensation, data display, and conclusion drawing. The findings reveal that both schools implement educational marketing strategies based on the marketing mix framework (7P), including product, price, place, promotion, people, process, and physical evidence. SMA Strada Santo Thomas Aquino applies a community-relational approach that emphasizes Catholic values, alumni loyalty, school reputation, and word-of-mouth communication within community networks. SMA Katolik Lia Stephanie adopts a differentiation-professional approach by offering flagship academic programs, modern facilities, and structured promotional strategies coordinated by a dedicated marketing team. The effectiveness of these strategies is supported by church networks, alumni engagement, foundation support, and consistent educational service quality. Several challenges were identified, including limited marketing resources, insufficient digital marketing competence, and increasing competition among private schools. Strengthening adaptive and innovative marketing strategies is essential for maintaining institutional competitiveness and sustaining student enrollment in Catholic private educational institutions.

The increasing sedentary lifestyle in the digital era has the potential to cause various health problems due to lack of physical activity. One approach that can be taken to encourage physical activity is through the use of digital games with body movement-based control mechanisms. This study aims to develop a body gesture-based game character control system using a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. CNN is used to extract spatial features from each video frame, while LSTM serves to model the temporal relationship between frames so that movement patterns can be recognized sequentially. The research method used refers to the Machine Learning Lifecycle stages, starting from data collection, preprocessing, model development, to implementation in the endless runner game genre. Testing results show that the CNN–LSTM model is capable of classifying body gestures and generating outputs that can be used as commands to control game characters. The implementation of this system enables more natural and interactive game interactions without conventional input devices, and has the potential to encourage players to lead a more active lifestyle.

Health issues related to free radicals remain a serious concern in Indonesia as they can trigger oxidative stress and degenerative diseases. Red ginger (Alpinia purpurata) is a rhizome plant with potential as a source of natural antioxidants due to its secondary metabolite content; however, its effectiveness is highly influenced by extraction techniques. Although numerous experimental studies have been conducted, a systematic research mapping on this topic is still lacking. This study aims to perform a bibliometric analysis of scientific publications regarding the antioxidant potential of red ginger, focusing on extraction techniques and free radical scavenging activity. The research method employs a quantitative analysis using data sourced from the Scopus database for the 2015–2025 period. Through specific inclusion and exclusion criteria, 38 relevant articles were obtained and analyzed using VOSviewer 1.6.20 software. The results indicate that publication trends have fluctuated, reaching a peak in 2024. Research distribution is dominated by Asian countries, with India, Thailand, and Indonesia as the primary contributors. Network visualization reveals three main clusters focusing on bioactivity, phytochemistry, chemical analysis, antimicrobial activity, and extraction techniques. A research gap was identified for the Alpinia purpurata species compared to Alpinia galanga, as well as opportunities for developing advanced instruments such as LC-MS and other complex analytical techniques. The implications of this study highlight the need for further exploration into 'nanoemulsion' and 'green extraction' to enhance the bioavailability of red ginger's antioxidant compounds in the development of future innovative pharmaceutical products

Human–Robot Interaction (HRI) systems increasingly rely on data-driven approaches to interpret multimodal sensory inputs and support natural interaction. However, purely neural-based HRI models often suffer from limited interpretability and insufficient context-aware decision-making, which can reduce user trust and adaptability in dynamic interaction scenarios. To address these limitations, this study proposes a hybrid neural–symbolic HRI framework that integrates multimodal neural perception with explicit symbolic reasoning for adaptive and interpretable robot behavior. The proposed system combines deep neural networks for processing visual, speech, and gesture inputs with a rule-based symbolic reasoning layer that models interaction context, user states, and behavioral constraints. A loosely coupled integration strategy enables neural outputs to be transformed into symbolic representations, allowing logical inference to guide action selection while preserving perceptual accuracy. The framework was evaluated through controlled HRI experiments comparing a neural-only baseline with the proposed hybrid configuration across multiple interaction scenarios. Experimental results demonstrate that the hybrid neural–symbolic system significantly improves interaction accuracy, contextual responsiveness, and user satisfaction, while achieving substantial gains in interpretability. These findings indicate that symbolic reasoning effectively complements neural perception by enhancing transparency and context-aware adaptation without compromising performance. The study concludes that hybrid neural–symbolic architectures provide a promising foundation for developing trustworthy, adaptive, and human-centered HRI systems.

