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This study aims to analyze the role of Gujarat, India, as a major hub in the dynamics of the Islamization of the Indonesian Archipelago (Nusantara) and its impact on the formation of a distinctive Southeast Asian Muslim identity. This research employs a library research method by examining various primary and secondary sources, including historical manuscripts, travel accounts, archaeological findings, and relevant scholarly books and journal articles. The findings indicate that the arrival and spread of Islam in the Nusantara around the thirteenth century were strongly influenced by Muslim traders from Gujarat, who functioned not only as economic agents but also as carriers of religious and cultural values. The process of Islamization occurred relatively peacefully through trade networks, intermarriage, Islamic education, and political alliances with local rulers. The dominance of Sufistic teachings, which were adaptive and accommodative to local cultural traditions, facilitated a process of cultural inculturation that enabled Islam to be accepted without significant social conflict. This interaction produced a moderate and syncretic form of Islam, reflected in religious practices, local traditions, and Islamic architectural forms across the region. The study concludes that the Islamization of the Nusantara was a continuous process of cultural dialogue that shaped a tolerant, contextual, and harmonious Muslim identity in Southeast Asia, distinguishing it from Middle Eastern Islamic traditions.

This study aims to examine the historical legacy of the Sriwijaya and Majapahit Kingdoms in the context of the development of Southeast Asian civilization through a literature review approach. The study focuses on the contributions of both kingdoms in various aspects, including politics, economy, maritime activities, culture, education, and international diplomacy. Research data were obtained from primary and secondary sources, such as historical manuscripts, inscriptions, archaeological reports, and national and international scholarly publications. The findings indicate that the Sriwijaya Kingdom played a significant role as a major maritime power and an international center of Buddhist education that influenced intellectual networks across Asia. Meanwhile, the Majapahit Kingdom was prominent in developing systems of governance, law, and regional integration of the Nusantara through extensive political and trade networks. Both kingdoms not only shaped the social and cultural structures of their respective periods but also laid an important historical foundation for the formation of identity and regional dynamics in modern Southeast Asia. Therefore, the legacies of Sriwijaya and Majapahit remain highly relevant for understanding historical processes, regional integration, and the long-term development of Southeast Asian civilization.

Rainfall is a crucial factor in the stability of the Earth's ecosystem and has a significant impact on agriculture, forestry, energy, and water management. However, increasingly unstable climate change makes rainfall patterns difficult to predict accurately using traditional methods. The city of Medan, the capital of North Sumatra Province, has a tropical rainforest climate with an average annual rainfall of approximately ±2200 mm and an average temperature of 27°C. Significant weather fluctuations in this area can trigger flooding when rainfall increases and cause water shortages when rainfall decreases (BMKG, 2021). Therefore, a prediction approach that can manage non-linear and dynamic data is needed. Artificial Neural Networks (ANN) are one of the reliable machine learning methods for detecting data patterns. By using the backpropagation algorithm, the model can gradually reduce prediction errors, making it widely used in weather forecasting applications. In this regard, this study uses ANN with the backpropagation method to forecast monthly rainfall in Medan City by utilizing data from 2022–2024 as training and testing data.

Male infertility represents a major global health concern, with oxidative stress playing a central role in the deterioration of sperm quality. In recent years, growing attention has been directed toward herbal medicine as a potential alternative or complementary therapy due to its antioxidant and multi-target properties. However, a comprehensive long-term overview of the global research landscape in this field remains limited. A bibliometric and visualized analysis was conducted using publications indexed in the Scopus database from 2005 to 2025. Eligible peer-reviewed articles were retrieved following PRISMA guidelines. Bibliometric indicators were analyzed using Biblioshiny (R-Bibliometrix) to assess publication trends, leading contributors, and thematic evolution, while VOSviewer was employed to visualize international collaboration networks, co-citation patterns, and keyword co-occurrence clusters. A total of 562 articles were included, revealing a strong upward trend in scientific output, particularly after 2016, with an annual growth rate of 15.78% and a peak in 2025. Iran, China, and Nigeria emerged as the most productive countries, while the Faculty of Veterinary Medicine and several universities in Africa, the Middle East, and Asia were identified as leading institutions. Keyword analysis highlighted three dominant research fronts: semen quality assessment, testicular histopathology, and oxidative stress–related molecular mechanisms. The collaboration network indicated increasing global and South–South research synergy. The field of herbal medicine for male infertility has evolved into a mature and multidisciplinary research domain, increasingly grounded in mechanistic and experimental evidence. These findings provide a strategic overview of research hotspots and emerging trends, offering valuable guidance for future experimental and clinical investigations.

