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This study examines the foundational framework and evolving dynamics of Indonesian civil procedural law (Hukum Acara Perdata) amid its transformation from colonial legal heritage toward a modern, digitalized justice system. Using normative-doctrinal legal research methodology combined with empirical court statistics from 2022–2024, the study analyzes four dimensions: (1) core procedural principles rooted in the HIR and RBg; (2) the mandatory mediation framework under PERMA No. 1 of 2016; (3) the evolution of evidence law toward electronic evidence under the amended UU ITE (Law No. 1 of 2024); and (4) mechanisms of judicial decisions and legal remedies. Findings show that e-court implementation has dramatically increased efficiency minutasi productivity rose from 64.35% (2022) to 96.50% (2024)—while 594,816 users registered through e-court by 2023. The study concludes that Indonesia’s civil procedural law is actively adapting to technological disruption, though structural challenges such as digital inequality, electronic evidence authentication gaps, and regulatory harmonization remain unresolved.

Indonesia, as a country located at the intersection of three tectonic plates, is highly vulnerable to earthquakes. This has led to the development of various innovations in earthquake-resistant housing. An interesting phenomenon can be found in Sigi District, Central Sulawesi, where several vernacular houses of the Kaili people constructed with a hybrid design (wooden frame and mud walls) have proven to withstand the major earthquake that struck Palu, Central Sulawesi, in 2018. This study aims to examine the architectural typology of non-engineered hybrid houses within the Kaili community using a qualitative descriptive approach and typological analysis. Data collection involved direct observation, interviews, and literature reviews, with a focus on the spatial and physical systems of the buildings. The sample for the study was determined through purposive sampling with specific criteria. Data analysis was conducted using a qualitative descriptive approach. The results of the study indicate that, despite changes in materials due to adaptations to technological advancements and reconstruction policies, the spatial layout and fundamental structure of the houses have maintained traditional philosophies. These houses demonstrate the integration of cultural values, local knowledge about seismic activity, and the use of local materials, which allow for both structural flexibility and some rigidity, contributing to the buildings’ resilience. These findings highlight the importance of combining technical and cultural approaches in designing earthquake-resistant houses within a local context.

The development of information and communication technology has increased the use of electronic documents in legal and business activities, thereby affecting the evidentiary system in Indonesian civil procedural law. This study aims to analyze the legal regulation of electronic evidence and its evidentiary strength in resolving civil cases under Indonesian civil procedural law. The research method used is normative juridical research with statutory and library research approaches. The data were obtained from primary and secondary legal materials and analyzed qualitatively. The results show that electronic evidence has been legally recognized through Law Number 11 of 2008 in conjunction with Law Number 19 of 2016 concerning Electronic Information and Transactions. Its evidentiary strength has the same legal position as other forms of evidence as long as it fulfills the requirements of authenticity, integrity, and validity of electronic data. Therefore, more detailed regulations regarding authentication standards and examination procedures for electronic evidence are still needed to ensure legal certainty in civil court practices.

The digital transformation of the Indonesian judicial system through the implementation of e-court and e-litigation necessitates a redefinition of conventional evidentiary laws, which have historically been governed by the Herziene Inlandsch Reglement (HIR) and the Rechtreglement voor de Buitengewesten (RBg). This research aims to analyze the evidentiary strength of electronic documents as expanded means of proof in civil proceedings and to identify the challenges regarding their implementation. The research method employed is normative legal research using a statutory approach and a conceptual approach. The findings indicate that based on the principle of functional equivalence, electronic documents hold a legal status equivalent to paper-based documents, provided they meet the requirements of integrity, accessibility, and authenticity through certified electronic signatures as mandated by Law Number 1 of 2024 concerning Electronic Information and Transactions. The evidentiary strength of an electronic document may reach the level of conclusive evidence, similar to an authentic deed, if supported by a reliable electronic system. However, implementation still faces technical hurdles concerning metadata verification and limited human resource competency within the judiciary. This research recommends the urgent need for a new codification of civil procedural law and the standardization of digital forensic procedures in trials to ensure legal certainty and justice for all parties in the digital era.