Village entrepreneurship is an initiative designed to stimulate regional economic growth by accelerating the development of rural areas, which have long been perceived as slow, less productive, and lagging behind urban centers. The creation of entrepreneurial villages offers a concrete strategy to enhance community productivity because it encourages all components of the village government, residents, and local institutions to participate in structured and collaborative entrepreneurial activities. Such villages are expected to generate new economic opportunities, strengthen local industries, and increase community independence. However, initial observations from this Community Service program show that partner villages still face significant barriers, including limited land area, geographical remoteness, and inadequate human and natural resources. Many peripheral villages have not yet utilized digital technology effectively, making it difficult to expand markets or access broader economic networks. In addition, challenges related to financial access, limited talent development, socio-cultural constraints, and weak market infrastructure continue to hinder economic progress. These conditions demonstrate the urgent need for strategic assistance, capacity-building programs, and stronger support systems to help villages transform into sustainable entrepreneurial ecosystems capable of contributing to long-term regional development.

This study analyzes the implementation of Asset-Based Community Development (ABCD) principles in the 'Nusa Loe' program in Biting Village, East Manggarai. The ABCD approach emphasizes utilizing local assets such as social networks, cultural traditions, natural resources, and human skills as the foundation for sustainable development and community independence. The program begins with an asset mapping activity led by the community, enabling the identification of the village's strengths from five key aspects: human, social, natural, institutional, and cultural assets. Through active participation, the community designs and implements development activities based on local potentials, replacing the conventional development paradigm that focuses on deficiencies and needs. The qualitative research approach employs in-depth interviews, participatory observations, and data triangulation to understand the shifts in mindset and community empowerment. The findings show that the implementation of ABCD successfully transformed the community's mindset, previously dependent on external aid, into a more independent and self-confident one. For example, Mr. Petrus, who improved the economic value of local coffee products after attending a coffee roasting training, and Mrs. Maria, who emphasized the importance of social capital and solidarity in supporting the sustainability of village businesses. The strengthening of cultural assets and local traditions, such as the Caci dance and customary rituals, was also integrated into the village's development plans and institutional management. The 'Nusa Loe' program demonstrates that asset-based empowerment can build a self-reliant, innovative, and sustainable village in the face of development challenges.

This study aims to analyze land cover changes in Pariaman City in 2013, 2018, and 2024 using Landsat-8 imagery with the support of the Google Earth Engine (GEE) platform and the Classification and Regression Trees (CART) algorithm. Land cover change is a critical issue in Pariaman City, as population growth, infrastructure development, and economic activities have driven significant land conversion. The classification generated eight land cover classes representing the general condition of the area. The analysis revealed that rice fields were the dominant land cover in all three periods; however, they experienced a substantial decline, indicating land conversion, particularly into settlements and road networks. In addition, the area of mangrove forests and water bodies in coastal regions decreased, while plantation areas in the eastern and southern parts of the city increased. Conversely, river land cover remained the smallest category, with annual fluctuations. The identified change patterns included urban sprawl (expansion of settlements into suburban areas), the conversion of productive land into infrastructure, and coastal degradation due to human activities and natural factors. The accuracy assessment produced overall accuracy and a Kappa index above 80%, while validation using the Mapping Accuracy method through Google Earth showed per-class accuracy rates above 75%, categorized as very good. These findings indicate that the use of satellite imagery and the CART algorithm in GEE is effective for monitoring land cover dynamics while providing valuable insights for local governments in formulating sustainable development policies and managing coastal environments such as those in Pariaman City.

