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Starfruit (Averrhoa bilimbi L.), commonly known in Indonesia as belimbing wuluh, is widely used as a natural acid in traditional cuisine. It is often processed into dried sour starfruit (asam sunti), which can last 1–1.5 years. However, traditional sun-drying methods are inefficient due to weather dependency, long processing times, and inconsistent product quality and color. This study aims to design and develop a tunnel-type starfruit dryer equipped with a blower system and heat control based on the Arduino Mega 2560. The research employs a quantitative method to evaluate tool performance. The dryer is cylindrical and supported by key components, including LPG gas as a heat source, a blower for air circulation, and a drum holder. Drying is conducted in six stages, each lasting 120 minutes, totaling 12 hours to achieve optimal dryness. Temperature monitoring at three points (T1, T2, T3) uses a MAX6675 sensor with a thermocouple connected to the Arduino Mega 2560, while weight measurement is done manually. Results indicate the tool functions effectively. A denser drying chamber and proper blower installation are recommended to ensure even heat distribution and improved drying efficiency for community use.

Starfruit (Averrhoa bilimbi L.), commonly known in Indonesia as belimbing wuluh, is widely used as a natural acid in traditional cuisine. It is often processed into dried sour starfruit (asam sunti), which can last 1–1.5 years. However, traditional sun-drying methods are inefficient due to weather dependency, long processing times, and inconsistent product quality and color. This study aims to design and develop a tunnel-type starfruit dryer equipped with a blower system and heat control based on the Arduino Mega 2560. The research employs a quantitative method to evaluate tool performance. The dryer is cylindrical and supported by key components, including LPG gas as a heat source, a blower for air circulation, and a drum holder. Drying is conducted in six stages, each lasting 120 minutes, totaling 12 hours to achieve optimal dryness. Temperature monitoring at three points (T1, T2, T3) uses a MAX6675 sensor with a thermocouple connected to the Arduino Mega 2560, while weight measurement is done manually. Results indicate the tool functions effectively. A denser drying chamber and proper blower installation are recommended to ensure even heat distribution and improved drying efficiency for community use.

The computer laboratory is an essential facility in higher education that requires efficient management of usage and environmental conditions to support the teaching and learning process. However, laboratory management at the Kalimantan Business and Technology Institute is still carried out manually, including scheduling, room condition monitoring, and report creation, which is prone to errors and time-consuming. This study aims to develop an Internet of Things (IoT)-based laboratory monitoring system prototype to improve the effectiveness of computer laboratory management. The approach used is Research and Development (R&D) with a prototype development model, allowing for design adjustments based on user feedback iteratively. Data were collected through observations, interviews, and document studies related to laboratory conditions and analyzed to determine the main system features, such as temperature and humidity monitoring, scheduling, and report generation. The results of the study show that the developed prototype can structure the laboratory workflow, provide real-time monitoring, facilitate schedule management, and simplify report preparation. This prototype is expected to serve as a foundation for developing a more comprehensive application, improving data accuracy, time efficiency, and the quality of laboratory management.

This study aims to provide a comprehensive analysis of Predictive Policing and its implications for law enforcement transformation in Indonesia, based on an extensive review of its global applications, benefits, and challenges. The study uses qualitative literature and international case study review methods to assess the impact and complexity of implementing digital technologies such as artificial intelligence (AI), machine learning, and big data analytics within a Predictive Policing framework. The results of this review highlight that while Predictive Policing offers significant potential for proactive crime prevention and increased operational efficiency, its implementation is consistently fraught with critical legal, ethical, and technical challenges, including regulatory gaps, risks of algorithmic bias, and data privacy concerns, which are particularly relevant to Indonesia. The findings underscore that public trust and police legitimacy in the context of adopting such technologies are strongly influenced by transparency, strong accountability mechanisms, and community involvement in shaping their use. This study contributes to the growing discourse on digital policing in developing countries and culminates in practical policy recommendations designed to guide the Indonesian police towards the development and implementation of Predictive Policing models that are effective, efficient, and fundamentally respectful of legal and human rights principles.

