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Martial arts training centers are essential facilities that support the technical, physical, and mental development of athletes. This study aims to identify the facility requirements and architectural design criteria for martial arts training centers in Surabaya. A qualitative descriptive method was employed, utilizing literature reviews, field observations, and interviews with relevant stakeholders. The findings indicate that the required facilities include a main training area, supporting spaces, and operational support facilities, all of which must be designed in accordance with the specific characteristics of each martial art discipline. Furthermore, the implementation of appropriate zoning systems and design criteria—such as flexibility, safety, comfort, and functional integration—plays a significant role in enhancing the effectiveness of the training environment. Properly designed facilities not only accommodate diverse training needs but also contribute to optimizing athlete performance. Therefore, a comprehensive architectural approach is necessary to ensure that martial arts training centers can function efficiently and sustainably in supporting long-term athlete development.

The relocation of the Social Welfare Shelter Environment (Lingkungan Pondok Sosial/Liponsos) in Sidoarjo represents a strategic effort to address social and spatial issues characterized by overcapacity, limited facilities, and an inadequate living environment that does not fully support the physical and psychological needs of people with social welfare problems (PMKS). These conditions indicate that the challenges faced by Liponsos are not merely social in nature but are also closely related to the quality of the built environment, which significantly influences the rehabilitation and recovery process of its residents. Therefore, a human-centered design approach is required in planning social service facilities. This study aims to analyze the application of humanistic architecture principles in the planned relocation of the Sidoarjo Liponsos as a foundation for creating a more livable, dignified, and user-oriented environment. The research employs a literature review method by collecting data from relevant books, journals, regulations, and previous studies. The analysis is conducted using descriptive and content analysis to examine the relationship between humanistic architectural principles and the needs of residents within social welfare facilities. The results indicate that the humanistic architectural approach is highly relevant to the relocation of Liponsos, particularly through spatial zoning based on levels of privacy and security, the provision of green open spaces and communal areas as media for social interaction and therapeutic activities, and the design of self-development spaces that support rehabilitation and empowerment. The implementation of these principles is expected to create a built environment that not only fulfills basic functional requirements but also supports psychological recovery, social stability, and sustainable improvement in the quality of life of PMKS residents.  

This research examines the implementation of mobile public services in archipelagic contexts, where dispersed settlements, dependence on sea transportation, weather uncertainty, and limited digital connectivity significantly constrain service access and continuity. Although digital government has developed rapidly, most previous studies focus on urban or mainland settings and rarely consider geography as a determining factor. This creates a gap in understanding how archipelagic conditions interact with governmental capacity, governance structures, and frontline practices. The study aims to explain the mechanisms of mobile service implementation under these constraints and to develop an Archipelagic Implementation Framework that integrates context, capacity, governance, and outcomes. Using a qualitative single-case study approach in Raja Ampat Regency, Indonesia, data were collected through in-depth interviews, policy documents, and limited observation, then analyzed using hybrid thematic analysis. The findings reveal that archipelagic constraints lead to frequent rescheduling, hybrid online–offline service delivery, increased coordination demands, and connectivity challenges. Service sustainability depends on staff rotation, portable infrastructure, and adaptive strategies by frontline actors. The proposed framework highlights how contextual constraints shape administrative capacity, coordination, and service outcomes, offering practical insights for resilient public service delivery.

This study was designed to create a visual communication medium in the form of a video mapping depicting the institutional change process from the Nahdlatul Ulama Institute of Technology and Science (ITSNU) Pasuruan to the Nahdlatul Ulama University of Pasuruan (UNU Pasuruan). This institutional status change represents a strategic moment that needs to be communicated to the public through a creative and engaging media approach. The research approach used the Research and Development (R&D) method, referring to Sugiyono's development framework, which consists of ten stages: identification of potential and problems, data collection, product design, design validation, design refinement, product testing, product refinement, implementation trials, final product revisions, and mass production (if necessary). Data collection techniques included in-depth interviews, field observations, documentation studies, and validation by media and content experts. The research output is a video mapping product, approximately 7 minutes in length, that has undergone a validation process and is deemed suitable as a means of communicating institutional change. This product is expected to serve both as a visual archive and as a more effective and communicative institutional promotion instrument.

