Digital transformation is key in business processes, including for Small and Medium Enterprises (SMEs). With digital transformation, a company can change the way it serves its customers, but this implementation carries high risks due to limited resources and management changes. SMEs often face obstacles in adopting new technologies, such as financial constraints, lack of technical skills, and barriers in technology implementation and human resources. Good IT governance is necessary for SMEs to survive technological developments. In general, business processes that already run are still done conventionally, thus governance needs to be implemented. From these problems, a strategic plan is needed that can produce a blueprint framework which is needed by the company. The findings of this study consist of TOGAF ADM-based architectural artifact documents, which serve as a foundation for identifying technology mappings that can be developed in alignment with the existing business processes of small and medium-sized enterprises (SMEs). A blueprint framework can be used by SMEs to determine the priorities and stages of system development over the next few years to be carried out, of course, by considering internal and external factors. From the results of the framework, the development of information systems for marketing and selling SME products is carried out to overcome problems such as the lack of knowledge of partners in marketing so that it has an impact on partner income. With structured IT governance, SMEs can align IT strategies with business goals and develop information systems that support business continuity in the future.

Bali’s cultural richness includes the Ogoh-Ogoh tradition, which has recently evolved from a ritualistic practice into a competitive event evaluated through multidimensional criteria. However, the current manual evaluation process faces significant challenges, including subjective bias, inconsistent scoring standards, and inefficient data processing, which often reduce public trust in competition results. This study proposes a web-based Decision Support System (DSS) integrating the Simple Multi-Attribute Rating Technique (SMART) and Simple Additive Weighting (SAW) methods to enhance objectivity and transparency in Ogoh-Ogoh assessment. The system was implemented and tested using a dataset of 45 Ogoh-Ogoh alternatives. The comparative analysis demonstrates a strong consistency between the two methods, evidenced by a Spearman’s Rank Correlation coefficient of 0.81. Furthermore, performance testing revealed that the system achieved an 85% ranking accuracy compared to expert manual evaluations and significantly improved operational efficiency by reducing processing time by 40% (from 2.5 hours to 1.5 hours). These findings confirm that the proposed DSS not only minimizes subjectivity but also serves as a valid tool for the digitalization and preservation of cultural heritage assets.

The telecommunications industry has evolved significantly, with advancements from 1G to 4G and now towards 5G, promising enhanced data speeds and connectivity. Orthogonal Frequency Division Multiplexing (OFDM) is crucial in this transition due to its efficient spectrum utilization and ability to handle frequency-selective fading. However, OFDM is susceptible to Carrier Frequency Offset (CFO), leading to Inter-Carrier Interference (ICI) and degraded performance. This research investigates the impact of CFO on conventional OFDM systems and proposes mitigation techniques using Improved Sinc-power (ISP) pulse shaping and Convolutional Channel Coding. MATLAB simulations were conducted to analyze CFO-induced ICI in a standard OFDM system, followed by performance comparison with ISP-OFDM and ISP-OFDM combined with Convolutional Coding. The results demonstrate that CFO significantly increases ICI, causing a higher Bit Error Rate (BER). The application of ISP pulse shaping reduces the side-lobe interference of each subcarrier, while the combination of ISP pulse shaping and Convolutional Coding provides the best performance improvement, achieving a BER of approximately 0.0018. Overall, the integration of ISP and Convolutional Coding effectively mitigates CFO-induced degradation, offering a robust and reliable solution for future 5G wireless communication systems

The management of relationships between customers and companies is still largely carried out conventionally. Communication between companies and customers tends to be slow and is highly limited by time constraints. This condition makes it more difficult to maintain good relationships between customers and companies, even though such relationships are crucial for business continuity. Conventional communication methods also hinder the monitoring of customer service performance toward customers. Technology-based Customer Relationship Management (CRM) can be a strategic solution to overcome the weaknesses in managing and maintaining these relationships. The purpose of this research is to implement a CRM application using real-time live chat technology to enable faster customer service responses and facilitate service monitoring for the company’s management, thereby making performance auditing of customer service easier. The CRM live chat application in this study was developed using the waterfall method and implemented using Visual Studio Code, Laragon as a web server, PHP, JavaScript, MySQL, and Pusher. The implementation was tested using Blackbox testing and User Acceptance Testing (UAT), with the results showing that all functions related to real-time communication and service monitoring operated properly, and the user satisfaction rate reached 85.8%.

Choosing a digital bank is a challenge for anyone, especially due to cognitive biases that influence decision making. This study aims to develop an Android-based Decision Support System (DSS) using the MAGIQ MARCOS method to provide recommendations for digital banks that suit users’ preferences. The MAGIQ method is used to weight the main criteria, namely Application Performance (C1), Financial Reports (C2), and User Experience (C3), while MARCOS is used to rank digital banking alternatives. This study includes data collection through surveys and interviews, data processing using MAGIQ for weighting, and ranking alternatives using MARCOS. The results indicate that Jenius ranked first with a preference value of 0.7632 followed by Seabank with 0.7164 and Krom Digital Bank with 0.6983. These findings show that the system is able to differentiate alternatives based on user priorities. The system achieved an accuracy of 80.39 percent compared with students’ manual selections confirming that the recommendations align with actual user preferences. The recommendations generated by the system are consistent with the priorities of decision makers who value application quality and user experience. Use case testing also shows that all test scenarios function as expected. This research contributes to the development of technology based DSS to help students make more rational and data driven decisions in choosing a digital bank. Future work may integrate real time data updates and predictive analysis to improve recommendation accuracy and expand the MAGIQ-MARCOS method to other sectors that require multi criteria decision making.