Official travel constitutes a routine yet strategically significant component of local government administration, closely intertwined with bureaucratic processes, public financial management, and accountability arrangements. Despite its operational importance and fiscal visibility, official travel management has received limited integrative attention in the public administration literature, and existing studies remain fragmented across procedural, financial, and governance perspectives. This article addresses this gap by providing a comprehensive literature review on administrative efficiency in official travel management within local governments, with particular attention to bureaucratic processes and cost control mechanisms. Adopting a narrative–analytical literature review design, the study employs a state-of-the-art and theory-driven synthesis of recent peer-reviewed scholarship in public administration, public financial management, governance, and related fields. The analysis integrates thematic and conceptual synthesis techniques to identify recurring patterns, relationships among key concepts, and unresolved issues in the literature. The findings reveal consistent patterns of procedural inefficiency, including administrative burden, complex approval chains, and process fragmentation, which persist even under formal cost control and accountability systems. The review further demonstrates that compliance-oriented financial controls often secure fiscal conformity without necessarily improving administrative efficiency, particularly when misaligned with bureaucratic workflows and constrained by limited administrative capacity. Governance and accountability mechanisms enhance transparency and oversight but frequently prioritize answerability over performance learning, thereby legitimizing inefficiencies rather than resolving them. By synthesizing insights from Administrative Efficiency Theory, Public Financial Management, Bureaucratic Process Theory, Administrative Capacity Theory, and Governance and Accountability perspectives, this article advances an integrative conceptual framework that explains efficiency outcomes as systemic products of interacting institutional dimensions.

The computer laboratory is an essential facility in higher education that requires efficient management of usage and environmental conditions to support the teaching and learning process. However, laboratory management at the Kalimantan Business and Technology Institute is still carried out manually, including scheduling, room condition monitoring, and report creation, which is prone to errors and time-consuming. This study aims to develop an Internet of Things (IoT)-based laboratory monitoring system prototype to improve the effectiveness of computer laboratory management. The approach used is Research and Development (R&D) with a prototype development model, allowing for design adjustments based on user feedback iteratively. Data were collected through observations, interviews, and document studies related to laboratory conditions and analyzed to determine the main system features, such as temperature and humidity monitoring, scheduling, and report generation. The results of the study show that the developed prototype can structure the laboratory workflow, provide real-time monitoring, facilitate schedule management, and simplify report preparation. This prototype is expected to serve as a foundation for developing a more comprehensive application, improving data accuracy, time efficiency, and the quality of laboratory management.

The proliferation of cloud infrastructures has intensified concerns regarding data security, integrity, identity and access management, and user privacy. Despite recent advances, existing solutions often lack comprehensive integration of privacy-preserving mechanisms, dynamic trust management, and cross-provider interoperability. This study proposes an AI-enabled, zero-trust, blockchain-fused identity management framework for secure, privacy-preserving multi-cloud environments. The framework integrates homomorphic encryption with differential privacy for aggregate-level protection and secure multi-party computation for collaborative data processing. The proposed system was validated in a simulated multi-cloud environment using CloudSim, Ethereum blockchain, and AWS EC2. Experimental results indicate homomorphic encryption latency of approximately 450ms per operation and statistically significant security improvements (t(128) = 12.47, p < 0.001), privacy (t(95) = 8.93, p < 0.001), and throughput (t(156) = 15.21, p < 0.001). The framework achieved differential privacy with ε = 0.1 while retaining 99.2% data utility, and demonstrated a 34% improvement in processing speed over conventional differential privacy approaches. In addition, the implementation was observed to be 2.3× faster than BGV-based configurations, with 45% lower memory consumption than CKKS and a 67% reduction in ciphertext size relative to baseline implementations. From an operational perspective, the framework shows a 23% reduction in security management costs, a 31% improvement in resource utilization efficiency, and an 18% decrease in compliance audit expenses. The model further indicates a 27% reduction in total cost of ownership (TCO) compared with multi-vendor security solutions, a projected return on investment (ROI) within 14 months, and an 89% reduction in security incident response costs under the evaluated conditions.