This study aims to describe the concept of Immersive Commerce based on Augmented Reality (AR) in increasing consumer confidence in E-Commerce transactions and to design a conceptual prototype design framework for an AR E-Commerce system with a clear technical flow. The research method uses a literature study through a comprehensive review of AR-based E-Commerce implementations and needs analysis from the perspective of consumer problems and business opportunities. The main problem identified is consumer hesitation in purchasing products online due to limited visualization that relies solely on product photos, causing fears about differences in shape, size, and quality of goods. The research results produced a conceptual framework that includes an integrated system architecture with a frontend layer (AR visualization engine), backend layer (product database and 3D asset management), and integration layer, as well as a systematic user flow design from the discovery to the decision phase. Based on simulations using literature data, the designed framework has the potential to increase customer engagement by up to 169%, conversion rates by up to 11 times, and reduce product return rates by up to 50%. The benefits of this research include digital dimensions through E-Commerce technology innovation, social dimensions by increasing consumer trust and reducing product fraud, and environmental dimensions through the potential reduction of product returns. This conceptual prototype provides a foundation for digital innovation, social trust building, and environmental sustainability through reduced product returns.

The Regional Public Library of Sidoarjo Regency plays an important role as a public facility in supporting the development of community literacy. Along with population growth and the advancement of the education sector, the library is required to provide spaces that are comfortable, safe, and inclusive for all users. However, existing conditions indicate various architectural problems that potentially hinder the optimal function of the library. This study aims to identify architectural issues at the Regional Public Library of Sidoarjo Regency, including building conditions, spatial layout, user comfort, and the availability of thematic literacy facilities and inclusive spaces. The research method employed observation and literature review. Field observation was conducted to document the actual conditions of the building and library facilities, while the literature review focused on library standards, building regulations, and relevant architectural theories. The results show that although the library building is structurally adequate, the quality of space has not fully met the required standards of comfort, safety, and functionality. The spatial arrangement remains conventional and lacks flexibility, limiting its ability to support diverse literacy activities. In addition, thematic literacy facilities and the application of inclusive design principles are still limited. This study concludes that architectural development efforts are needed, focusing on improving spatial quality, implementing adaptive spatial planning, and providing more diverse and inclusive literacy facilities. The findings are expected to serve as a reference for future development planning of the Regional Public Library of Sidoarjo Regency in order to enhance its role as an educational public space and a community literacy center.

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.

Micro, Small, and Medium Enterprises (MSMEs) constitute the backbone of Indonesia's national economy, contributing approximately 61% of GDP and absorbing 97% of the total workforce; however, the majority of MSME actors, particularly in eastern Indonesia, continue to face structural barriers in digital technology adoption and capital access that constrain optimal business performance. This study aims to analyze the partial and simultaneous effects of marketing digitalization and business capital on MSME performance in Ambon City. A quantitative approach with associative-causal design was employed, involving 30 respondents selected through purposive sampling from active MSME operators in Ambon City. Data were collected via a five-point Likert scale questionnaire and analyzed using multiple linear regression with IBM SPSS version 26, preceded by validity, reliability, and classical assumption tests. Results demonstrate that marketing digitalization exerts a positive and significant partial effect on MSME performance (t = 8.060; sig. = 0.000; β = 1.061), establishing it as the most dominant predictor in the model. Conversely, business capital shows no significant partial effect (t = 0.746; sig. = 0.462), attributable to the homogeneity of capital access among MSME actors in archipelagic regions. Simultaneously, both variables significantly influence MSME performance (F = 287.070; sig. = 0.000), with an exceptionally high R Square of 0.955, indicating that 95.5% of performance variance is collectively explained by the two predictors. These findings underscore the critical role of digital marketing capabilities over financial resources alone in archipelagic contexts. It is recommended that the Ambon City Government integrate digital marketing literacy training programs synergistically with inclusive financing schemes to comprehensively strengthen MSME competitiveness across the Maluku archipelago.