This study investigates the genealogy of multisectoral social conflict and cultural disintegration in Bima Regency by examining the interplay of political, economic, socio-cultural, and geospatial determinants. Using a qualitative exploratory-critical method, the research employs content analysis, critical discourse analysis, and spatial conflict mapping based on secondary data from government reports, academic literature, and civil society documentation. The results reveal that social conflict in Bima is not simply driven by unequal access to political and economic resources, but also by structural exclusion, historical marginalization, and the erosion of traditional social values caused by uneven modernization and weak institutional performance. Politically, civil servant bias, the persistence of patronage networks, and minimal civic engagement weaken state legitimacy, public trust, and accountability. Economically, the unequal distribution of natural resources, selective access to government assistance, limited economic diversification, and unresolved agrarian disputes intensify socio-economic inequalities and rural discontent. From a geospatial perspective, the contestation over land, identity, ecological zones, and environmental resources reflects the failure of inclusive and participatory spatial development planning. The study emphasizes that the conflict is systemic, complex, and institutionalized, calling for integrated cross-sectoral policies, inclusive political reforms, and spatially participatory strategies. It advocates for institutional reform, the revitalization of local wisdom and social capital, and the reconstruction of a shared cultural identity to restore long-term social cohesion and resilience. In the context of post-decentralization Indonesia, addressing such deep-rooted, chronic conflicts requires not only strategic policy reorientation but also the empowerment of local communities to play meaningful and active roles in planning, governance, and sustainable development processes. Sustainable peace in Bima hinges on a genuine commitment to social justice, inclusive governance, ecological balance, and the preservation of cultural diversity.

This community service activity aims to increase the capacity of residents of Tumpangrejo Hamlet, Ngenep Village, Karangploso District, Malang Regency through training in the manufacture and use of solar-powered lamps. The main problem faced by residents is limited access to electricity in some areas, especially in remote areas not yet covered by the PLN electricity network. Furthermore, there is a lack of knowledge about environmentally friendly renewable energy technology and its potential use in daily life. The activity method begins with socialization about solar energy, where residents are given an understanding of the basic principles and benefits of solar energy as an environmentally friendly alternative energy source. Then, practical training is conducted on assembling solar-powered lamp components, which includes installing solar panels, batteries, and LEDs. During the training, participants are guided to understand each stage of the assembly process and the importance of maintaining the equipment to ensure its long-lasting and optimal function. Technical assistance is also provided after the training to ensure residents can apply the skills learned in their daily lives. Participants are encouraged to assemble and maintain solar-powered lamps independently, so they do not solely rely on external parties for maintenance. The results of this activity demonstrated an increase in residents' knowledge and skills in understanding the concept of solar energy, as well as their ability to independently assemble and maintain solar-powered lamps. Furthermore, residents began utilizing solar-powered lamps for street and household lighting. This reduced reliance on conventional electricity sources, which are often limited and expensive. This activity is expected to become a model for community empowerment based on appropriate technology in rural areas, improving their quality of life through the sustainable use of available natural resources.

The design of Lecturer Housing for Campus 4 Gorontalo State University is a strategic necessity in line with the expansion of educational institutions and the increasing number of educators requiring adequate and proximate housing. The primary objective of this research is to produce a lecturer housing design that is not only adaptive and sustainable, but also harmonized with the Regional Spatial Plan (RTRW) of Bone Bolango Regency. The research methodology encompasses a comprehensive site analysis, examining aspects such as climate conditions, topography, vegetation patterns, infrastructure and utility networks, ambient noise levels, and site accessibility. Furthermore, a zoning and spatial relationship study was conducted to determine effective space allocation based on the principles of tropical architecture and green building practices. The findings of the study highlight that a north-south building orientation significantly reduces heat absorption, while the integration of cross-ventilation strategies enhances natural airflow and thermal comfort. Spatial zoning into public, semi-public, private, and service areas allows for organized layouts and promotes social interaction among residents. Sustainability is reinforced through the application of green architectural technologies, including the use of green concrete, lightweight steel frameworks, solar energy panels, as well as integrated waste-water management systems that separate black water and grey water. Landscaping elements, both softscape and hardscape, are utilized to enhance aesthetics while supporting ecological balance and rainwater absorption. This design proposal contributes to the broader discourse of sustainable tropical housing and may serve as a replicable model for future lecturer housing projects in similar regional contexts. It emphasizes the importance of environmentally responsive, cost-efficient, and socially supportive residential architecture.