This study aims to analyze the readiness of the digital bureaucracy in transforming public services in Sorong City, focusing on the capacity and digital literacy of the State Civil Apparatus (ASN). The research uses a mixed-methods approach with a sequential explanatory design, in which quantitative data are collected through questionnaires and analyzed descriptively and correlatively, and then deepened with qualitative data through in-depth interviews. The study's results show that the Sorong City Government's digital bureaucracy is in the early stages of implementation and has not been optimal. ASN capacity and ASN digital literacy are positively related to the readiness of the digital bureaucracy, but they remain at a sufficient, fundamental level. The transformation of digital public services has increased the efficiency of certain services. However, this improvement has not been evenly distributed due to limited human resources, system integration challenges, and a bureaucratic work culture. The study's implications emphasize the importance of strengthening civil servants' capacity and digital literacy as a prerequisite for the successful transformation of digital public services. The limitation of this study is in use of correlation analysis, which does not explain causal relationships. Further research is recommended to use a more comprehensive analytical approach and expand the scope of the research area.

This study aims to analyze and design the electrical installation requirements for a prototype residential building with dimensions of 11 × 19 meters. The research applies a technical calculation approach based on lighting standards, air conditioning capacity estimation, load analysis, and electrical protection planning. Lighting requirements were determined using the lumen method by considering room area, illumination standards, coefficient of utilization, and light loss factor. Air conditioning capacity was calculated based on room area and standard cooling load values in BTU/hr. Furthermore, total electrical loads were analyzed to determine installed capacity, nominal current, Miniature Circuit Breaker (MCB) ratings, and appropriate cable cross-sectional areas with safety factors applied. The results indicate that proper load grouping, appropriate MCB selection, and correct cable sizing significantly enhance system safety and reliability. The study provides a systematic planning model for residential electrical installations that meets safety, efficiency, and technical standards, and can serve as a reference for similar residential building designs.

This study evaluates the implementation of spot-check testing for road preservation works in Manado City, specifically on the Kairagi–Mapanget segment, the Manado City–Wori boundary, and the Liwas Terminal Access Road, to identify existing problems and assess the effectiveness of engineering-based mitigation efforts. The objective is to examine construction quality compliance with technical specifications and identify operational challenges encountered on site. The methodology encompasses the measurement of asphalt layer thickness and density via a core drill, the examination of contract documents, the analysis of laboratory test results, and the execution of field observations. The findings show that most samples meet the required standards, although several locations require corrective action. Challenges arise from weather conditions, heavy traffic, equipment limitations, and the need for adaptation to updated technical regulations. Operational mitigation strategies, staff training, the use of core drill technology, and digital documentation were found to enhance accuracy and efficiency in evaluation. The research demonstrates the value of stakeholder collaboration and capacity building in supporting quality control for road preservation works. A multidimensional approach is effective in resolving technical and operational complexities in urban road projects.

The integration of Artificial Intelligence (AI) into precision agriculture has significantly improved plant disease recognition; however, many existing deep learning models remain computationally expensive and feature-redundant, limiting their deployment on low-power and edge devices. To address these limitations, this study proposes a lightweight framework for maize leaf disease recognition based on serial deep feature extraction, dimensionality reduction, and machine-learning–based classification. A pre-trained MobileNetV2 network is employed as a fixed feature extractor to obtain discriminative visual representations, while Principal Component Analysis (PCA) is applied to reduce feature dimensionality by approximately 76%, retaining 95% of the original variance and improving computational efficiency. The compressed features are subsequently classified using a Radial Basis Function Support Vector Machine (RBF-SVM), optimized via grid search and cross-validation. Experiments conducted on a four-class maize leaf disease dataset (Northern Leaf Blight, Common Rust, Gray Leaf Spot, and Healthy), with class imbalance handled during training, demonstrate that the proposed MobileNetV2–PCA–SVM pipeline achieves 97.58% accuracy, 96.60% precision, 96.59% recall, and 96.59% F1-score, outperforming the DenseNet201 + Bayesian-optimized SVM baseline (94.60%, 94.40%, 94.40%, and 94.40%, respectively). This improvement corresponds to a 2.98% accuracy gain, a 55% reduction in error rate, an 86% reduction in model parameters (20.31M to 2.75M), and an 85% reduction in model size (81 MB to 12 MB). These results indicate that the proposed framework provides a compact and efficient solution with strong potential for deployment in resource-constrained agricultural environments.