The digital era demands a fundamental transformation in the role of early childhood educators, shifting from passive technology consumers to active architects of digital literacy. However, the dominance of technocentric views often acts as a substantial psychological and pedagogical barrier for teachers in regional areas. This collaborative community service project aims to reconstruct the paradigm of 50 kindergarten teachers in Bulukumba Regency by integrating "unplugged coding" logic and deep learning into play-based learning. Utilizing a Product-Based Intensive Training method with a "Logic over Laptop" strategy, the program focused on deconstructing technology-related stigmas and reconstructing teachers' ability to transform abstract concepts into safe, concrete media for children. Data analysis revealed a significant shift in teacher paradigms; while the majority were initially in the "less successful" category, 100% of participants reached positive categories (successful and very successful) post-intervention. Statistically, the program's effectiveness was evidenced by a dramatic increase in mean scores from 18.04 to 31.24 (p < 0.05) and an N-Gain score of 0.778, classified as highly effective. Furthermore, the partner satisfaction index reached 4.82 (very satisfied), confirming that the tri-campus collaboration model (STAI Al-Gazali, UNM, and Unismuh) is highly relevant to the implementation of the Merdeka Belajar curriculum. This project concludes that strengthening digital literacy through non-digital algorithmic reasoning effectively dismantles technical barriers for teachers while ensuring the safety of child development in the digital age.

The Secretariat of the Regional People's Representative Council (DPRD) of Southwest Sumba Regency plays an important role in supporting local government administration. In carrying out its duties, the Secretariat produces various administrative documents that must be properly managed and maintained. However, archive management is still conducted conventionally through physical storage, which increases the risk of document damage, loss, and inefficiency in retrieval. This study aims to develop a web-based archive management information system to improve the effectiveness of document management. The research methodology involves several systematic stages, including needs analysis, system design, development, and system implementation. Data were collected through observation and interviews to identify existing problems in archive management practices. The results show that the developed system provides significant improvements in managing archives electronically. It enables faster data access, better document organization, and reduced risk of physical damage. Therefore, the system offers a practical solution to enhance administrative efficiency and support digital transformation within the DPRD Secretariat of Southwest Sumba Regency.

Attention mechanisms have been widely incorporated into recurrent neural network architectures for financial time series forecasting, with most prior work reporting improvements in price-level error metrics. This study revisits that claim through a controlled empirical comparison of four deep learning architectures on nearly two decades of Telkom Indonesia (TLKM) closing price data from the Indonesia Stock Exchange (IDX). The models evaluated are a three-layer Gated Recurrent Unit (GRU) baseline, a comparable Long Short-Term Memory (LSTM) network, a Bahdanau end-attention GRU (Attn-GRU-V2), and a multi-head self-attention GRU hybrid (Attn-GRU-V3). Each architecture is trained over 30 independent runs with distinct random seeds, and performance is reported as 95% confidence intervals derived from the t-distribution. Statistical comparisons employ the Wilcoxon signed-rank test, a nonparametric paired test appropriate given the confirmed non-normality of residuals. The main finding is a consistent trade-off: the plain GRU achieves the lowest RMSE (94.02 ± 1.22 IDR) across all 30 runs, while Attn-GRU-V2 achieves the highest directional accuracy (45.91 ± 0.09%), surpassing GRU in every independent run. Bahdanau attention weights are nearly uniform across the 30-day lookback window (coefficient of variation: 3.21%), indicating that the mechanism cannot identify selectively informative timesteps in this univariate price series. This finding is consistent with the weak-form Efficient Market Hypothesis for the Indonesian market. An ablation study reveals that a 20-day lookback window maximizes directional accuracy (47.72 ± 0.21%) for the Attn-GRU-V2 model. These results suggest that Bahdanau end-attention consistently and significantly improves directional accuracy relative to a plain GRU baseline, providing an architecturally attributable advantage for direction-based applications, even when absolute price-level error is not reduced. The directional accuracy values remaining below 50% across all models are consistent with a weak-form efficiency characterization of the Indonesian market.