Criminal responsibility is constructed through the elements of homicide offences, the assessment of mens rea, accomplice liability, and sentencing rationality grounded in individual culpability. In judicial practice, however, cases involving the killing of women are frequently framed at the outset through the labels of “honour” or “intimate relationship,” leading honour killing and intimate partner femicide to be treated as interchangeable categories. This practice shifts the assessment of intent toward the perpetrator’s narrated motive and narrows accountability to the last physical actor. This research aims to formulate, first, legal criteria and evidentiary indicators for distinguishing the two categories through a staged judicial classification test, and second, to assess the implications of such classification for the construction of intent, the attribution of responsibility to non-executing actors, and sentencing rationality through disciplined reason-giving. The study employs a normative legal method with a conceptual approach, based on library research of primary and secondary legal materials. The findings demonstrate that the core deficiency lies in the absence of an operational classification device, allowing honour narratives to displace structured mens rea analysis and to obscure the causal contributions of non-executors. The article proposes working definitions and a stepwise indicator-based test—focusing on the presence of determinative social pressure or sanctioning, provable role allocation within perpetrator networks, and prior threats framed in terms of honour restoration—and links these indicators to concrete doctrinal consequences for intent, accomplice liability, and sentencing. Through this framework, judicial reasoning is redirected from label-driven interpretation toward accountability, while restraining the use of honour as a mitigating rationale and preventing femicide patterns from being concealed by reputational narratives.

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.

In demographic studies, population growth refers to changes in the number of inhabitants over a given period, measured by calculating numerical differences in population size and expressing them within specific time units to illustrate patterns of increase or decline. Based on data from Tembalang District in Figures 2025, the population growth rate in Kedungmundu Village rose by 0.51% between 2016 and 2025, with most residents relying on clean water services provided by PDAM Tirta Moedal as their primary source of drinking water. This study aims to estimate clean water demand based on projected population growth and to design a pipeline network system capable of meeting future needs. The analysis of water demand applies population projection methods, including arithmetic, geometric, and least square approaches, to compare their levels of accuracy, while the clean water distribution network is modeled using EPANET 2.0. One of the main challenges faced by PDAM is that service coverage has not yet been fully optimized. The ten-year projection results indicate that the arithmetic method provides a correlation value closest to 1, estimating a future population of 14,904 people with a total clean water requirement of 3.48 liters per second. To support this projected demand, the proposed network design utilizes High Density Polyethylene (HDPE) pipes with diameters of 12, 10, and 8 inches to ensure efficient and sustainable water distribution.

This study is motivated by the increasing need for accurate modeling and classification of one-dimensional signal data in intelligent systems. The rapid development of deep learning has led to the adoption of more adaptive and complex neural network architectures capable of capturing both temporal dependencies and local patterns in sequential data. This research aims to analyze and compare the performance of several deep learning models, namely Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid Convolutional Neural Network–GRU (CNN–GRU) model for signal data classification. The research method employs a quantitative experimental approach involving data preprocessing, windowing, model training, and performance evaluation. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the hybrid CNN–GRU model outperforms the other models, particularly in capturing local features and long-term temporal dependencies within signal data. These findings suggest that the integration of convolutional layers and recurrent mechanisms enhances feature representation and learning stability. This study is expected to contribute both theoretically and practically to the development of deep learning models for signal processing and time-series-based intelligent applications.

Migrant workers are vulnerable to exploitation due to transnational and well-organized global human trafficking networks. Recently, a new modus operandi has emerged in human trafficking cases, namely recruitment under the pretext of employment in the overseas digital industry, promoted through social media and LinkedIn. Although these offers appear promising, they instead entrap migrant workers in trafficking schemes. Therefore, comprehensive case assistance involving the roles of government institutions and non-governmental organizations is urgently needed to protect victims. This study aims to examine the case assistance provided by Migrant CARE in addressing human trafficking cases in which victims are trapped as online scammers, viewed from a social welfare perspective. This research employs a descriptive qualitative approach through observation, interviews, and documentation. The findings indicate that the social interventions carried out by Migrant CARE are effective from the engagement stage through the follow-up stage. Supporting factors in the success of case assistance include proactive involvement from the government, victims, families, and the media. Meanwhile, inhibiting factors include the absence of personal documents such as passports, identity cards, and family cards, confiscation of mobile phones, and cross-national conflicts. From the individual perspective, supporting factors consist of personal resilience, courage, motivation, and skills, whereas inhibiting factors include victims’ hesitation to leave human trafficking situations due to fear of the risks involved.