This community service aims to foster and develop an entrepreneurial spirit among teenagers in Sanrobone Village, Takalar, through the optimization of Micro, Small, and Medium Enterprises (MSMEs) based on local potential. Teenagers are identified as strategic assets that need to be empowered for economic independence and job creation. The method of implementing this activity adopts a participatory and educational-practical approach, including the preparation stage, implementation of training and mentoring, and monitoring and evaluation. A total of 35 teenagers from Sanrobone Village were actively involved in a series of basic entrepreneurship training, identification of local business opportunities, technical production skills workshops, and simple digital marketing. The results of the activity showed a significant increase in teenagers' understanding of the concept of entrepreneurship from just selling to creating value. They succeeded in identifying 15 potential business ideas from local resources and 20 teenagers were able to produce product prototypes from these ideas. In addition, the initiative to form five micro-business groups initiated by teenagers is clear evidence of the growth of motivation and collaboration. In conclusion, this program is effective in laying a strong foundation for the development of a youth entrepreneurship ecosystem in Sanrobone Village, which is supported by the potential of natural resources and the enthusiasm of the participants. It is recommended that there be ongoing mentoring, facilitation of access to micro-capital, strengthening of networks, and integration of entrepreneurship curriculum in schools to ensure the sustainability and long-term impact of the program.

This study aims to examine the village government's strategy in developing the tourism potential of Lake Rana Kulan, located in Rana Kulan Village, Elar Sub-district, East Manggarai Regency. The research uses a descriptive method with a qualitative approach. Data collection techniques include interviews, observation, and documentation, using both primary and secondary data sources. Data analysis was carried out through data reduction, data presentation, and conclusion drawing. The study adopts the tourism development components theory by Gunn (2002), which includes attraction, service/facilities, promotion, transportation, and information. The results show that the village government is working to develop tourism attractions such as the lake's natural beauty, fishing and swimming activities, as well as cultural attractions like festivals. In terms of facilities, gazebos, toilets, parking areas, a stage, and trash bins are available, although food stalls and accommodations remain limited. Basic infrastructure such as solar power (PLTS) and internet networks are in place, but clean water supply remains a challenge. Promotion is conducted through social media and cultural festivals, with plans to develop a village website. Road access to the location is still damaged but under repair, supported by local motorcycle taxi services. In terms of information, there are directional signs and digital access via Google Maps and the Jadesta website. Overall, the strategies implemented indicate the village government's commitment to tourism development, although infrastructure and facility limitations remain obstacles.

This study aims to examine the village government's strategy in developing the tourism potential of Lake Rana Kulan, located in Rana Kulan Village, Elar Sub-district, East Manggarai Regency. The research uses a descriptive method with a qualitative approach. Data collection techniques include interviews, observation, and documentation, using both primary and secondary data sources. Data analysis was carried out through data reduction, data presentation, and conclusion drawing. The study adopts the tourism development components theory by Gunn (2002), which includes attraction, service/facilities, promotion, transportation, and information. The results show that the village government is working to develop tourism attractions such as the lake's natural beauty, fishing and swimming activities, as well as cultural attractions like festivals. In terms of facilities, gazebos, toilets, parking areas, a stage, and trash bins are available, although food stalls and accommodations remain limited. Basic infrastructure such as solar power (PLTS) and internet networks are in place, but clean water supply remains a challenge. Promotion is conducted through social media and cultural festivals, with plans to develop a village website. Road access to the location is still damaged but under repair, supported by local motorcycle taxi services. In terms of information, there are directional signs and digital access via Google Maps and the Jadesta website. Overall, the strategies implemented indicate the village government's commitment to tourism development, although infrastructure and facility limitations remain obstacles.

