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Information technology (IT) governance plays a strategic role in supporting the achievement of organizational goals, especially in higher education institutions. Adiwangsa University Jambi, as a private university, is required to manage IT effectively, efficiently, and in line with the institution's vision. This study aims to analyze the level of IT governance capabilities at Adiwangsa Jambi University using the COBIT 2019 framework with a focus on the EDM (Evaluate, Direct, Monitor) domain. The research method used is a descriptive quantitative method through data collection using questionnaires, interviews, and documentation studies. The results show that the level of IT governance capability in the EDM domain is at level 2 (Managed Process), indicating that the IT governance process is running but still needs improvement in several aspects, especially in monitoring and controlling IT performance. This study is expected to provide recommendations for Adiwangsa University Jambi in improving IT governance in a sustainable manner.

Magnetic resonance imaging (MRI) provides rich anatomical contrast for brain tumor assessment, yet routine interpretation remains time-intensive and demands high precision. This work develops a pipeline for four-class brain MRI image classification (glioma, meningioma, pituitary tumor, and no tumor) by combining automated brain-region cropping, data augmentation, and transfer learning with EfficientNetB1. Experimental results demonstrate exceptional performance, achieving an overall accuracy of 0.99 (99%) on the test set. Specifically, the model reached an F1-score of 1.00 for the no tumor class, 0.99 for pituitary, and 0.98 for both glioma and meningioma classes. Beyond reporting numerical performance, the study utilizes Grad-CAM heatmaps to verify that predictions rely on clinically plausible regions rather than spurious background cues. These results indicate that an efficiency-oriented backbone, paired with systematic preprocessing, can achieve reliable and interpretable performance for brain tumor classification tasks.

The implementation of the Electronic-Based Government System (SPBE) is essential for achieving efficient, transparent, and accountable village governance. Sido Rukun Village in Merangin Regency, Jambi Province, has begun using several government applications but lacks a structured enterprise architecture aligned with the national SPBE framework. This study aims to develop an enterprise architecture for SPBE in the business process domain at Sido Rukun Village. The research employs the TOGAF ADM (The Open Group Architecture Framework – Architecture Development Method) approach, involving stages such as identifying current business processes, designing a target architecture, and conducting a gap analysis between the as-is and to-be states. The findings include a business process architecture blueprint compliant with Presidential Regulation No. 95 of 2018 and Presidential Regulation No. 132 of 2022 on the National SPBE Architecture. This blueprint encompasses BPMN-based business process models and supporting artifacts that serve as a foundation for integrated information systems at the village level. The study’s implications are significant: it provides Sido Rukun Village with a practical and standardized technical blueprint for implementing a sustainable electronic-based government system, thereby supporting its transformation toward a Smart Village capable of adapting to evolving information and communication technology trends.

This study analyzes creditworthiness assessment and predicts non-performing loan (NPL) risk using the Naïve Bayes algorithm at BPR Ukabima Lestari, Jambi Branch. A quantitative data mining approach with probabilistic classification is applied. The dataset includes borrower attributes such as age, occupation, income, loan amount, tenor, collateral, and repayment history. Research stages comprise data preprocessing, model development, and performance evaluation using accuracy, precision, recall, and F1-score implemented in RapidMiner. The results indicate that the Naïve Bayes model achieves 99.58% accuracy, demonstrating strong capability to predict potential problem loans accurately and efficiently, supporting data-driven credit decisions and strengthening credit risk management in microbanking institutions.

This study aims to classify customer payment methods at 17 Coffee & Eatery using machine learning algorithms, namely Naïve Bayes and Support Vector Machine (SVM). The increasing use of digital and non-cash payments has generated large volumes of transaction data that are rarely analyzed optimally, even though such data contain valuable information for business decision making. This research used secondary transaction data collected from January to March 2025, consisting of 10,147 transaction records. The dataset included several attributes such as order time, payment time, transaction type, total sales, number of items, and payment method. Data preprocessing was performed through data cleaning, feature engineering, normalization, and label encoding before being divided into training and testing sets with an 80:20 ratio. The Naïve Bayes and SVM models were then trained and evaluated using accuracy, precision, recall, F1-score, and ROC–AUC metrics. The results show that both algorithms were able to classify payment methods effectively, but SVM achieved higher accuracy and more stable performance than Naïve Bayes. These findings indicate that SVM is more suitable for handling complex and heterogeneous transaction patterns. The implementation of machine learning for transaction classification can support more efficient financial management and data-driven decision making for small and medium enterprises in the culinary sector.