The agricultural sector is a vital pillar in maintaining food security and enhancing the livelihoods of rural communities. In an effort to improve agricultural productivity, the Government of Lamongan Regency, through the Food Security and Agriculture Agency (DKPP), has implemented the Agricultural Facilities and Infrastructure Provision and Development Program. This program is designed to strengthen agricultural production capacity by supporting the availability of farming machinery, improving irrigation networks, and developing farm access roads. Despite its strategic importance, the program’s implementation continues to encounter various obstacles, including constrained budget availability, unequal distribution of agricultural facilities, reliance on rain-fed irrigation systems, substandard farm road infrastructure, and limited intensity of agricultural extension services. This research aims to examine the implementation of the Agricultural Facilities and Infrastructure Provision and Development Program in Sukodadi Village, Sukodadi District, Lamongan Regency. The study adopts a qualitative descriptive approach, utilizing data collected through in-depth interviews, direct field observations, and document review. Analysis is conducted using the Van Meter and Van Horn policy implementation framework, which emphasizes six key variables: policy standards and objectives, resource adequacy, characteristics of implementing organizations, inter-organizational communication, implementer disposition, and social, economic, and political conditions. The results reveal that although policy standards and objectives are well defined, program execution remains suboptimal due to limited resources and insufficient institutional support at the operational level. Consequently, stronger coordination among implementing actors and improved resource optimization are necessary to achieve more effective and sustainable agricultural development outcomes.  

The development of information and communication technology requires students to have digital literacy and communication skills as essential competencies for the 21st century. However, limited infrastructure and low technological literacy remain obstacles in a number of schools, including Moria High School in Sorong City. This activity aims to improve computer network technology literacy and students' speaking skills through a hands-on, practice-based training approach. The method used is qualitative with stages of observation, socialization, training, and evaluation. Computer network training includes an introduction to basic networking concepts, cabling practices, and network simulations using Cisco Packet Tracer. While speaking skills training is conducted through role-play techniques, repetition, and direct feedback. The results of the activity showed an increase in students' understanding of computer network technology, technical practical skills, and increased confidence and clarity in speaking. Evaluations showed that most participants were able to apply the material presented conceptually and practically. Thus, this activity has proven effective in improving students' computer network technology literacy and speaking skills as preparation for facing 21st-century learning and the world of work in the digital era.

The Digital Population Identity (IKD) constitutes a pivotal government innovation aimed at transforming public services, yet its widespread adoption is impeded by disparities in digital literacy and inherent technical constraints. This community engagement activity, integrated with a participatory internship scheme, concentrates on analyzing and supporting the innovation diffusion trajectory executed by the Population and Civil Registration Department (Disdukcapil) of Kendal City. The adopted methodology employs a Participatory Action approach, strategically combined with a mobile service paradigm (jemput bola) across three designated urban villages: Langenharjo, Kebondalem, and Karangsari. Core interventions involved intensive, interpersonal socialization sessions and customized technical support for account activation. The assessment findings robustly suggest that the "jemput bola" model effectively leveraged interpersonal communication channels, thereby mitigating public skepticism and substantially elevating community involvement in IKD activation. While persistent challenges related to network instability and device incompatibility were observed, this direct, hands-on approach is ascertained as fundamentally crucial for achieving effective and equitable digital transformation at the local level.

