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This study analyzes the performance of the Dual Modulus RSA algorithm in securing text data using Python. The rapid growth of digital technology has increased the risk of data security threats, making efficient and secure encryption essential. Dual Modulus RSA is a modification of the classic RSA algorithm that uses two different moduli in the encryption and decryption process, thus increasing security levels because attackers must factorize two moduli simultaneously. This research uses an experimental quantitative approach by measuring the execution time of encryption and decryption processes with variations in plaintext length (5, 10, and 15 characters). Implementation was carried out using Python 3 with the time.perf_counter() function for microsecond-precision measurement. The results show that the Dual Modulus RSA algorithm successfully encrypts and decrypts all test plaintexts correctly. Encryption time ranged from 0.0212 ms to 0.0823 ms, while decryption time ranged from 0.0422 ms to 0.0955 ms. There is a positive linear relationship between plaintext length and processing time. Decryption is consistently slower than encryption due to the larger private key exponent (d1=2753, d2=3533) compared to the public exponent (e=17). The main factors affecting performance are exponent size, dual modulus overhead, CPU caching effects, and Python interpretation overhead. This study recommends using Dual Modulus RSA with hybrid encryption for practical implementation to balance security and performance.

The role of ports as crucial nodes in the global supply chain positions the maritime sector as one of the most dynamic industries worldwide. This research analyzes the strengthening of business law support maritime sector integration to enhance port operational efficiency and the role of business law instruments in realizing the principle of fair competition within ports. This research employs a normative legal approach focusing on the study of existing legal norms and regulations governing port management and maritime integration. The strengthening of business law in the maritime sector plays a crucial role in improving port operational efficiency. A strong and well coordinated business law framework will create ports that are more efficient, competitive, and adaptive to the changing demands of the global market. Several key principles must be developed. First, rules on information disclosure and accountability to ensure transparency in port governance. Second, prohibitions against abuse of dominant positions and oversight of vertical integration to prevent anti-competitive practices. Third, clear contractual norms and governance structures for public private partnerships (PPP), including proportional risk-sharing mechanisms to ensure fairness and efficiency in infrastructure development. Fourth, multi-level governance alignment across national and regional authorities to prevent incentive distortions.

This study aims to (1) describe the marketing channels of fresh fruit bunches (FFB) of oil palm under partnership and independent (self-managed) schemes in Merlung District, Tanjung Jabung Barat Regency; (2) analyze marketing margins, farmer’s share, and marketing efficiency for each scheme; and (3) compare differences in marketing margins and farmer’s share between single-level marketing channels in both business schemes. The research was conducted in 2025 using a survey method with simple random sampling techniques. The data used consisted of primary data obtained through direct interviews with farmers and marketing institutions, as well as secondary data from relevant agencies. Data analysis was carried out using descriptive analysis, marketing margin analysis, farmer’s share, marketing efficiency analysis, and Multivariate Analysis of Variance (MANOVA). The results show that there are two types of marketing channel patterns, namely single-level and two-level channels. The single-level marketing channel has lower marketing margins, higher farmer’s share, and better marketing efficiency. In addition, there are significant differences between single-level marketing channels in both business schemes. These findings imply that shorter marketing channels are more efficient and provide a greater share of the selling price to smallholder oil palm farmers

Public space revitalization is an important strategy in urban development aimed at improving the quality of urban environments while stimulating social interaction and local economic activities. This study aims to evaluate the implementation of the Motang Rua Field revitalization program in Manggarai Regency and assess the extent to which the program achieved its intended development objectives. The study employed a qualitative descriptive approach. Data were collected through field observations, in-depth interviews with key informants including local government officials, project contractors, small business actors, and community members, as well as documentation related to the revitalization program. Data were analyzed through data reduction, data display, and conclusion drawing, supported by triangulation techniques to ensure data validity. The findings indicate that the revitalization program demonstrates a high level of relevance to community needs and aligns with the regional development vision of Ruteng Kota Molas. The implementation process shows strong coherence through effective inter-institutional coordination and consistency between planning and execution. The revitalization has also proven effective in improving public facilities, enhancing community social activities, and stimulating local economic growth through increased micro and small business activities. Furthermore, the program was implemented efficiently in terms of budget management and project timeline. The revitalization also contributes to strengthening socio-cultural values through the preservation of the historical figure Motang Rua and shows strong sustainability potential through increased public utilization of the revitalized space.