This research was designed to explore the extent to which public beliefs influence the implementation of Digital traceability  systems in the halal industrial sector. The approach used was quantitative with a survey method, where questionnaires were distributed to 60 respondents who were consumers of halal products in Indonesia. Data were analyzed using simple linear regression via Microsoft Excel. Research findings indicate that public confidence has a positive and significant influence on the adoption of Digital traceability  systems, with a regression coefficient of 0.476 and a significance level of 0.000 (<0.05). In addition, the coefficient of determination (R Square) value of 0.219 indicates that public confidence contributes 21.9% to the implementation of the Digital traceability  system, while the rest is determined by other factors that were not researched. These findings confirm that public trust is an important element in encouraging acceptance of digital technology, especially in the halal industry which relies heavily on transparency and consumer confidence. Thus, implementing a Digital traceability  system that is supported by information openness and easy access to technology can be an effective strategy to strengthen consumer trust while expanding technology adoption.

In the era of the Industrial Revolution 4.0 and Society 5.0, digital technology is developing rapidly. This has prompted the world of education to make major changes in learning methods, including in Islamic Religious Education (PAI). Until now, PAI has used outdated methods. These methods are inadequate to meet the needs of children who are accustomed to technology. Therefore, there is a need for technology integration in the learning process. This study aims to determine how to use E-Learning, Learning Management Systems (LMS), and Artificial Intelligence (AI) in PAI learning. This study also aims to determine the benefits, challenges, and strategies for implementing these technologies. This study used a qualitative approach with a literature review type. Data were obtained from various sources, such as books, scientific articles, journals, and relevant documents. The results show that the use of E-Learning, LMS, and AI can improve the quality of PAI learning. E-Learning makes learning easier and more flexible. LMS helps manage learning more systematically. AI helps make learning more personalized and tailored to students' needs. However, the implementation of these technologies still faces several challenges, such as limited infrastructure and low digital skills of teachers and students. Therefore, a strategy is needed to address these challenges. This strategy includes enhancing digital capabilities, strengthening technological infrastructure, developing innovative learning media, and monitoring the use of technology based on Islamic values. This will ensure optimal integration of technology into Islamic Religious Education learning.

This study aims to develop a financial saving application to improve the saving habits of students, particularly in Islamic boarding schools, through an adaptive challenge approach. The system integrates a mobile iOS application with a backend service and Large Language Model (LLM) processing via Ollama. Transaction data entered by users is processed by the backend to generate contextual and personalized saving challenges, applying Reinforcement Learning concepts in an adaptive and data-driven manner. The research adopts a descriptive quantitative method using surveys and system testing with 50 respondents. Results indicate that the application functions as designed, with no significant bugs detected. User evaluation shows high satisfaction, with an average score of 4.3 out of 5, covering ease of use, interface design, and increased awareness of saving. The combination of gamification, reward systems, and adaptive personalization successfully motivates users to save regularly. This system demonstrates the potential of integrating AI-driven personalization to strengthen financial literacy and healthy financial habits among students in a fun and interactive way.methods, and a summary of the results. The abstract should end with a comment about the significance of the results or conclusions brief.

This study is motivated by the increasing complexity of global culture, which demands a value system capable of providing direction and meaning in human life. In this context, Islam is viewed as having the potential to serve as a global value system through a cultural concept grounded in revelatory values. This study aims to analyze the concept of culture from an Islamic perspective as a global value system by examining the thought of Ali Ahmad Madkur. The method used is a qualitative approach based on a Systematic Literature Review (SLR), systematically and critically examining various relevant scientific literature. The results of the study indicate that Islamic culture in Madkur’s thought is built upon two main dimensions: the normative dimension, which is derived from the Qur’an and Sunnah, and the practical dimension as its actualization in social life. Although this approach has strengths in maintaining the integrity of values, it still faces challenges in responding to the complex, dynamic, and hybrid nature of global cultural dynamics. Therefore, a more integrative approach is needed to ensure that Islamic cultural values remain relevant and operational in a global context. This study has implications for strengthening more contextual studies of Islamic culture and developing Islamic education that is adaptive to the changing times.