The ornamental fish industry in Indonesia has experienced significant growth, positioning the country as the second-largest global exporter of ornamental fish in 2020. However, fish shop owners still face operational challenges, especially in managing consistent and timely feeding across multiple aquariums. Manual feeding practices often lead to inefficiencies and can compromise fish health and water quality. This study presents an end-to-end fish feeding information system integrated with an Android mobile application, designed to address these challenges. System development in this study employs waterfall method. The system supports automated fish feeding routines, device management, and multi-user access with token-based authentication, enabling fish shop owners to operate multiple feeders under a single account. Communication between IoT devices and the backend server utilizes MQTT, ensuring independent control of each feeder through unique topics. The system introduces a novel architecture that supports multi-user, multi-device operations in an end-to-end feeding workflow, improving scalability and efficiency compared to existing single-device systems. System testing, including black box and load testing, demonstrated robust performance, with all test scenarios passing successfully and an error rate of 0.00% during high-load simulations involving up to 100 virtual users. These results indicate that the system effectively addresses existing limitations in fish feeding management and is capable of supporting multiple users and fish feeder devices simultaneously. Further development is recommended to enhance infrastructure, security, and scalability for real-world deployment.

This research designed and developed a prototype e-wallet management application for crowdfunding-based tuition fee payment at STMIK Bandung Bali, addressing higher education cost challenges. Using Agile methodology, the development covered requirements analysis, UI/database design, payment gateway integration, and testing. Core functionalities include student data, academic history, billing, e-wallet balance, donor contributions, and campus operator disbursements. Functional testing showed 100% success across all 9 black-box test scenarios, confirming successful crowdfunding system implementation. However, load and stress tests on shared hosting (0.5 CPU, 256 MB RAM) revealed performance limitations. Response times increased sharply from 2.2 seconds (100 requests) to 14.6 seconds (200 requests), with over 95% system failure beyond 400 concurrent requests, indicating hosting resource constraints. Empirical user evaluations (10 students, 5 donors, 2 operators) confirmed high system effectiveness and usability, yielding average scores of 4.2 for effectiveness and 4.0 for usability (on a 5-point Likert scale). Security measures include private key API integration, AES password encryption, and restricted sensitive data access. This research's success lies in its specific technical solution for institutional tuition crowdfunding, integrating directly with STMIK Bandung Bali's financial management, differentiating it from general platforms.

Social crime is a complex problem that occurs every day and requires a quick response. The large number of reports with language variations makes the manual classification process difficult. This research aims to develop an AI-based chatbot to classify types of social crime reports automatically using the IndoBERT model. Data was obtained from East Denpasar Police, LAPOR website, and X social media. The initial data set of 250 reports was augmented to 6,250 data using synonym augmentation technique. The data was then divided into 70:20:10 training scenarios to produce the best model. The evaluation showed high performance with accuracy 0.999200, precision 0.999203, recall 0.999200, and F1-score 0.999200. Validation was also done through confusion matrix and accuracy-loss graph. The chatbot is able to receive reports from the public and classify them into five main categories, namely theft, maltreatment, embezzlement, domestic violence, and murder. The results show that IndoBERT is effective in understanding and classifying Indonesian text reports accurately. The system is expected to assist law enforcement agencies in improving efficiency and speed in handling community reports as well as supporting the digitisation of the social crime complaint process.

This study explores the application of the YOLOv8 algorithm in detecting humanoid objects in an open space environment, with a special focus on school areas such as parking lots. The main objective is to develop an intelligent system that can accurately identify students based on four uniform classifications: none, grey, batik, and department-specific uniforms. The system is designed to function effectively in real-time by analyzing image and video data. The research methodology begins with data acquisition using CCTV footage, followed by annotation and preprocessing using Roboflow. The dataset consists of 314 images with 1,649 labeled bounding boxes, which are then divided into training and validation sets. A yaml configuration file is created to interact with the YOLOv8 model. Training is performed using YOLOv8s variants, with experimental variations in image size, batch size, and epochs to optimize model performance. The evaluation results show that the model achieves a precision of 0.86, a recall of 0.92, and a mean Average Precision (mAP@0.50) of 0.93. Furthermore, visual testing confirms the system's ability to detect students with a total detection accuracy of 85%. Some minor errors were observed in distinguishing between visually similar classes, such as batik and department uniforms. These results demonstrate the robustness and reliability of YOLOv8 in dynamic real-world environments. This study concludes that YOLOv8 can be effectively applied to educational settings for surveillance or monitoring systems. Future research will focus on improving accuracy by expanding the dataset and incorporating more diverse categories of humanoid objects.