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Micro, Small, and Medium Enterprises (MSMEs) play a vital role in driving economic growth; however, their production activities frequently face uncertainty in achieving predetermined targets. Such uncertainty arises from fluctuating market demand, delays in raw material supply, labor limitations, variations in processing time, and other technical constraints. Conventional deterministic production planning methods often fail to capture these real-world risks and variations, leading to less accurate and suboptimal decisions. Therefore, a more adaptive analytical approach that incorporates probability and uncertainty is required. This study aims to analyze the probability of achieving MSME production targets using the Monte Carlo Simulation method. This method models random production conditions by generating data based on probability distributions derived from historical records. Simulations are repeated through numerous iterations to estimate possible variations in production output and measure the likelihood of meeting targets. The results indicate that Monte Carlo simulation provides more realistic and comprehensive production forecasts compared to traditional planning approaches. By understanding both the probability of success and potential risks, MSMEs can design adaptive strategies, optimize resource allocation, manage inventory more effectively, and improve overall production planning accuracy to ensure long-term business sustainability in a dynamic environment.

Stock monitoring is a critical phase that must be performed regularly to maintain the accuracy and efficiency of inventory management. Continuous monitoring ensures that all items remain under proper oversight, thereby making stock management processes simpler, more controlled, and highly accurate. PT XYZ operates in the general contracting sector and provides a range of services, including land transportation, crude oil rental, heavy equipment and light vehicle rental, material supply, and well maintenance services. At present, stock monitoring at PT XYZ still relies on a general-purpose application designed solely for numerical calculations. This approach leads to several limitations in the current system, most notably the lack of a login feature and the requirement to recreate reports using a separate application. Based on these problems, this research aims to design a prototype of a stock monitoring information system at PT XYZ. The system is developed using the waterfall development model and documented using use case diagrams. The main output of this study is a prototype information system that allows users to change their own password, perform CRUD operations on data entities including users, items, categories, brands, units, vehicles, suppliers, incoming goods, and outgoing goods, generate various reports related to inventory/stock at PT XYZ.

This research aims to design and build a client service website and pre-registration of cases at the KOFIPINDO Law Office as a solution to administrative problems that are still carried out manually. The system is developed using the waterfall method through the stages of needs analysis, system design, coding, testing, and maintenance. The website that was built provides features for account registration, filling in case data, uploading supporting documents, and verification by the admin so that the file is ready to be submitted to the e-Court system. In addition, the system is equipped with a case status monitoring dashboard, automatic notifications to clients, and submission history that can be accessed at any time. The results of the study show that this platform is able to improve service efficiency, minimize administrative errors, and speed up the pre-registration process of cases. The implementation of this system also helps to increase the transparency of communication between law firms and clients. Furthermore, the use of this website has the potential to reduce the administrative workload of staff, optimize the management of case data, and improve the accuracy of legal documentation. The system is also designed with the client's data security and privacy aspects in mind in accordance with the principles of digital information protection. Thus, this system can be an effective, structured, and integrated digital means to support the modernization of legal services at KOFIPINDO, improve service professionalism, and strengthen the competitiveness of law offices in the digital ecosystem.

The increasing complexity and scale of modern network traffic driven by IoT and cloud-based infrastructures have made accurate intrusion detection a critical challenge. Conventional network intrusion detection systems (NIDS) and many deep learning–based approaches struggle to reliably detect minority and stealthy attacks due to severe class imbalance and limited discrimination of subtle traffic patterns. To address these limitations, this study proposes a hybrid CNN–RBF–Attention framework for network intrusion detection. The proposed model integrates three complementary components: (i) a convolutional neural network for hierarchical feature extraction from network flow data, (ii) a radial basis function (RBF) network for localized nonlinear classification using prototype-based decision regions, and (iii) an attention mechanism that adaptively weights RBF activations to emphasize discriminative traffic patterns. SMOTE is applied exclusively to the training data to mitigate class imbalance. The framework is evaluated on the widely used CICIDS2017 and CICIDS2018 benchmark datasets in both binary and multiclass settings, using recall, precision, F1-score, confusion matrices, and ROC analysis. Experimental results demonstrate that the proposed hybrid model consistently outperforms standalone CNN and RBF baselines, particularly in terms of recall and F1-score. On the CICIDS2018 dataset, the model achieves 99.81% accuracy and 99.81% F1-score in binary classification, and 99.54% accuracy and 99.54% F1-score in multiclass classification. On CICIDS2017, it achieves 98.12% accuracy and 98.12% F1-score in binary classification, and 98.92% accuracy and 98.92% F1-score in multiclass classification. Confusion matrix and ROC analyses further show strong class separability and reliable performance in low–false-positive-rate regions, which is critical for real-world IDS deployment. These results confirm that combining deep hierarchical feature learning, localized prototype-based classification, and attention-guided refinement yields a robust, operationally reliable intrusion detection framework for highly imbalanced network environments.