The Fréchet distribution is one of the commonly used Extreme Value Distributions (EVDs) in statistical modeling and heavy-tailed data analysis, where it plays an important role in describing product lifetimes as well as climatic and financial phenomena. The estimation of its two parameters, namely the shape parameter and the scale parameter, is traditionally based on the Maximum Likelihood Estimation (MLE) method. However, maximizing the likelihood function for this distribution involves numerical difficulties, which necessitates the use of numerical optimization methods. In this study, we propose the use of the Aquila Optimizer (AO), a recent metaheuristic algorithm inspired by the hunting behavior of eagles, as an efficient numerical tool for maximizing the likelihood function of the Fréchet distribution. The objective function was formulated as the negative log-likelihood function (-LogL), and the Aquila Optimizer was employed to obtain the optimal estimates of the distribution parameters. Several simulation experiments with different sample sizes were conducted to compare the performance of the proposed method with a conventional approach represented by the Nelder–Mead method, using the Mean Squared Error (MSE) criterion. The simulation results demonstrated that the Aquila Optimizer outperformed the Nelder–Mead algorithm in many cases, although the superiority was slight. The results also showed that both algorithms were consistent, as their MSE values decreased with increasing sample size. In addition, a practical application was carried out using real data, and the results of the survival function estimation indicated a good fit.

Prescription service wait times are an indicator of pharmacy service quality that directly impacts patient satisfaction and the Hospital Minimum Service Standards (SPM). The Kraton Regional General Hospital in Pekalongan Regency has implemented an electronic prescribing system (e-prescribing) to improve efficiency and reduce prescribing errors. However, with prescription volumes reaching hundreds per day, periodic evaluation is necessary to ensure that services remain in compliance with standards. This study aims to evaluate the waiting time for electronic prescription services among outpatients at the Pharmacy Department of RSUD Kraton, Pekalongan Regency, and to compare it with the Ministry of Health’s standards. This is a descriptive observational study using a quantitative approach. The study sample consisted of 300 prescriptions (74 compounded prescriptions and 226 non-compounded prescriptions) selected via simple random sampling from medical records for the period August–October 2025. Research instruments included an observation sheet and an interview guide to support observational data. Data analysis was conducted descriptively to calculate the average service waiting time. The results showed that the average service time for non-compounded prescriptions was 7 minutes and 42 seconds, while for compounded prescriptions it reached 28 minutes and 11 seconds. All samples examined met the established waiting time standards, namely ≤30 minutes for non-compounded prescriptions and ≤60 minutes for compounded prescriptions, in accordance with Ministry of Health Regulation No. 4 of 2019 and Ministry of Health Decree No. 129/2008. The electronic prescription service at Kraton District General Hospital in Pekalongan Regency has been operating very efficiently and meets the Minimum Service Standards (SPM). The implementation of e-prescribing technology has significantly contributed to maintaining the speed and quality of pharmaceutical services on an ongoing basis.

High dependence on the United States Dollar (USD) in international transactions has long been a challenge for economic stability in the Southeast Asian region, especially amidst global exchange rate fluctuations and geopolitical tensions. This study aims to analyze the effectiveness of Local Currency Settlement (LCS) cooperation in supporting intra-ASEAN trade stability. The main focus of this study is how local currency mechanisms can mitigate exchange rate risks and strengthen regional economic integration as part of a de-dollarization strategy. The research method used is descriptive qualitative with a literature review approach, relying on secondary data from central bank reports, ASEAN policy documents, and relevant academic literature. The results show that the implementation of the LCS framework, particularly in countries such as Indonesia, Malaysia, and Thailand, has provided more efficient transaction alternatives by reducing double conversion costs. However, its effectiveness still faces challenges such as low awareness among business actors, limited local currency liquidity compared to the USD, and the need for broader cross-border digital payment system integration. These findings imply the need for strengthened synergy between central banks in the ASEAN region and increased literacy for the private sector so that the economic stability benefits of LCS can be optimally achieved. This strategy not only strengthens monetary sovereignty but also encourages a more resilient ASEAN economic integration against external shocks.

Public service performance serves as a crucial measure of how effectively government agencies provide services to the public. Rising expectations from the public compel governments to ensure that services are delivered in an efficient, open, responsible, and responsive manner. Within the realm of public administration reform, the standard of public services has emerged as a key issue for governments looking to enhance public confidence and governance. The Indonesian government has created the Public Service Index (Indeks Pelayanan Publik/IPP) as a tool to assess the performance of public services. The IPP functions as an all-encompassing measurement system to evaluate the quality, efficiency, and responsibility associated with public service provision across government entities. This research intends to explore the definition of public service performance, the legal framework that regulates public services in Indonesia, and the metrics employed for calculating the Public Service Index. The study uses a qualitative descriptive method and a literature review approach, evaluating various pertinent books, regulations, and scholarly articles concentrating on public service management and performance evaluation. The results indicate that the Public Service Index plays a vital role in assessing service quality, institutional effectiveness, and the degree of public satisfaction. Additionally, the application of the IPP measurements fosters transparency, bolsters accountability, and encourages ongoing enhancements in public service delivery. Therefore, enhancing public service performance necessitates robust regulatory backing, institutional dedication, and the utilization of clear and quantifiable performance metrics to guarantee enduring improvements in service quality.