Traffic congestion is a persistent challenge in urban areas in Indonesia, where increasing vehicle density creates the need for intelligent traffic monitoring systems. This study aims to develop a real-time vehicle parking system using the YOLOv8 object detection model to provide efficient traffic analysis from live CCTV broadcasts and recorded videos. This study uses a quantitative experimental approach with the implementation of the YOLOv8m model using the Ultralytics library in Python, tested on data collected from CCTV cameras A TCS Dishub Medan and additional footage from mobile devices. Vehicles are detected and counted in two directions up (Up) and down (Down) using virtual detection lines on the video frame. The system performance is evaluated by automatic detection counting with manually recorded ground truth data. The results show that on live CCTV broadcasts, the YOLOv8m model achieves an average precision of 98.96%, a recall of 96.59%, and an F1 score of 97.74% for upstream traffic, while for downstream traffic it achieves 100% precision, 95.64% recall, and an F1 score of 97.730/0. On the other hand, on high-quality recorded videos, all performance metrics achieve 100%, indicating perfect detection accuracy. These findings confirm the effectiveness of YOLOv8 in real-time traffic monitoring, but also indicate that video quality and stream stability affect detection performance. In conclusion, the developed system shows strong potential to support smart city traffic management solutions. Future research should focus on performance optimization under low-resolution live streaming conditions to improve accuracy in practical applications.  

This research aims to design and implement a village MSME website as a centralized digital promotional medium to overcome the limitations of conventional marketing and expand the market reach of local products more effectively and sustainably. The system was developed using the waterfall method, encompassing requirements analysis, design, implementation, testing, and maintenance. The system was developed using the PHP programming language and the CodeIgniter framework based on the Model-View-Controller (MVC) architecture to ensure a structured, efficient, and maintainable development process. The implementation resulted in a responsive and user-friendly website equipped with key features such as an informative product catalog, village MSME profiles, and a content management system via an admin dashboard that allows MSMEs to update data independently and flexibly. Functional testing demonstrated that all features functioned well and reliably according to user needs. Therefore, this village MSME website can be concluded as an effective digital solution for increasing the visibility of local products, strengthening MSME competitiveness, and supporting village economic growth through sustainable and integrated online promotion.

The principle of comparative advantage explains that every country or society, like individuals, can gain benefits from their trade activities by exporting goods or services in which they have a major comparative advantage and importing goods or services in which they do not. Based on the law of comparative advantage, even though a country may be less efficient (having an absolute disadvantage) compared to other countries in the production process, the structure of industrial performance can be seen through the analysis of industrial sector behavior analyzed through various strategies such as Price, Product, and promotion. The theory of comparative advantage related to the exchange of goods is relevant as long as the traded goods are still useful. In other words, Performance is defined as the result of activities influenced by the structure and behavior within the industrial sector, where these results are often measured by the size of a company's market share or profitability in an industry. In more detail, performance can also be reflected in the form of efficiency, development (including market expansion), job creation, employee welfare, and a sense of group pride.

Micro, Small, and Medium Enterprises (MSMEs) are the backbone of regional economies, including in the city of Banjarmasin, as they play a crucial role in job creation and income generation for local communities. However, many MSMEs continue to face challenges in the marketing aspect, particularly due to their reliance on conventional marketing methods that have limited reach and require relatively high costs. In response to these issues, this community service program aims to promote the digitalization of MSME marketing methods in Banjarmasin through intensive training and mentoring in the utilization of social media marketing. The implementation methods include an initial needs assessment survey to identify partners’ levels of understanding, workshops on digital marketing strategies, and hands-on mentoring for direct implementation on social media platforms such as Instagram and Facebook. The results of the program indicate a significant improvement in MSME actors’ knowledge and skills, particularly in managing business accounts, developing content strategies, and creating visually appealing content that aligns with target market characteristics. Furthermore, MSME partners were able to expand their market reach more effectively and efficiently, which is expected to enhance business competitiveness and sustainability in the digital era.