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.

The money market plays a vital role in the global financial architecture as a provider of short-term liquidity and a primary channel for monetary policy transmission. This research is motivated by the rapid transformation of financial instruments, which now encompass conventional and Sharia-compliant sectors, as well as digital innovations such as e-money and stablecoins. The purpose of this study is to examine the concept of the money market, identify the diversity of modern instruments, and analyze their strategic role in economic stability through a qualitative literature review approach. The analysis shows that the money market is highly effective in managing bank cash reserves and controlling inflation by regulating the money supply. The presence of digital instruments has been proven to accelerate liquidity flows, while Sharia schemes provide transparent and equitable investment alternatives. However, the emergence of digital assets also brings challenges of volatility that require adaptive regulation and professional skepticism from market participants. The implications of this research emphasize the importance of synergy between monetary authorities and financial technology to address global disruption. Strengthening regulations on future instruments is expected to create a more inclusive and stable financial system that can respond precisely to economic shocks.

Cohabitation—defined as the practice of two unmarried individuals living together in an intimate relationship—presents a growing normative challenge in Indonesian society, which maintains a theistic constitutional foundation while navigating rapid social change. This article examines cohabitation through three juridical lenses: first, the regulatory architecture through which Indonesian law prohibits cohabitation; second, the criminalization framework established under Law Number 1 of 2023 on the National Criminal Code; and third, the practical and theoretical problems that emerge in law enforcement. Employing a normative legal research methodology that integrates statutory, conceptual, and comparative approaches, this study finds that prohibitions against cohabitation are embedded across multiple regulatory layers—spanning marriage law, customary law, codified Islamic law, and regional legislation. The National Criminal Code, through Articles 412 and 413, introduces for the first time an explicit criminalization of cohabitation within Indonesia's post-independence penal codification. Nevertheless, the complaint-based offense structure, evidentiary challenges, and persistent tension between formal norms and social realities produce a substantial implementation gap. The study concludes that legal effectiveness in addressing cohabitation requires coordinated action across criminal enforcement, family institution strengthening, and sustained legal literacy programs.

Community service is a form of implementation of the Tri Dharma of Higher Education which aims to make a real contribution to society through the application of knowledge and skills possessed by students. The rapid development of the business world requires every company to be able to manage operational activities effectively and efficiently. In carrying out company activities, administration has an important role in regulating various activities related to data recording, document management, and archiving company information. This service activity was carried out at CV. Stara Maju Perkasa with the aim of assisting in the implementation of more orderly and systematic operational administration. The method used is a descriptive method with a participatory approach through observation, interviews, and assistance in operational administration activities. The results of the activity show that the implementation of operational administration can improve the neatness of data recording, document management, and archiving of company archives so as to facilitate the process of searching for information. In addition, this activity also improves employee work efficiency and minimizes errors in administrative management. Thus, the implementation of good operational administration is able to support the smooth running of the company's activities as a whole.

Bangunan Landhuizen Kelengan di Kota Semarang, merupakan salah satu bangunan yang memiliki arsitektur Indische Empire Style. Arsitektur Indische Empire Style merupakan arsitektur yang bukan murni arsitektur Belanda, tetapi sudah beradaptasi dengan iklim setempat. Arsitektur Indische Empire Style hadir melalui karya arsitek Belanda dan diperuntukkan bagi bangsa Belanda yang tinggal di Indonesia, pada masa sebelum kemerdekaan Indonesia. Bangunan Landhuizen Kelengan dibangun pada awal abad ke 18, tetapi tidak ada data pasti mengenai tahun berdirinya bangunan tersebut. Fungsi awal bangunan ini merupakan sebuah rumah tinggal dari seorang pejabat VOC (Vereeniging van Oost-Indische Compagnië) yang bernama Tuan Klein, kemudian berpindah tangan kepada Be Biauw Tjoan, seorang pedagang, pebisnis, serta pemimpin masyarakat Tionghoa (opsir) di Kota Semarang. Penelitian ini bertujuan untuk mengetahui gaya karakter arsitektur Indische Empire Style pada bangunan Landhuizen Kelengan di Kota Semarang. Penelitian ini menggunakan metode deskriptif kualitatif, yaitu dengan melakukan observasi, pengukuran, pengambilan foto, serta dokumentasi  secara langsung di lapangan. Hasil penelitian pada bangunan Landhuizen Kelengan berupa temuan elemen penyusun fasad pada bangunan yang berarsitektur Indische Empire Style.