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.

This study aims to evaluate the growth performance of KUB chickens fed with a combination of fermented agricultural waste, specifically Maggot BSF (Hermetia illucens) and Azolla microphylla. A total of 96 KUB chickens aged 30 days were used in a Completely Randomized Design (CRD) with four treatments and four replications. The treatments consisted of a basal diet substituted with the fermented waste combination at levels of 0% (P0), 5% (P1), 10% (P2), and 15% (P3). Parameters observed included feed consumption, body weight gain (BWG), and Feed Conversion Ratio (FCR). The results showed that increasing the level of waste substitution significantly affected feed consumption due to the bulky nature of the fiber, yet body weight gain remained stable across all treatments. The FCR values ranging from 3.21 to 3.25 indicated that the high-quality protein from Maggot BSF effectively compensated for the reduced feed intake. It is concluded that the substitution of fermented agricultural waste up to 15% (P3) is an optimal and efficient formulation to maintain the growth performance of KUB chickens.

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.

Hypertension is a global health issue and major risk factor for cardiovascular disease, requiring lifelong medication. In addition to pharmacological treatment, healthy lifestyle changes and non pharmacological therapies such as warm water foot soaks, can help prevent complications by inducing vasodilation to lower blood pressure. This study aims to describe nursing care for hypertensive patients, focusing on the application of warm water foot soaks to reduce blood pressure in an inpatient setting. Methods a descriptive case study approach was used three hypertensive respondents. The intervention involved 15 minute warm water foot soaks at a temperature of 39-40°C, conducted once daily for three days. Data were collected using digital sphygmomanometers and observation sheets, with measurements taken before and after the intervention. Results, after the therapy, all respondents showed an average reduction in blood pressure, with systolic pressure decreasing by 6,6 mmHg and diastolic by 2,3 mmHg. Respondents also reported felling more relaxed and experiencing lighter sensations in their legs. Conclusion, warm water foot soak therapy is an effective, safe, and cost efficient non pharmacological intervention for hypertensive nursing care, suitable for implementation by both nurses dan families.

The transformation of public services through information technology has become one of the government’s strategies to improve service quality for the community. The implementation of e-government enables public services to be delivered more effectively, efficiently, transparently, and with easier access. This study aims to analyze the transformation of public services through the implementation of e-government in the KLAMPID New Generation application at the Department of Population and Civil Registration of Surabaya City. The study employs a qualitative approach with a Case Study paradigm. Data were collected through interviews, observations, and documentation involving government officials and the public as service users. The research analysis uses the e-government development stage model proposed by Karen V. Layne and Jungwoo Lee, which includes the stages of presence, interaction, transaction, and transformation. The results indicate that the implementation of the KLAMPID New Generation application has supported the transformation of population administration services in Surabaya City. At the presence stage, the application provides digital information on population administration services. At the interaction stage, the system enables communication between the public and service providers through features such as notifications and service status monitoring. At the transaction stage, the public can submit service requests online by uploading required documents via the application system. Meanwhile, at the transformation stage, the KLAMPID New Generation application has integrated various population administration services into a single digital platform.

This study aims to analyze the workload of sewing operators within a single production line and to equalize the workload using the line balancing method at PT. Pan Brothers. The garment industry requires high production efficiency; therefore, balanced workload distribution among operators is necessary to ensure optimal production processes. This research used an observational approach with data collection through direct observation of the sewing process, literature study, and documentation of production data. The research population consisted of 36 operators on the observed production line, analyzed through time study activities. The analyzed data included cycle time, takt time, and work processes at each workstation. The results showed that several processes had cycle times exceeding the takt time, specifically in processes number 2, 5, 6, 12, 17, 23, and 26, which caused bottlenecks and workload imbalance among operators. The calculation results indicated that the actual process time reached 520 seconds per product, while the ideal time based on takt time was 287 seconds per product, resulting in a time difference of 233 seconds or about 44% longer than the standard time. The implementation of the line balancing method was carried out by redistributing work elements from processes with high workloads to processes with remaining work capacity. The improvement results showed that the workload among operators became more balanced, the production process became more efficient, and the potential for bottlenecks could be minimized, enabling the company to achieve its production targets.

Vegetables are an important horticultural commodity in fulfilling community nutritional needs; however, their consumption level in Indonesia remains relatively low, necessitating efforts to increase production. Spinach (Amaranthus sp.) is a promising vegetable due to its short growth cycle, ease of cultivation, and high nutritional value. Limited agricultural land, particularly in urban areas, poses a major constraint to conventional cultivation, thereby encouraging the adoption of alternative technologies such as hydroponics. The hydroponic system, especially the Nutrient Film Technique (NFT), offers efficient use of land and water while enabling better control of plant growth. This review aims to identify hydroponic cultivation techniques for spinach in greenhouse conditions and to analyze factors influencing its success, including nutrient concentration, growing media, pH, temperature, and light intensity. The findings indicate that optimal nutrient concentration ranges from 410–900 ppm, supported by proper environmental and pH management to enhance vegetative growth. The implementation of the NFT system improves production efficiency, resulting in more uniform, cleaner, and higher-quality yields. Nevertheless, challenges remain, including nutrient management, environmental fluctuations, and pest and disease risks. Therefore, proper and sustainable system management is essential to ensure successful hydroponic spinach cultivation.