The production sector at both the district and city levels is crucial for fostering structural change and boosting economic growth in specific areas. Still, many regions struggle with issues such as linking supply chains, readiness for technology, quality of labor, and efficient policies. This research intends to examine the strategies of the manufacturing sector at the district and city levels to enhance regional competitiveness and promote sustainable economic growth. The study utilizes a descriptive qualitative method based on a review of literature from academic journals, policy papers, and official statistics related to manufacturing progress. Results reveal that several important factors strongly affect regional manufacturing growth. These include the connection of local supply chains, industry strategies focused on the market, the implementation of digital and smart manufacturing methods, innovation encouraged by educational institutions and organizations, and the influence of local governments in developing an effective industrial policy atmosphere. Furthermore, creating designated industrial areas and managing operations efficiently significantly helps attract investments and boost the manufacturing output of regions. The research concludes that a cohesive and tailored manufacturing strategy for each region is vital for improving local productivity, generating jobs, and enhancing economic stability at both district and city scales.

The Community Service (PkM) activity aimed to enhance the capacity of civil society to conduct effective public oversight of the East Lombok Regency Regional Revenue and Expenditure Budget (APBD) for fiscal year 2025. The “Budget School” program, organized by the Pimpinan Daerah Pemuda Muhammadiyah in collaboration with the Indonesian Forum for Budget Transparency (FITRA) NTB and ITSKes Muhammadiyah Selong, provided participants with a comprehensive and critical analysis of the regional budget structure and allocation patterns. The key findings highlighted notable fiscal inefficiencies, particularly the disproportionately high allocation for Employee Spending (Belanja Pegawai), which indicates an urgent need for budget reallocation toward increasing Capital Expenditure (Belanja Modal). Such realignment is essential to accelerate infrastructure development, enhance public service delivery, and ensure broader socio-economic benefits for the community. The event, conducted on September 25, 2025, successfully improved fiscal literacy among youth and civil society actors by strengthening their understanding of fiscal governance and legal oversight mechanisms. Overall, the activity fostered collective awareness and encouraged active participation in promoting sustainable, transparent, and efficient regional financial management.

The rapid development of the Internet of Vehicles (IoV) has introduced new security challenges, particularly in protecting Controller Area Network (CAN Bus) communications from cyberattacks such as Denial of Service (DoS) and spoofing attacks. This study proposes the implementation of the Extreme Gradient Boosting (XGBoost) algorithm combined with Information Gain feature selection to improve intrusion detection performance in IoV environments. The CICIoV2024 dataset, which represents both benign and malicious traffic, is used as the primary data source. The research process includes data integration, preprocessing, feature selection, data splitting, and model training using a 5-fold cross-validation approach. Experimental results demonstrate that the proposed model achieves outstanding performance, with accuracy, precision, recall, and F1-score exceeding 99.99%, and an Area Under Curve (AUC) value approaching 1.00. Furthermore, Information Gain successfully identifies the most influential CAN payload features, enhancing model efficiency without sacrificing accuracy. These findings confirm that the combination of Information Gain and XGBoost is highly effective for developing a fast, accurate, and efficient intrusion detection system in IoV networks.

Public complaint services are an essential part of public service delivery in supporting the government’s rapid response to various social issues and emergency situations. In West Tanjung Jabung Regency, public complaint services are provided through the HALO USTAD 112 Call Center managed by the Department of Communication and Informatics. However, the existing service still faces several limitations, including the lack of optimal integration in complaint data management, inadequate documentation of reports based on regional classifications, and limited capabilities in storing and retrieving complaint data. This study aims to optimize the HALO USTAD 112 Call Center service through the design of a mobile-based public complaint information system, so that the processes of receiving, managing, and monitoring reports can be carried out more effectively and in a structured manner. The system development applies the Waterfall method, which consists of requirement analysis, system design, implementation, and testing stages. The designed information system includes key features such as user and admin login, complaint submission, report management and verification, report monitoring, statistical visualization of complaint data, and regional-based report recapitulation. The application is developed using the Flutter framework with the Dart programming language, while Supabase is utilized as the backend integrated with a PostgreSQL database. The results of this study are in the form of a system design and prototype that are expected to improve the quality of public complaint services and support more accurate, integrated, and efficient data management.