The dissemination of personal data through digital media has increased significantly alongside the growing use of Quick Response (QR) Codes for various purposes, such as electronic tickets, certificates, and digital identities. Conventional QR Codes are open and can be easily scanned, copied, or manipulated by unauthorized parties. The personal data referred to in this study includes sensitive information such as full name, identity number (NIK/National ID), date of birth, address, phone number, and email address. This research proposes a layered security system that combines the Advanced Encryption Standard (AES) cryptographic algorithm with steganography using the Discrete Cosine Transform (DCT) method. The process begins with encrypting personal data using AES, converting the encrypted result into a QR Code, and embedding the QR Code into a digital image using DCT, hiding it in the image’s frequency domain. The digital images used are of fixed size and formats that preserve visual quality. System evaluation is carried out by testing the visual quality of the stego image, the success rate of QR Code extraction, and the integrity of the encrypted data. The results are expected to conceal sensitive information visually while maintaining its confidentiality, with potential applications in electronic ID cards, digital certificates, e-tickets, and other confidential documents.

The enactment of Indonesia's Law Number 20 of 2025 on the Code of Criminal Procedure (KUHAP 2025), effective January 2, 2026, introduces a paradigmatic shift in the coordination between investigators and public prosecutors: Article 58 mandates active coordination from the investigation stage, fundamentally departing from the sequential-passive model of the former KUHAP, while Article 70 imposes a strict seven-day deadline for indictment drafting after case files are declared complete. This study examines two interconnected questions: (1) how the legal framework governing investigator–prosecutor coordination is structured under KUHAP 2025 and related legislation; and (2) how that framework is implemented in practice at the Purwakarta District Prosecutor's Office. A normative–empirical mixed-method design was employed, integrating statutory, conceptual, and case-study approaches. Data were gathered through in-depth interviews with prosecutors and investigators at Purwakarta District Prosecutor's Office and Purwakarta Police Resort, case document analysis, and field observation. The theoretical framework combines Lawrence M. Friedman's Legal System Theory and Soerjono Soekanto's Law Enforcement Theory. Findings reveal that KUHAP 2025 delivers substantial normative advancement yet harbours three critical regulatory gaps: the absence of binding technical protocols for implementing mandatory active coordination, the lack of uniform and measurable case-file completeness standards, and no formal mechanism for resolving institutional disagreements on legal interpretation. On the ground, coordination at Purwakarta still operates under the old sequential-passive pattern despite the new law: case-file returns (P-19) remain frequent, driven primarily by absent expert testimony, insufficient factual narration in examination records, and mismatches between charged articles and legal facts. A Friedman–Soekanto diagnostic reveals simultaneous dysfunction across all three legal system components substance, structure, and legal culture with the entrenched 'waiting culture' between the police and the prosecution identified as the most resistant obstacle to reform.

This research is motivated by the problems in manual attendance systems at schools, which remain vulnerable to fraud, time-consuming, and inefficient. The expected solution is to develop an automated attendance system based on face recognition that can operate in realtime with high accuracy. The research object is vocational high school students, with the applied method implementing the YOLO v10 algorithm for face detection, followed by the face_recognition library for identification. The instruments used include an Imou CCTV camera as the input device, a mid-range laptop as the hardware platform, and Python with SQLite as the software environment for data processing and attendance storage. The results show that the developed system achieved an average face detection accuracy of 96% under normal lighting and 91% under low lighting, with an average processing speed of 27 FPS. The implementation of an anti-duplication feature also ensured data validity by allowing each student to be recorded only once per day. In conclusion, the use of YOLO v10 in face-based attendance proved to be effective, efficient, and capable of reducing fraud. The implication of this study is that the system can be applied in both Islamic boarding schools and general schools as a modernization of attendance systems, with a recommendation for further development through web-based application and cloud database integration.

Bullying is a serious problem that can disrupt the learning process and mental development of students, including in Islamic boarding schools. Early detection of bullying is essential to creating a safe and conducive learning environment. This study aims to apply the You Only Look Once (YOLO) algorithm to automatically detect bullying through video recordings in the environment of the SMK Skill Village Islamic School Business Boarding School. The method used involves collecting a video dataset representing various types of bullying behavior, labeling the data, and training an object detection model using the YOLOv5 algorithm. The developed system is capable of detecting and classifying bullying behavior in real- time with detection accuracy reaching [accuracy value if known]. The implementation of this system is expected to assist school authorities and boarding school administrators in monitoring, preventing, and addressing bullying incidents more quickly and effectively, while also serving as an initial step in leveraging artificial intelligence technology to create a safer and more comfortable educational environment.