This study aims to analyze the impact of digital innovation on financial management in Purbalingga Regency, focusing on two main systems: the Regional Government Information System (SIPD) and the Village Financial System (SISKEUDES). Digital innovation is considered important to enhance transparency, efficiency, and accountability in public financial management (Lips, 2020; Agostino, Bracci, & Steccolini, 2022). The method used is a qualitative approach with a case study supported by in-depth interviews with application users and previous research (Daffa, 2024; Nadaa & Priyanti, 2023). The findings show that the use of SIPD and SISKEUDES has successfully increased transparency and efficiency in financial reporting in Purbalingga through data integration and public access to information. However, technical issues, limited human resources, and lack of inter-agency cooperation remain obstacles. Efforts by the local government, such as routine training, system updates, and digital literacy improvement, have strengthened digital-based financial governance. Overall, this study shows that the success of digital innovation in public financial management is influenced not only by technology but also by institutional readiness, a culture of transparency, and community involvement in overseeing regional financial management.

The study analyzes the implementation of the 5S method (Seiri, Seiton, Seiso, Seiketsu, Shitsuke) in improving shoe production quality at the Assembly section of PT X through observation, interviews, and documentation. Involving supervisors, production leaders, and operators, the study aimed to assess the impact of 5S on production processes. The results showed that the implementation of 5S successfully created a more organized, cleaner, and standardized work environment. These improvements led to a reduction in potential work errors, increased process efficiency, and the establishment of an employee disciplinary culture. Employees became more aware of maintaining cleanliness and orderliness, which contributed to a smoother workflow and better quality control. The findings align with theories of quality management, organizational behavior, and operations management, demonstrating the benefits of 5S in enhancing operational effectiveness. This study confirms that integrating the 5S method into industrial practices, particularly during internships, is an effective strategy to encourage continuous improvement and enhance production quality in the industry.

Vaname shrimp (Litopenaeus vannamei) has a fast growth rate, good tolerance to a wide range of salinity and temperature, and resistance to several common shrimp diseases. Vaname shrimp has an efficient feed conversion making it more economical to cultivate, and its international market demand is high. The research method used four treatments of fermented soybean meal doses, namely 0% (control), 15%, 25%, and 35%, each with 3 replications. The feed was formulated using Pearson’s Square method targeting 35% protein, and the soybean meal was fermented using tempe yeast method. The observed parameters included daily growth rate (%), absolute weight gain (grams), survival rate (%), feed utilization efficiency (FUE), and feed conversion ratio (FCR). The results showed that the treatment with 25% fermented soybean meal dose gave the best results with a daily growth rate of 6.42%, absolute weight gain of 0.22 grams, survival rate of 90.66%, and the highest feed utilization efficiency of 0.66.

The selection process for students eligible for the National Selection Based on Achievement (SNBP) requires objective and structured assessment because it involves various academic and non-academic criteria. This study aims to develop a Decision Support System (DSS) to determine the ranking of SNBP eligible students at SMAN 8 Surabaya using the Additive Ratio Assessment (ARAS) method. The ARAS method is used to evaluate student alternatives based on their report card scores for semesters 1-5, academic ability tests (TKA), academic achievements, non-academic achievements, discipline, organizational activity, and attendance through a normalization process to obtain relative Ki values. The results of the study show that the system is capable of producing objective student rankings with relative utility values (Ki) ranging from 95.15 to 89.38, where the highest value indicates the best alternative from all alternatives. The application of ARAS-based DSS can improve the efficiency, transparency, and consistency of the SNBP student selection process.

The white shrimp (Litopenaeus vannamei) is one of the popular marine shrimp species in aquaculture due to its adaptability to various environmental conditions, including a wide range of salinity, and its omnivorous feeding behavior. This study aims to determine the effect of different combinations of stocking density and salinity on the growth rate and post larval survival of vaname shrimp (Litopenaeus vannamei). The research was conducted using factorial planning based on two factors namely stocking density (3, 6, and 9ekor/L) and salinity (15 ppt and 25 ppt). Parameters measured included daily growth rate, absolute weight, survival rate, feed utilization efficiency, and feed conversion ratio (FCR). Water quality observations were also made during the study period including temperature, pH, dissolved oxygen, salinity, and total ammonia. The results showed that the combination of stocking density of 3 fish/L with salinity of 25 ppt gave the best results with a daily growth rate of 0,00664 gram and a survival rate of 86%. This treatment also produced the best feed utilization efficiency of 0.87 and the best FCR value of 1.27, indicating the most efficient use of feed. Water quality parameters during the study were within the appropriate range to support the growth of vanamei shrimp.