Sweet corn is a horticultural commodity that is widely consumed in Indonesia. This study evaluated the effectiveness of NPK 18-18-18 fertilizer on the vegetative growth, yield components, and relative agronomic effectiveness (RAE) of sweet corn (Zea mays saccharata). Field experiments were conducted using fertilizer dosages of 0,50, 0,75, 1,00, and 1,50 NPK, with a control treatment for comparison. The application of NPK 18-18-18 significantly increased plant height, stem diameter, leaf number, ear length, ear diameter, biomass weight, ear weight with husk, ear weight without husk, plot yield, and overall productivity compared with the control. Mean values across treatments ranged from 68,94–205,72 cm for plant height, 7,41–20,47 mm for stem diameter, 6,01–13,00 leaves per plant, 15,41–20,89 cm for ear length, and 36,05–49,65 mm for ear diameter. Biomass weight ranged from 0,12–0,34 kg, ear weight with husk from 0,13–0,34 kg, and ear weight without husk from 0,12–0,28 kg. Plot yield varied between 7,91–25,46 kg, corresponding to productivity levels of 5,02–16,16 t/ha. RAE analysis indicated that fertilizer application was effective at dosages of 0,75, 1,00, and 1,50 NPK, with the highest effectiveness observed at 1,50 NPK (118%). Notably, the 0,75 NPK dosage achieved an RAE value of 101%, demonstrating that lower fertilizer input can enhance yield while reducing production costs and mitigating fertilizer scarcity. These findings suggest that NPK 18-18-18 fertilizer, when applied at an optimal dosage, can be effectively utilized in sweet corn cultivation to improve growth and productivity while ensuring efficient nutrient management.

This study was conducted to analyze the implementation of financial management and accounting practices at a Coffee Shop in South Jakarta operating within the MSME sector, with a focus on the importance of structured financial management and the application of standardized accounting as a fundamental element in supporting business sustainability. The research employed a qualitative descriptive method through interviews, direct observations, and document review. The findings indicate that the Coffee Shop has applied a cash-based recording system and utilizes a simple point of sales (POS) application to monitor cash flow and sales transactions. However, the recording of fixed assets, depreciation calculations, and the recognition of liabilities have not fully complied with the guidelines of FAS MSMEs, resulting in financial information that remains limited, lacks transparency, and does not provide a comprehensive overview of the business’s financial condition. Therefore, this study highlights the need to improve human resource capabilities in accounting and to implement simple yet credible accounting software to support more accurate, efficient, and accountable financial reporting. With more professional financial management, business owners will be better equipped to make strategic decisions, strengthen stakeholder trust, and enhance the competitiveness and long-term sustainability of the Coffee Shop.

Each country has different holiday policies, but the number of holidays in Indonesia is quite large, which impacts uncertainty for investors when buying or selling shares. These events can cause market anomalies or irregular market conditions and produce abnormal returns at certain times, known as the holiday effect. This study uses a quantitative descriptive method with an event study approach, data collection is carried out using documentation and literature methods. The data used are secondary data in the form of the Jakarta Composite Index (JCI), the LQ45 Index, and the Jakarta Islamic Index (JII) from the official website of the Indonesia Stock Exchange (IDX). Exchange rate data is taken from the official website of Bank Indonesia. The population of this study is every company listed on the IDX, while the data used are JCI, LQ45, and JII data 6 days before and 6 days after the Eid al-Fitr holiday and regular trading days from 2011-2025. The results of the study show that there is no significant difference in the JCI, LQ45 Index, or JII before and after the Eid al-Fitr holiday, so there is no holiday effect. These results indicate that all three indices reflect a market that tends to be efficient and stable in responding to seasonal events. Furthermore, the Rupiah exchange rate had a negative but significant effect on the Jakarta Composite Index (JCI). The Rupiah exchange rate had a negative but insignificant effect on the JII before and after the Eid al-Fitr holiday. The Rupiah exchange rate had a positive but insignificant effect on the LQ45 Index before and after the Eid al-Fitr holiday.