QRIS (Quick Response Code Indonesian Standard) has become a widely used national digital payment standard. User satisfaction with this service needs to be monitored continuously to ensure its sustainability. This study aims to predict the level of QRIS user satisfaction based on their experiences and perceptions expressed organically on the Twitter social media platform. The method used is sentiment analysis with the Naive Bayes classification algorithm implemented using RapidMiner software. The research data was obtained from Twitter user comments collected through web scraping techniques. The text data then went through a preprocessing stage that included cleansing, stopword filtering, stemming, and tokenizing to be prepared as features ready to be processed by the model. The data was divided into training (80%) and testing (20%) subsets for model training and validation. The results showed that the Naive Bayes model was able to predict user satisfaction sentiment with an accuracy of 80.99%. These findings indicate that the model is highly accurate in identifying satisfied comments and sufficiently sensitive in detecting dissatisfaction. This study concludes that sentiment analysis of Twitter UGC data using Naive Bayes is an effective and efficient approach for predicting QRIS user satisfaction in real time. The practical implication of this study is to provide an automatic feedback system for service providers to monitor public sentiment and take targeted corrective actions.

Digital transformation in the judicial system represents a strategic step to improve the quality of legal services in Indonesia. One of the innovations implemented by the Supreme Court of the Republic of Indonesia is the E-Court system, which enables case administration and court proceedings to be conducted electronically. This study aims to analyze the effectiveness of the implementation of E-Court in realizing the principles of simple, fast, and low-cost justice, as well as to identify the obstacles faced by parties who do not utilize the system. The research method employed is a normative legal approach, involving the analysis of statutory regulations and scholarly literature. The results indicate that, normatively, E-Court has been effective in supporting judicial efficiency; however, in practice, it still encounters various challenges, including technical, social, and institutional aspects. These obstacles vary among different legal subjects, namely advocates, individuals, private legal entities, and government legal entities. Therefore, comprehensive efforts are required to optimize the implementation of E-Court to ensure that the objectives of judicial reform can be fully achieved. Furthermore, the findings reveal that, from a normative perspective, E-Court provides a more efficient mechanism compared to conventional systems, particularly in terms of case administration and communication between parties. However, this effectiveness has not been evenly realized due to constraints such as the digital divide, limited technological infrastructure, and low user readiness. Consequently, the implementation of E-Court still requires reinforcement in both technical and cultural aspects to ensure optimal accessibility for all segments of society.

The rapid growth of digital commerce has encouraged companies to integrate digital and physical marketing channels to create seamless and consistent customer experiences. This study aims to analyze the role of integrated marketing channels through omnichannel strategies, the utilization of Artificial intelligence (AI), and their impact on customer experience in the context of digital commerce. The study employs a Systematic literature review (SLR) approach by examining relevant scholarly articles related to omnichannel marketing, AI technologies, and customer experience. The findings indicate that integrated marketing channels supported by AI enhance service personalization, customer engagement, operational efficiency, and the quality of interactions between companies and customers. Furthermore, the implementation of omnichannel strategies contributes to higher customer satisfaction and loyalty by providing a more connected experience across multiple customer touchpoints. However, the implementation of integrated marketing channels still faces several challenges, including fragmented channel integration, technological complexity, high investment requirements, and concerns regarding customer data privacy and security. Therefore, effective management of integrated marketing channels is essential for improving customer experience while creating sustainable competitive advantages for companies in an increasingly dynamic digital era.

This community service program addressed the limited capacity of educators to use artificial intelligence in a structured and pedagogically responsible way for textbook development. The program focused on empowering educators through NotebookLM in the Ngabuburit AI 2026 activity. Its objective was to improve participants’ understanding and practical skills in organizing sources, designing textbook structures, developing chapter content, and generating interactive learning media. The program used a participatory training approach consisting of presentation, demonstration, guided practice, discussion, and feedback-based evaluation. The results showed that participants gained a clearer understanding of AI-assisted academic writing workflows and recognized NotebookLM as a useful tool for integrating source analysis, textbook writing, and learning media preparation. The activity also fostered new awareness that artificial intelligence can strengthen, rather than replace, educators’ academic roles in producing more systematic and interactive teaching materials.