Manual management of sports field bookings is still widely practiced and often leads to scheduling conflicts, data recording errors, and low service efficiency. This study aimed to design and develop a desktop-based sports field booking application that automates the booking process and manages schedules in a structured manner. The research employed a system design and development method using an object-oriented programming (OOP) approach. Data were collected through direct observation of the booking process, interviews with field managers, and documentation of system requirements. The application was developed using the Python programming language with the PyQt5 framework for the graphical user interface and MySQL as the database management system. The results showed that the developed application is capable of managing field data, schedules, bookings, and user information in an integrated manner while reducing recording errors and minimizing scheduling conflicts. The application of OOP resulted in a modular, well-organized, and maintainable system structure. This application is expected to improve the efficiency and accuracy of sports field booking management and provide a practical solution for implementing a computerized booking system.

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

This study aims to analyze the role of the tally man at PT. Gelora Perkasa Belawan in supporting the effectiveness and smoothness of loading and unloading processes at Belawan Port. As the front line in cargo supervision, the tally man bears a crucial responsibility for recording the quantity, condition, and types of goods, which serve as the primary data for company reports. The research method employed is descriptive qualitative, with data collection techniques involving field observations, interviews with operational staff, and literature reviews conducted during the internship period. The findings indicate that while the tally man performs a vital function as a supervisor and data recorder, their performance is often hindered by high workloads and a limited number of personnel. The main obstacles identified include dual responsibilities, which lead to decreased data accuracy and risks of operational delays due to physical fatigue. Optimization efforts are carried out by increasing personnel during peak hours and improving accuracy in validating cargo documents. The study concludes that strengthening the tally man's role through better workload management is essential for ensuring logistical reliability and the efficient flow of goods at the port.

This paper aims to find out the process of issuing permits for checking and unloading cargo outside the customs area at the customs and excise office, this application must have a permit that must be fulfilled to be managed by the agent, because the implementation of checking and unloading cargo outside the designated area without obtaining a permit can be subject to criminal sanctions as regulated in Article 102 letter (b) of Law Number 17 of 2006 concerning Customs Due to port congestion, ships can dock outside the customs area (out custom area) by fulfilling certain requirements and following the procedures set by the Customs and Excise authorities. in managing permits, agents often encounter obstacles or constraints when issuing permits that are detrimental to certain parties. Therefore, the responsible party of PT. Bahari Eka Nusantara Belawan branch must coordinate and have accurate accuracy in the process of issuing this permit. The role of PT. Bahari Eka Nusantara Belawan branch is very important for parties who participate in the process of managing permits outside this area. This permit is crucial to avoid criminal charges against those responsible for checking and unloading outside the customs area. It can be concluded that there are still obstacles or errors in data input that can cause delays in approval of permits for checking and unloading outside the customs area.

This study aims to evaluate the implementation of Sidoarjo Regent Regulation Number 69 of 2017 concerning the Civil Servant Code of Ethics in the Family Planning Sector at the Sidoarjo Regency Women's Empowerment, Child Protection, and Family Planning Office. A qualitative approach with a case study was used to explore the implementation of the policy through interviews, observation, and documentation. The analysis was conducted using William Dunn's six policy evaluation indicators, namely effectiveness, efficiency, adequacy, equity, responsiveness, and accuracy. The results show that the policy has provided clear behavioral guidelines and is applied evenly in the work environment. However, the effectiveness and efficiency of implementation are not optimal due to disciplinary violations, weak supervision, and unstructured communication between superiors. The aspects of adequacy and accuracy are considered relevant to the needs of the organization, but have not been able to fully overcome obstacles such as high workloads and low internalization of ethical values ​​among employees. In general, this policy contributes to shaping the professionalism of civil servants, but still requires strengthening through continuous supervision and more systematic coaching. The originality of the study lies in the use of Dunn's evaluation model in the context of the implementation of the civil servant code of ethics at the regional level, as well as identifying gaps between normative policies and field practices. These findings confirm that the success of a code of ethics depends heavily on organizational communication, work culture, and consistency of oversight.