This study discusses the introduction of digital signature patterns using the Backpropagation method on Artificial Neural Network (JST) to identify a person's characteristics and potential. The increasing use of digital identities demands a verification system that is more secure, accurate, and adaptive to the variations of each individual's signature. The main problem faced in the signature recognition system is the low level of accuracy when the visual features of the signature have similarities between users, both in terms of shape, size, and stroke pressure. In addition, variations of signatures made by the same individual are also a challenge in the identification process. As a solution, this study implements Principal Component Analysis (PCA) to extract important features from the signature image before the training process using JST. PCA is used to reduce the data dimension so that the learning process becomes more efficient and optimal. A total of 80 signature images were used in this study, consisting of 60 training data and 20 test data. The results showed that the system was able to achieve an accuracy level of 92.5%. These findings prove that the combination of PCA and JST methods is effective in recognizing digital signature patterns and has the potential to be applied to digital security-based biometric identification systems.

The evolution of modern civil procedural law demands dispute resolution mechanisms that are responsive, efficient, and substantively just. In this context, Alternative Dispute Resolution (ADR) particularly mediation and arbitration has transformed from a supplementary option into a central instrument within Indonesia’s dispute resolution architecture. This study aims to analyze the development of ADR from the perspective of modern civil procedural law, focusing on the normative status and practical efficiency of mediation and arbitration as pre-litigation mechanisms. The research employs a normative-juridical approach, analyzing primary legal sources (legislation, Supreme Court Regulations [PERMA], court decisions) and secondary sources (scholarly journals, books, policy documents). The findings indicate that mediation has been mandatorily integrated into civil procedure through PERMA No. 1 of 2016, functioning as a court-facilitated pre-litigation stage, while arbitration operates as a consensual out-of-court mechanism under Law No. 30 of 1999. Both mechanisms demonstrate clear efficiency in terms of time (resolution within weeks to months), cost (minimal to predictable), and the ability to preserve parties’ relationships through collaborative processes and procedural confidentiality. Nevertheless, implementation challenges remain, particularly concerning the availability of qualified mediators and public perception of ADR. The study concludes that ADR is no longer a marginal alternative but an integral pillar of modern civil justice, aligning with principles of procedural efficiency and substantive justice.

This research is purposely conducted to evaluate and compare the effectiveness, efficiency, applicability  and appropriateness of modern firefighting systems installed on board ships. As a result, the review attempt to  ascertain the significance of firefighting systems on ships in terms of their safety, types, structure, principles as  well as installations. In this way, the review tries to justify the importance of these systems on board ships. The design of this survey revolves around three basic elements that incorporate the complete firefighting  procedure. These components include: modern technology of firefighting, different types of systems that are  used on board ships and people traits in terms of drilling, firefighting training to deal with any incident of fire.  In the previous decade, the globe has experienced massive destructions, harms and injuries resulting from fire incidents on ships. This has triggered and ignited enhanced fabrication and staging of new practices,  technologies and inventions aimed at efficiently deal with fire incidents so as to support the safekeeping and  reliability on these fire systems on board ships. Equally, through this technological enhancement, the  firefighting systems have been designed to facilitate rapid detection capable of differentiating between real  smokes or flames indicators in case of a fire event on board ship. In recent times, these technological  developments have been viewed as strategies that help ship owners to ensure better protection of the crew’s life,  ship’s inventories as well as minimising on the possible losses that occur as a result of fire events on board ship.  Therefore, with the review of the recent or latest methods and technologies for speedy fire detection on ships,  the review demonstrates enhancement attributes, features and qualities of these new systems. Additionally, the  paper critically evaluates the components of these firefighting systems, as well as looking at their competences,  capabilities, benefits and applicability on board ships.

Manual quality assessment of Platelet Concentrate (TC) is highly subjective and inconsistent, necessitating an objective, automated classification system. This study aims to develop a computationally efficient, low-cost model for TC quality classification using Histogram Features extracted from grayscale images combined with the K-Nearest Neighbor (KNN) algorithm. The methodology employed critical preprocessing steps, including StandardScaler for normalization and SMOTE for balancing the training data, followed by optimization across K=1 to K=30. The optimal model achieved a maximum accuracy of 69.23% at K=6, with an F1-Score of 71.43%, confirming robust performance on the imbalanced testing set. The results validate the effectiveness of the Histogram-KNN approach as a consistent and reliable decision support system for rapid TC quality screening in resource-limited settings.