Gethsemane Salatiga website using Jakob Nielsen’s heuristic evaluation method combined with a triangulation approach. The evaluation involved 20 respondents, consisting of 7 UI/UX experts and 13 general users and guests, who assessed the website based on Nielsen’s ten heuristic principles, including visibility of system status, match between system and the real world, user control and freedom, consistency and standards, error prevention, recognition rather than recall, flexibility and efficiency of use, aesthetic and minimalist design, error recovery, and help and documentation. To minimize potential bias, the study integrated heuristic evaluation with user context analysis and empirical validation through usability testing, thereby providing a more comprehensive assessment. The results indicate that the overall UI/UX quality of the website is relatively good, with average scores ranging from 3 to 4 on a 5-point scale. Most identified issues fall within minor to moderate severity levels, with no critical problems affecting core functionalities. Consistency and standards received the highest average score (4.0), while alignment with user expectations and visual comfort still need improvement. Recommendations include enhancing system feedback, strengthening error prevention mechanisms, and improving help and documentation features. The differences in assessments between expert and non-expert respondents highlight the importance of professional evaluation in identifying usability issues. This study contributes to the refinement of a more reliable and contextually relevant UI/UX evaluation framework, particularly within the hospitality and tourism sector.

Tax administration reform is a strategic government agenda to improve the effectiveness, efficiency, and transparency of tax management. One effort undertaken is the implementation of an integrated, digital-based Core Tax Administration Sistem (Coretax). However, the success of Coretax implementation is determined not only by technological aspects, but also by the role of strategic management in planning, organizing, and managing organizational change. This study aims to examine the role of strategic management in supporting the successful implementation of Coretax as part of tax reform in Indonesia. The research method used is a literature review by examining various secondary sources such as scientific journals, international agency reports, government policy documents, and official publications related to tax reform and the digital transformation of the public sector. The results of the study indicate that strategic management plays a crucial role in aligning policies, human resource readiness, strengthening information technology, and managing resistance to change. These findings confirm that Coretax implementation will be more optimal if supported by comprehensive strategic planning, improved human resource competency, and an organizational culture that is adaptive to digital transformation.

Open-pit mining activities in Indonesia contribute significantly to the national economy but require stringent monitoring to mitigate environmental degradation. Conventional monitoring methods relying on terrestrial surveys are often constrained by vast coverage areas, high operational costs, and limited field accessibility. This study aims to develop an artificial intelligence model capable of automatically detecting and mapping mining areas to enhance surveillance efficiency. The applied method is Deep Semantic Segmentation utilizing the U-Net Convolutional Neural Network (CNN) architecture. The model was trained using Sentinel-2 satellite imagery, focusing exclusively on Red, Green, and Blue (RGB) spectral channels to replicate human visual perception. Experimental results demonstrate that the proposed model performs reliable segmentation of mining areas, achieving an Accuracy of 93.58% and a Global Intersection over Union (IoU) of 0.8067. These findings indicate that the U-Net architecture can effectively extract spatial features of mines even when utilizing standard visual data. This research contributes to the development of an efficient, cost-effective, and scalable digital monitoring prototype to support innovation in sustainable environmental governance.

Tobacco is a leading commodity that forms the foundation of the rural economy, but its cultivation faces challenges in the form of labour intensity, significant capital requirements, and farmers' lack of understanding of systematic cost structures. This study aims to analyse the production cost structure and evaluate the economic efficiency of tobacco farming managed by the Batu Daun Farmer Group in Batuan Village, Sumenep Regency. The research method used a qualitative descriptive approach with data collection through in-depth interviews with the head of the farmer group, field observations, and analysis of financial documents as secondary data. The analysis focused on identifying fixed and variable costs, as well as evaluating economic performance using the Break Even Point (BEP) and Revenue-Cost Ratio (R/C) indicators. The results showed that the total production cost was IDR 28,597,500 (fixed costs of IDR 3,450,000 and variable costs of IDR 25,147,500) for the production of 2,800 kg of tobacco with a gross income of IDR 70,000,000. The R/C ratio value of 2.44 (>1) indicates that the business is operating efficiently and profitably, while the BEP of 215.4 kg shows that actual production far exceeds the break-even point, meaning that the business is in an economically safe zone. The results of the study conclude that the tobacco farming business of the Batu Daun Farmer Group is economically viable and efficient.