The lompong flower is an ornamental plant that requires stable soil moisture conditions and a stable environment so that it can grow optimally. Moisture level mismatches often hinder growth and even have the potential to cause plant damage. This research aims to design and implement an Internet of Things (IoT)-based humidity monitoring system that is able to monitor the humidity condition of the pond flower in real-time. The system developed uses soil moisture sensors as input devices, microcontrollers as data processors, and internet networks as a medium for sending data to the monitoring platform. The data obtained is displayed directly so that users can know the actual humidity conditions and take appropriate maintenance actions. The results show that the system is able to display moisture data with a good level of accuracy, as well as provide relevant information for users in plant care decision-making. The implementation of this system has proven to be effective in supporting the maintenance of lompong flowers, especially in maintaining soil moisture stability. This research is expected to be a reference for the development of IoT-based ornamental plant monitoring technology, as well as contribute to improving the quality of plant care in a more efficient and measurable manner.

This study aims to determine the extent of the influence of network disruptions within the Ministry of Transportation's online system on the crew signing process (sign-on/sign-off) conducted by PT. Menara Suar Maritim, Tanjung Balai Karimun Branch. The online system is a vital instrument that supports the administrative efficiency of crew signing, serving as legal evidence and fulfillment of employment requirements on board in accordance with shipping regulations in Indonesia. In practice, this system acts as a link between shipping companies, government agencies, and the crew; thus, network stability is a decisive factor for the success of the process. However, frequent network disruptions lead to service delays, accumulation of physical files, and hindered ship departure schedules, which impact company operations. The method used is field research through direct observation, interviews with relevant parties, and library research by reviewing relevant regulations and literature to strengthen the analysis. This research is expected to serve as a reference for shipping companies and related agencies in improving the effectiveness of crew signing services to support smooth operational processes while ensuring maritime safety. The results indicate that network disruptions directly affect the document verification stage, as well as the data input process for the issuance of signing certificates.

The BIMP-EAGA Vision 2035 (BEV 2035) marks a significant paradigm shift in border management in Southeast Asia, with a primary focus on integrating a more inclusive, open, harmonized, and resilient system. In this context, sea-based border crossing posts (PLBN) in Indonesia, such as the Sebatik PLBN, the Serasan PLBN, and the Miangas and Marore Border Crossing Stations (BCS), play a strategic role as maritime connectivity nodes connecting Indonesia with neighboring countries. The transformation of these PLBNs faces several challenges, particularly related to the disharmony of CIQS (Customs, Immigration, Quarantine, and Security) regulations between countries, technical obstacles related to non-conventional vessels (NCSS), and inadequate port infrastructure. To overcome these obstacles, strategic measures are needed, such as revising cross-border trade agreements, increasing quarantine capacity and facilities, and strengthening maritime logistics networks between countries. Furthermore, the development of a sustainable blue economy in border areas is crucial to ensure that the PLBN functions not only as an administrative checkpoint but also as a key facilitator in driving economic growth based on maritime resources. Within the framework of the BIMP-EAGA Vision 2035, the Integrated Maritime PLBN is expected to reduce dependence on informal trade and improve the quality of life of border communities by accelerating connectivity, developing the maritime economic sector, and opening international market access for local products. Thus, the PLBN must transform into a catalyst for inclusive and environmentally friendly economic development in Indonesia's border regions.

The training on computer network cabling conducted in schools serves not only to enhance students’ technical competencies but also functions as a strategic medium for developing collaborative attitudes, technological independence, and religious social harmony. Based on the community service activities carried out at SMA Negeri 3 Kota Sorong, the training integrated LAN cabling practice, the introduction of network devices, and guided sessions through observation, socialization, practicum, and evaluation. Throughout these stages, students gained direct experience in crimping, attaching RJ-45 connectors, testing cables with LAN testers, and simulating network configurations, which collectively strengthened their technical mastery. From the perspective of the sociology of religion, the training created a shared learning environment where students of diverse religious backgrounds collaborated equally in completing collective technical tasks. Values such as mutual respect, cooperation across differences, and social solidarity were visibly demonstrated as students helped one another in solving cabling-related challenges. Evaluation results indicate that participants became more independent in completing technical tasks and more open in communicating and interacting with peers of different beliefs. These findings emphasize that technology training can serve as a constructive medium for fostering religious social harmony and character building through practical experiences and meaningful social interaction.