This study aims to design and develop a functionally efficient and operationally effective Internet of Things (IoT)-based air quality monitoring system for radiology departments. The system utilises a DHT22 sensor integrated with an ESP32 microcontroller to monitor the temperature and humidity of diagnostic rooms in real time, and to display the data via the UdaraKu mobile application. The research method employed a quantitative experimental approach focused on measuring system performance, specifically the accuracy of the temperature and humidity sensors. The research model used was the Research and Development (R&D) method, aimed at transforming conventional air quality monitoring in radiology into a real-time digital system based on IoT. The research results indicate that the IoT-based monitoring system is capable of maintaining room temperature and humidity stability within the ideal range, namely 22–24°C and 50–60% RH, in accordance with international standards. This improvement in environmental stability has a direct impact on reducing noise in digital radiography images, as evidenced by an increase in the Signal-to-Noise Ratio (SNR). Instrument validation demonstrated a high level of reliability with a Cronbach’s Alpha value of 0.848, reinforcing the reliability of the data and the system. Overall, the IoT-based air quality monitoring system has proven effective in controlling noise in digital radiography images, improving the quality of diagnostic services, and supporting patient safety principles and operational efficiency within radiology departments.

This community service activity aims to improve teenagers' self-management skills through the program “Remaja Jago: Kelola Diri Tanpa Drama”. The program was specifically designed to provide a practical and applicable understanding for youth on how to recognize their own potential, manage emotions effectively, organize time efficiently, and make wise and responsible decisions in daily life. The activity was carried out at Vihara Dhamma Bhakti Tangerang with 20 participants aged 13–20 years, representing a diverse range of educational and social backgrounds. Methods included interactive lectures, engaging educational games, structured group discussions, and guided self-reflection sessions to encourage active participation and deeper personal insight. Evaluation results showed a significant improvement in participants’ decision-making abilities, personal discipline, emotional control, and goal-setting skills after completing the program. In addition, participants demonstrated increased self-confidence and awareness of their strengths and weaknesses. Overall, this program effectively contributes to developing independent, disciplined, and responsible youth character, while also equipping them with essential life skills needed to face various personal and social challenges in the future.

The management of essential drug inventory is a strategic component of hospital pharmaceutical services because it directly influences service continuity, cost efficiency, and the overall quality of healthcare delivery. Poor inventory control can result in excessive stock accumulation, increased risk of drug expiration, inefficient budget utilization, and potential drug shortages that may compromise patient care. This study aims to evaluate the effectiveness of essential drug inventory control at Pekerja General Hospital by applying the ABC-VEN, Economic Order Quantity (EOQ), and Reorder Point (ROP) methods. It also examines differences in inventory management efficiency between 2024 and 2025 based on inventory value, cost of goods sold (COGS), and Inventory Turnover Ratio (ITOR). A mixed-methods approach with a sequential explanatory design was used. Quantitative analysis involved a paired sample t-test comparing inventory data from 2024–2025, while qualitative data were collected through in-depth interviews and analyzed thematically using NVivo. The findings reveal a significant improvement in inventory management in 2025 (p < 0.05), reflected in reduced inventory value and COGS, along with an increased ITOR. However, the implementation of ABC-VEN, EOQ, and ROP methods has not been fully integrated, and challenges such as limited human resources and procurement bureaucracy persist.In conclusion, although inventory control became more efficient in 2025, further integration of inventory methods and strengthening of human resource capacity are necessary to ensure sustainable improvements.

This study aims to design and implement an information technology-based economic and tourism potential mapping system in Tema Tana Village. This village faces several problems, including manual data management, limited access to information, and the absence of an integrated system for efficiently presenting village potential data. This study uses a descriptive approach with data collection techniques through observation, interviews, and documentation studies. In developing the system, the Waterfall method is used, consisting of the stages of planning, design, implementation, and testing. The system design was carried out using the Unified Modeling Language (UML) to ensure a clear system structure and flow. The results of the system development indicate that the system is able to present information in the form of interactive digital maps and reports that are easily accessible to users. Testing using the Black Box Testing method shows that all system features function well and are in accordance with predetermined needs. Thus, this system is expected to improve the effectiveness of data management and support better decision-making processes in developing village potential, particularly in the economic and tourism sectors.