The rapid development of digital technology in the 21st century has brought significant changes to various aspects of life, including education. One of the emerging innovations in the educational field is the use of Artificial Intelligence (AI) as a tool to support the learning process. The integration of AI in history education offers new opportunities to create more interactive, adaptive, and contextual learning experiences through access to various digital historical sources. This study aims to analyze the integration of Artificial Intelligence in history learning and its implications for improving students’ digital historical literacy. This research uses a qualitative approach with a descriptive method through a literature study by reviewing various scientific articles related to the use of AI in education and digital literacy in history learning. The findings indicate that the use of Artificial Intelligence can assist students in accessing, analyzing, and understanding various digital historical sources more broadly through interactive media such as online archives, historical simulations, and technology-based learning platforms. In addition, the integration of AI can increase student engagement in the learning process, encourage critical thinking skills, and support more personalized learning based on students’ needs and abilities. However, the implementation of this technology must be accompanied by the strengthening of digital historical literacy as well as the application of academic integrity, ethical principles, and responsible use of technology. Therefore, the proper integration of Artificial Intelligence can serve as an innovative strategy to enhance the quality of history education in the digital era.

The increase in the number of elderly residents demands the provision of residential facilities that not only meet physical needs, but also support the psychological and social well-being of their users. This study aims to formulate the concept of behavioral architecture-based nursing home design by focusing on the relationship between elderly activity patterns, privacy levels, and spatial relationships of space in the local context of Indonesia. This study uses a qualitative-descriptive approach in the framework of architectural design, with data collection through observation of elderly activities, site analysis, and documentation studies. Activity data was analyzed to identify space needs and usage patterns, then synthesized with site characteristics to formulate the concepts of zoning, circulation, and behavior-based spatial relationships. The results of the study show that the activities of the elderly form a layered behavioral structure, including residential and health activities as primary needs as well as social, productive, and educational activities as support for psychosocial welfare. Hierarchically arranged space zoning based on privacy levels has been proven to improve the readability of the space, sense of security, and comfort of the elderly. The integration of green open spaces as part of the activity system also strengthens support for light physical activity and social interaction. This study confirms that the application of behavioral architecture allows the translation of data on elderly behavior and site conditions into a contextual, humanist, and quality-of-life-oriented design concept. These findings provide practical implications for designers and policymakers in the development of sustainable elderly housing.

Implementing REST in modern applications, security will be a key foundation for its development because the REST architecture requires communication between servers. In this study, we will enhance REST request security by using SHA-1 tokens and the Keccak algorithm. Tokens are the access keys for making requests. The token generation process is carried out on the server; the client will generate a token, and the server will return a valid token. This valid token can be used to request data from the server. Adding a token will impact the security and speed of REST. The token will be verified by the server and declared valid. If valid, the server will return the data; otherwise, the server will send an error message. Compared to using a token, data security is more assured. Furthermore, adding a token parameter will increase the token verification process, thus increasing the number of processes, which will impact speed. The results of this test show that server data security is better maintained and more secure compared to using a token, because anonymous users cannot access the data. The API access speed without a token is 48.8 milliseconds, while using a SHA-1 token is 62.3 milliseconds, and the Keccak algorithm is 58.9 milliseconds. The time efficiency reduction for implementing the SHA-1 token algorithm is 27.67% or 13.5 milliseconds, and the Keccak algorithm is 26.6% or 10 milliseconds.