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

Stock price prediction remains a complex challenge due to the dynamic and non-linear nature of financial markets, especially for banking stocks like PT Bank Negara Indonesia (Persero) Tbk (BBNI). This study aims to optimize BBNI stock price forecasting by integrating an automated Extract, Transform, Load (ETL) pipeline with the Long Short-Term Memory (LSTM) algorithm within a data engineering framework. Historical data from 2019 to 2025 were processed through a structured ETL sequence—including data cleaning, feature engineering, and MinMaxScaler normalization—to ensure high data quality. The dataset was partitioned into 80% for model training and 20% for testing to ensure rigorous evaluation. The results demonstrate that the systematic ETL approach significantly enhances model stability and predictive accuracy compared to conventional methods. The LSTM model effectively captured long-term temporal dependencies, providing reliable trend forecasts with an impressive test accuracy, achieving a Root Mean Squared Error (RMSE) of 0.0354. This research underscores that integrating robust data engineering practices with deep learning is essential for building resilient financial decision-support systems.

Savings and Loan Cooperatives (KSP) play a vital role in expanding community access to capital, especially within the informal sector. Nevertheless, non-performing loans remain a persistent challenge that can threaten liquidity and long-term institutional sustainability. KSP CU Mera Ndi Ate faces similar issues, which are assumed to stem not only from administrative weaknesses but also from members’ perceptions and behavioral factors. This research aims to examine the potential causes of non-performing loans through text-based sentiment analysis using an unsupervised learning approach. A quantitative method with a data mining framework was applied. Data were gathered through interviews, observations, documentation, and 200 customer opinion texts processed using the Orange Data Mining application. The analytical stages included preprocessing, corpus development, feature extraction, sentiment clustering, and visualization. Because the dataset lacked predefined labels, unsupervised learning was used to identify naturally emerging sentiment patterns. Findings reveal a predominance of critical sentiments related to credit assessment procedures and service quality. The highest sentiment score (75) concerned insufficient creditworthiness evaluation, followed by concerns about service efficiency (66.6667). These insights suggest that improving assessment accuracy and service quality may help reduce non-performing loans.

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

This research presents the development of a transaction data protection mechanism for a bouquet sales application by utilizing the Advanced Encryption Standard (AES) algorithm. The rapid growth of digital commerce has led to an increase in online transactions, which in turn raises serious concerns regarding the security of sensitive transaction data. Information such as customer identities, order details, delivery addresses, and payment data are vulnerable to unauthorized access, data leakage, and manipulation if not properly secured. To address these issues, this study applies the AES-128 encryption algorithm using a 128-bit secret key to secure transaction data before it is stored in the system database. The encryption process follows the standard AES workflow, including key expansion, initial transformation, multiple encryption rounds, and a final transformation stage. Decryption is restricted exclusively to authorized users who possess the correct encryption key. The research methodology includes system analysis, AES integration into the application, and functional testing of the encryption and decryption processes. Data integrity is validated by comparing the original plaintext with the decrypted output, while system performance is evaluated based on processing time and decryption accuracy. Experimental results indicate that the average encryption and decryption time remains under 10 milliseconds per transaction, without affecting system performance. The findings confirm that AES-128 effectively enhances transaction data confidentiality and integrity in the bouquet sales application

The attendance process at SMK Swasta Dwitunggal 2 Tanjung Morawa is still carried out manually, potentially causing inefficiency, delays, and inaccuracies in attendance data. This condition results in the difficulty of managing attendance data and less than optimal support for managerial decision-making in the school environment. This study aims to design an Enterprise Architecture (EA) for a school attendance system using the TOGAF Architecture Development Method (ADM) framework. The research method used is a qualitative approach with a case study design. The analysis is carried out through mapping existing conditions (AS-IS) and designing expected conditions (TO-BE) in several TOGAF ADM phases, including business architecture, information system architecture, technology architecture, as well as the Opportunities and Solutions and Migration Planning phases. The results of the study are an enterprise architecture blueprint and a migration roadmap that can serve as guidelines for the gradual implementation of a technology based attendance system. This design is expected to improve the efficiency of attendance management, data accuracy, and support the decision-making process in schools.

Learning concentration is an important factor in supporting students’ academic success, particularly in Biology study programs that require high levels of accuracy and analytical skills. Learning concentration is influenced by various factors, including sleep quality and dietary patterns. This study aimed to analyze the relationship between sleep quality and dietary patterns and the level of learning concentration among Biology students of class T.BIO1/2022 at Universitas Islam Negeri Sumatera Utara. This study employed an analytic observational approach with a cross sectional design. The research sample consisted of 23 students selected using a total sampling technique. The research instruments included questionnaires on sleep quality, dietary patterns, and learning concentration. Data analysis was conducted descriptively and using cross tabulation. The results showed that students with good sleep quality and regular dietary patterns tended to have higher levels of learning concentration compared to those with poor sleep quality and irregular dietary patterns. Therefore, sleep quality and dietary patterns are important factors that need to be considered to support students’ learning concentration.  

