This study aims to assess the extent to which Microsoft Excel training is expected to improve the information processing skills of students in Madrasah aliyah (MA) Sunan Ampel, Sumberkima, Buleleng, Bali. Through a case study approach, we examine how this training successfully sculpts advanced-level students’ competencies, transforming them from passive users into individuals fluent in processing information. A curriculum focused on practical applications Exceeding expectations , ranging from basic functions to simple information visualization, is proven to significantly improve students’ ability to sort, analyze, and present information. The method applied in this study was a practice-based training, followed by an assessment of the skills of the participants after the training. The research findings indicated that microsoft training exceeded expectations was able to significantly improve students’ information retrieval skills, accelerating the process of information recapitulation and analysis. However, it was found that a proportion of participants still required individual assistance to achieve full mastery on the more advanced features. Overall, this training had a positive influence on improving information processing skills in Madrasah Aliyah (MA) Sunan Ampel Sumberkima, Buleleng, Bali. as well as can be an initial step to optimize the utilization of technology in educational information processing.
 

The application of machine learning, particularly the *Convolutional Neural Network* (CNN) method, has experienced rapid development in the medical field. Through the process of image data grouping, CNN is capable of classifying based on the similarity of characteristics and attributes of the images. This capability is highly beneficial in the healthcare sector, especially in differentiating chest X-ray images. This study aims to evaluate the DenseNet architecture while applying it to classify Covid-19 disease by utilizing 98 chest X-ray images as training data. The test results showed that the average *loss* value on the training data was 0.8621, with an average accuracy of 0.6950. Meanwhile, the average *loss* value on the validation data was recorded at 1.0682, with an average accuracy of 0.6399. From these results, it can be concluded that the model performed better on the training data than on the validation data, indicating the occurrence of *overfitting*. Therefore, further adjustments to the model are necessary to improve its performance on the validation data

The implementation of the Internship Program (Kuliah Kerja Praktek / KKP) aims to equip students with practical experience in the professional world while allowing them to apply the theoretical knowledge gained during their studies. This activity was carried out at Arfi Stik Keju and Brownies Batik Tangsel, a micro, small, and medium enterprise (MSME) that produces snack foods. One of the main challenges faced by this MSME is the inventory management process, which is still done manually, making it susceptible to errors, difficult to monitor in real time, and causing delays in generating reports. To solve these problems, a web-based stock management system was developed using PHP and MySQL technologies. This system enables real-time stock monitoring, improves the accuracy of inventory records, and accelerates report preparation. The research methods used include literature study, interviews, and direct observation. The result of this internship is a prototype of a web-based stock management system that can be accessed through various internet-connected devices, supporting the management in making data-driven decisions. It is expected that this system will help improve operational efficiency and contribute to the business development of Arfi Stik Keju and Brownies Batik Tangsel.
 

The KWS Community (Kumpulan Wong Sragen) serves as a collaborative platform for Sragen residents but faces challenges including manual data management, decentralized coordination, and limited transparency. This research aims to design an integrated web-based system to streamline administrative processes, member registration, activity scheduling, and participation tracking. The development employs the Waterfall method—a Software Development Life Cycle (SDLC) approach characterized by sequential phase completion, ensuring structured progression from one stage to the next. Results demonstrate a 60% reduction in administrative workload, improved data accuracy, and real-time communication via a centralized dashboard. Long-term impacts include enhanced member engagement and scalability for similar community models. This study highlights digital transformation's potential in strengthening community-based organizations.

The digital divide, resulting from economic limitations in accessing the internet, is a significant problem in many communities, including Pujorahayu Village. This research aims to design and implement a router-based Wireless Internet Service Provider (WISP) network as an affordable communal solution. The methodology employed is the Network Development Life Cycle (NDLC), which was applied to ten households utilizing TP-Link WR845N routers in WISP mode connected to a single primary internet source. The implementation results indicate that the WISP network successfully provides stable and economic internet access, while also positively fostering participation and a spirit of digital collaboration among residents. Nevertheless, several operational challenges were identified, such as limited signal range and potential bandwidth saturation during peak hours. Based on these findings, this research concludes that the solution is technically and socially viable. Further development of a bandwidth management system, the use of additional devices for coverage expansion, and the establishment of a transparent and sustainable communal management model are recommended. The implementation of this model can be replicated in other regions to support equitable internet access and strengthen digital inclusion on a national scale.
 