Digital transformation in education requires Early Childhood Education (ECE) teachers to develop competencies in utilizing digital-based learning media. However, in rural ECE contexts, the use of technology remains limited due to the lack of contextual and sustainable training. This community service program aims to enhance the digital learning media competencies of teachers at PAUD Ath-Thoyyibah in Pangadegan Village through the Asset-Based Community Development (ABCD) approach. The program was implemented through stages of community asset mapping, collaborative action planning, practice-based training, and post-training mentoring and reflection. The results indicate that ECE teachers were able to develop simple digital learning media based on images, videos, and audio tailored to early childhood characteristics. In addition to improving technical skills, the program increased teachers’ self-confidence and fostered collaborative networks within the educational community. These findings demonstrate that the ABCD approach is effective in strengthening ECE teacher capacity by emphasizing local asset utilization and active community participation. This program contributes to improving the quality of early childhood learning and strengthening adaptive educational communities in response to technological development.

Interoperability remains one of the most persistent challenges in digital health systems globally, affecting the continuity, quality, and efficiency of care. Electronic Medical Records (EMR) managers are central to implementing and sustaining EMR systems, yet their experiences navigating interoperability challenges are poorly understood. This study aimed to explore the lived experiences of EMR managers and to develop a conceptual understanding of the barriers and coping strategies involved. A qualitative phenomenological approach was employed, guided by van Manen’s interpretive framework. The study involved 21 EMR managers from public and private hospitals across three major healthcare networks. Participants were selected through purposive and snowball sampling, with inclusion criteria requiring at least two years of EMR management experience and involvement in interoperability efforts. Data were collected via in-depth, semi-structured interviews conducted virtually between September and November 2025. Each interview lasted 45–60 minutes, was audio-recorded, transcribed verbatim, and member-checked. Data were analyzed thematically using NVivo 14, following Braun and Clarke’s six-step method. Reflexivity, triangulation, and peer debriefing ensured methodological rigor, in accordance with COREQ and SRQR standards. Four overarching themes emerged: (1) Fragmented Systems, reflecting technical and architectural incompatibilities across platforms; (2) Role Strain and Isolation, describing limited authority and institutional support; (3) Adaptive Workarounds, highlighting informal coping strategies such as manual data tracking; and (4) Systemic Barriers, pointing to policy volatility and vendor resistance. A conceptual “feedback loop of fragmentation” was developed, illustrating how these themes interact to perpetuate interoperability challenges. EMR managers operate as critical yet often overlooked actors in the pursuit of health system interoperability. Supporting their role through policy stability, resource investment, and participatory system design is essential. Future digital health reforms must integrate managerial perspectives to build resilient and connected health information systems.

This study examines the development of a Crude Palm Oil (CPO) price forecasting model using an artificial neural network algorithm, specifically the backpropagation algorithm. As one of Indonesia’s main export commodities, CPO has a significant economic impact and influences the income of oil palm farmers. The CPO price data used in this study were obtained from CIF Rotterdam, covering the period from January 2019 to December 2023. The research methodology consists of several stages, including data collection, preprocessing, model design, and model implementation using Python programming. The training results of the backpropagation algorithm show an error value of 0.537829578 after 1,000 epochs, while the evaluation using Mean Squared Error (MSE) indicates an MSE of 0.022709 during the training process and 0.017604 during the testing process. The model also produces CPO price predictions for the next three months, namely 932.578 for the first month, 949.568 for the second month, and 774.855 for the third month. These findings indicate that the developed model is capable of predicting future CPO prices with adequate accuracy, which can assist companies in making better financial decisions and managing risks associated with CPO price fluctuations.

The increasing number of Indonesian Migrant Workers (TKW) working abroad, particularly through programs organized by BP2MI, has become a significant concern in managing the labor export process. One of the challenges faced is the uncertainty of the number of TKW to be sent each year, which is influenced by various external and internal factors. Therefore, this study aims to apply artificial neural networks (ANN) with a backpropagation algorithm approach to predict the number of TKW that will be processed by BP2MI. This method was chosen due to its ability to recognize patterns and nonlinear relationships between variables that affect the decision-making process for TKW export. In this study, the data used includes factors such as the number of job seekers, government policies, and the condition of the international labor market. The artificial neural network with the backpropagation algorithm is used to train the model based on existing historical data, with the goal of generating accurate predictions regarding the number of TKW to be processed in the coming years. The results of the tests show that the developed model can provide fairly accurate predictions and can serve as a tool for BP2MI in planning and managing the export of TKW more effectively. With the application of this technology, it is expected that the decision-making process related to TKW export can become more efficient and well-predicted.