Early detection of a potential heart attack is a crucial step in preventing sudden death from heart disease. This research aims to develop an Internet of Things (IoT)-based health monitoring system capable of measuring vital body data in real time and predicting the likelihood of a heart attack from CSV data obtained from sensors, integrated through RapidMiner as learning data using a machine learning algorithm, the Support Vector Machine (SVM). The system was built using an ESP32 microcontroller connected to a MAX30102 sensor to measure heart rate and finger oxygen levels (SpO₂), as well as a DHT22 sensor to measure temperature and humidity. The resulting data is sent to the Blynk application to display real-time data according to its parameters. The initial prediction logic was developed using a rule-based method based on medical thresholds for four vital parameters. The data was then used to train an SVM model as a classification system to detect potential heart attacks. Test results showed that the system can identify abnormal conditions with a good level of accuracy and provide early warnings based on changes in vital parameters in real time. This system is expected to be an initial solution for personal health monitoring, especially for individuals at risk of heart disease. It can be further developed with cloud integration and automatic notifications to users' devices.

SR12 Herbal Cosmetics is a company engaged in the field of herbal and skin care. Founded in 2015 byToni Firmansyah, S. Farm., Apt. and Asrianty Salam, Farm. This company has a vision to provide benefits to many people through the herbal and skin care products they produce. SR12 Herbal Cosmetics products are formulated based on research from certified scientists, and have been tested at the Sucofindo Laboratory, are free of mercury and hydroquinone, and have been registered with the Indonesian Food and Drug Supervisory Agency (BPOM RI). SR12 Herbal Cosmetics has several factories in West Java Province and has an extensive distribution network with hundreds of distributors and tens of thousands of partners throughout Indonesia. The goal to be achieved is to produce a management information system model including a management information system for PT SR12 Herbal Cosmetics. The research object chosen is a company in the field of cosmetics and skin care which has its head office in Gunung Sindur, West Java. This selection aims to form a management information system design model that is able to produce relevant and timely information for planning, controlling, decision making and evaluating the performance of activities. For the Web-Based Instagram Content Management Information System Design project to Support SR12 Herbal Cosmetics' Brand Awareness, I used Agile (Scrum) due to the dynamic nature of digital marketing and potential changes to the Instagram API or business needs. This allowed SR12 to get core functionality faster and provide iterative feedback, ensuring the system built was truly relevant to their brand awareness needs.

Social crimes at the village level require participatory and sustainable prevention approaches. This study aims to analyze the synergy between the Sidoarjo Police Resort through its BINMAS unit and the residents of Waruberong Village in implementing the “4D” anticipation framework (Corruption, Narcotics, Bullying, and Sexual Harassment). A descriptive qualitative method was employed, utilizing data collection through observation, semi-structured interviews, and documentation analysis of socialization activities and police-community forums. The results indicate that this partnership successfully shifts the security paradigm from a reactive enforcement model toward proactive collaboration grounded in education and early reporting. BINMAS functions effectively as an administrative liaison, coordinating Senkom Mitra Polri, village officials, and residents, thereby standardizing and streamlining report handling processes. The implementation of the 4D framework significantly enhances public legal literacy, strengthens trust in the DUMAS Presisi system, and encourages active community participation in security patrols and awareness campaigns. Although minor administrative challenges, such as limited reporting literacy and fluctuating attendance, were identified, they can be mitigated through structured mentoring. Overall, the 4D-based partnership model proves highly relevant for strengthening participatory security ecosystems at the village level. This study recommends standardizing educational modules, providing continuous administrative training for Senkom members, and integrating digital reporting platforms to ensure the program's long-term sustainability.

This study discusses a student concentration detection system using Convolutional Neural Network (CNN) with the MobileNetV2 architecture. The dataset was adapted from Classroom Student Behaviors and mapped into four concentration categories: highly focused, focused, less focused, and unfocused. The system was tested with a 720p webcam and produced real-time detection data. The evaluation results show an overall accuracy of 75.85%, with the highest precision achieved in the focused class (0.9859) and the highest recall in the highly focused (0.9739) and unfocused (0.9811) classes. The confusion matrix indicates that the focused class was detected most consistently, while highly focused and unfocused classes were often misclassified as focused, resulting in lower precision. In real-time testing, the system operated at an average of 7 FPS and worked optimally when students faced the camera directly with sufficient lighting, but its performance decreased significantly at face angles greater than 45°. User evaluation shows that 75% of students rated the detection results as accurate/very accurate with an average satisfaction score of 3.6 out of 5, and 75% felt assisted in recognizing their concentration level. From the teachers’ perspective, most stated that the results were consistent with classroom observations, and all expressed willingness to reuse the system.