The Cultural Advancement Facilitation through Pinhole Photography as an Educational Medium for Children program aims to enhance cultural literacy, creativity, and awareness among young generations regarding the preservation of Malay Deli cultural heritage. This initiative is motivated by the declining interest of children in local history and culture amid rapid modernization, particularly in coastal areas with limited access to educational resources. Pinhole photography is employed as an educational approach due to its simplicity and reflective nature, fostering patience, accuracy, and a fundamental understanding of the interconnection between art, science, and culture. The program was conducted over three days through a series of activities, including workshops on constructing pinhole cameras, field exploration and photography at Malay Deli heritage sites namely Maimun Palace, Al- Mashun Grand Mosque, Al-Osmani Grand Mosque, and Sri Deli Park and photo developing and curation sessions. The activities actively involved children from the Rumah Edukasi Anak Pesisir community as primary participants in the visual documentation of cultural heritage. The results indicate an improvement in participants’ understanding of Malay Deli historical and cultural values, the development of creative and reflective thinking skills, and the production of children’s photographic works with potential use as educational and cultural promotion media. Overall, this program contributes to participatory-based cultural advancement and strengthens sustainable cultural tourism through an educational, creative, and sustainable approach.

This study aims to analyze the transformative impact of digitalization and technology in the manufacturing sector on improving operational efficiency, particularly in budgeting and resource utilization, as well as to identify the main barriers to technology adoption. Using a Literature Review and Case Study Analysis of secondary data (journals, company reports, and industry publications), it was found that digitalization and Automation supported by Artificial Intelligence (AI) fundamentally transform budgeting functions. This transformation has been shown to improve budget accuracy by up to 50% (reducing human errors) and process efficiency by up to 25%, turning budgets from static documents into adaptive and predictive control tools. Positive impacts are also observed in operations through increased production capacity (revenue surge) and the implementation of Predictive Maintenance, which reduces expenditure and asset downtime, in line with the principles Cost Efficiency and Lean Manufacturing. Nevertheless, the adoption of advanced technology faces significant obstacles, namely high initial capital investment and skill gaps among the workforce. It is concluded that the success of digitalization heavily depends on strategic budget planning to overcome capital barriers and adequate allocation of funds for Human Resource (HR) training to support effective collaboration between humans and machines.

This study examines the effectiveness of problem-solving skills in enhancing decision-making processes among teachers at State Junior High Schools in Jambi Province. The research was motivated by the increasing demands placed on educators to make timely, accurate, and contextually appropriate decisions in academic, administrative, and student-related matters. A quantitative approach was employed using a survey method, involving teachers from several public junior high schools across the province. Data were collected through validated questionnaires measuring levels of problem-solving competence and decision-making quality. The results indicate a significant positive relationship between problem-solving skills and decision-making effectiveness. Teachers with higher levels of analytical thinking, alternative evaluation, and solution implementation were found to make decisions more systematically, responsively, and with greater accuracy. Moreover, the findings reveal that problem-solving skills contribute not only to improving daily pedagogical decisions but also to enhancing school governance and conflict resolution. This research highlights the importance of continuous professional development programs aimed at strengthening teachers’ cognitive and strategic abilities. The study concludes that integrating structured problem-solving training into teacher development initiatives can substantially improve decision-making quality in junior high schools, ultimately supporting better educational outcomes in Jambi Province.

Tajwid learning in rural mosque communities (DKM) is generally conventional, unstructured, and has not optimally utilized community potential, resulting in low quality of Qur’anic recitation (tahsin). This community service aims to improve the quality of santri’s Qur’anic recitation at DKM Al-Islah, Pangadegan Village, Pagelaran District, Cianjur Regency, through learning the Matan Tuhfatul Athfal using an Asset-Based Community Development (ABCD) approach. The method applied the ABCD framework through the stages of discovery, design, delivery/action, and reflection and sustainability, involving local ustadz, DKM administrators, and santri as key participants. The results indicate that mapping and utilizing local assets successfully established a more structured, participatory, and sustainable tajwid learning system. The integration of Matan Tuhfatul Athfal with peer learning increased active participation, self-confidence, and accuracy of Qur’anic recitation among santri. Furthermore, the program strengthened the institutional capacity of the DKM and fostered a strong sense of community ownership of the learning process. This study concludes that integrating classical matan-based learning with a community empowerment approach is an effective strategy for sustainably improving the quality of Qur’anic recitation in rural DKM settings.