Comparison of Svelte JS Framework with React JS on Javascript-Based Web Application Performance. Informatics and Computer Engineering Education Study Program, Informatics and Computer Engineering Department, Faculty of Engineering, Makassar State University, (supervised by Mustari Lamada and Abdul Wahid). Comparison of Svelte JS and React JS frameworks using GTMetrix and WebPageTest tools by taking performance parameters including first contentful paint, speed index, large contentful paint, time to interactive, total blocking time, and cumulative layout shift. Each framework was tested by creating a 3-page website that had different loads. The test results were converted into scores where the score range was 1-3 for each parameter tested. React JS gets a total score of 26, 25, and 28 for each page. Svelte JS gets a total score of 34, 34, and 33 for each page. Native JS gets a total score of 34, 33, and 33 for each page. From these scores, it can be concluded that the Svelte JS framework is faster than React JS.

The advancement of artificial intelligence (AI) and automation technology has opened new opportunities for developing intelligent communication systems through chatbots. This study explores how the integration of n8n, a fair-code workflow automation tool, with AI can create autonomous chatbots that not only interact automatically with users but also analyze and report conversation outcomes in a structured manner. We propose a methodology that combines natural language processing (NLP) with automated workflows to build a chatbot capable of storing user interaction data, processing it into insights, and generating analytical reports that support service evaluation and business decision-making. This approach not only enhances user service efficiency but also adds analytical capabilities that would typically require separate manual processes. The findings of this study indicate that such a solution is highly applicable in areas such as customer service, online education, and internal system analysis

The development of Augmented Reality (AR) technology in the e-commerce sector offers new opportunities to enhance online shopping experiences through immersive and interactive features. This study aims to analyze the influence of AR characteristics—interactivity, vividness, novelty, and informativeness—on consumer perceptions (perceived enjoyment, perceived usefulness, and hedonic value), and how these perceptions affect attitudes, satisfaction, and purchase intention on the Shopee platform. A quantitative approach was employed, with data collected from 100 active Shopee users who have experienced using AR features. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that most AR characteristics significantly affect consumer perception. Furthermore, perceived enjoyment, usefulness, and hedonic value were found to influence user attitudes and satisfaction, ultimately increasing purchase intention. This study contributes theoretically to the understanding of AR integration in consumer behavior within e-commerce, and provides practical implications for businesses in designing immersive technology-based marketing strategies.

This study examines the problem of the success rate of computer course program participants at LKP Mitra Prestasi which is still an obstacle for institutional administrators. By applying a hybrid model approach in machine learning, which combines Random Forest and Long Short-Term Memory (LSTM) algorithms through the assembly merging technique, this study aims to develop a predictive model that is able to estimate the success rate of participants and recognize variables that contribute to the achievement of learning outcomes. The probability outputs from both models are used as an input feature for the Logistic Regression meta-classifier that studies the optimal combination of predictions to produce final graduation decisions. This approach leverages the Random Forest's advantages in recognizing static features and LSTM in recognizing temporal patterns. Research data was obtained from 100 course participants which included demographic information, academic performance, as well as data on the order of attendance and assignment submission. The research findings show that the hybrid model with the stacking ensemble built succeeded in achieving 95% accuracy, 95% precision, 100% recall, and 97.44% F1 score, with the main variables affecting graduation being final exam scores, attendance percentage, and accuracy of assignment submission.

Gramedia Digital is a platform that provides electronic book services widely used by the Indonesian public. However, in its operation, users often encounter issues such as frequent application crashes, slow content access, and unresponsive checkout processes. These problems indicate inefficiencies in handling information technology (IT) service incidents. This study aims to apply the RACI model (Responsible, Accountable, Consulted, Informed) to map out the roles and responsibilities of each party involved in the IT service issue resolution process in the Gramedia Digital application. The research method used is descriptive qualitative with a case study approach. The analysis shows that the implementation of the RACI model can clarify task distribution among teams, reduce work duplication, and improve the effectiveness of incident handling. These findings provide recommendations to strengthen the IT service management structure at Gramedia Digital, particularly in the aspect of responding to service disruptions.