Natural gas is an essential industrial fluid, and its distribution requires a pipeline system that is safe and leak-free. Leakage in natural gas pipelines remains a major concern, as it reduces public confidence in using gas for household purposes. Therefore, testing is conducted before the pipeline network is operated. One commonly used method to detect leaks is the Pneumatic Test, which evaluates the strength and integrity of pipes and their joints according to standards for polyethylene natural gas pipelines. This test uses compressed air supplied by a compressor, with pressure and temperature measured at the inlet pipe using a pressure gauge. The study found no pressure changes or leakage throughout the test. The calculated holding time required for the pneumatic procedure was 1 hour, 26 minutes, and 8 seconds. Non-Destructive Testing (NDT) results showed that: (a) at 50% of test pressure (approximately 10 bar), the pipe held for 15 minutes with no pressure drop; (b) at 75% (around 15 bar), pressure remained stable for 15 minutes; and (c) at 100% (20 bar), pressure was maintained for 75 minutes without loss. Welding was performed using Nikko Steel electrodes (2 mm × 300 mm, 50–80 A), supported by proper safety gear and tools, and a Riland welding machine with a 400-A capacity.

Waropen Regency, located in Papua Province, Indonesia, is an area with abundant natural resource potential, such as fisheries (fish, shrimp, crabs), tropical forests and biodiversity. However, the varied topography, high rainfall and geographical conditions make this region vulnerable to flood disasters. This research aims to analyze and predict flood hazards in Waropen Regency using Geographic Information Systems (GIS) such as ArcGIS. The methods used include secondary data collection (topography, rainfall, land use, and river network), data processing using GIS software, as well as spatial analysis using Digital Elevation Models (DEM) and hydrological modeling. The research results produced a flood hazard zoning map that identifies priority areas based on threat level. This map is also used to analyze the potential impact of flooding on settlements, infrastructure and agricultural land. The findings of this research provide important information for local governments and stakeholders in designing disaster risk mitigation strategies, such as building flood control infrastructure and disaster mitigation-based spatial planning. Thus, this research shows the potential of GIS in supporting effective and data-based disaster risk management in Waropen Regency.

This article examines the role of coastal women in sustainable development, focusing on the challenges and opportunities they encounter. Women in coastal areas significantly contribute to fisheries, marine product processing, and environmental conservation. However, they often face various obstacles, including exclusion from decision-making processes and unequal resource access. A gender-based approach is used to explore how empowerment can be achieved through capacity building, enhanced access to technology and capital, and livelihood diversification. Additionally, strengthening social networks and local institutions proves essential for supporting active female participation. Inclusive and gender-sensitive public policies are key to creating an environment that fosters women's empowerment. Through these measures, the empowerment of coastal women is expected to enhance their contributions to sustainable development, strengthen community resilience, and support more sustainable natural resource management.

Natural disasters such as earthquakes, hurricanes, and floods pose significant risks to critical infrastructure. AI-driven disaster response systems provide real-time analytics, predictive modeling, and automated response strategies to mitigate damage and improve recovery efforts. This paper explores how AI-powered drones, satellite imagery, and sensor networks enhance disaster monitoring and decision-making. Additionally, the study discusses the role of AI in optimizing emergency resource allocation and predicting infrastructure vulnerabilities. Through an analysis of past disaster management strategies, this research aims to propose AI-integrated frameworks that enhance disaster preparedness and resilience.