This study aims to measure the level of participant satisfaction with learning services at LKP Multi Talenta Komputer Purwokerto. Participant satisfaction is an important indicator in assessing the effectiveness of the learning curriculum implemented. Factors such as facilities, services, and obstacles in learning become the main benchmark. Data from 54 respondents were processed using K-Means Clustering algorithm to identify the most superior and weak factors. The results of this study provide recommendations for future service improvements, in order to increase participant loyalty and learning effectiveness.

Currently, the Amikom University Purwokerto student portal is still not equipped with a student data search feature that can make it easier to access related information. The unavailability of this feature can provide significant obstacles to various students' daily activities, especially in activities such as academic research, collaborative group work across study programs, and interactions between departments. The aim of this research is to apply the TOGAF framework which is expected to be able to help identify the root of the problem and provide a more comprehensive, systematic and effective solution in an effort to improve the functionality of this portal, so that it can better support students' academic needs. This research uses the TOGAF method which includes Understanding Business Context, Vision Architecture, Definition Architecture, and Transition Architecture. The result of this research is a flowchart design for the student data search feature which was created and analyzed using the Bizagi Modeler application which was compared between the design and the current process. Based on the results, it shows that the design of the student data search flow through Student Amikom Purwokerto is much faster and more efficient because the data search time is reduced from 40 minutes to 3 minutes.
 

This study aims to evaluate the level of user satisfaction with the website of SMAN 1 Wangon by applying the End User Computing Satisfaction (EUCS) method. This method assesses user satisfaction based on five main dimensions: system quality, information quality, service quality, end user, and individual benefits. Data collection was conducted through questionnaires distributed to students, teachers, and administrative staff. The results of the analysis show that overall user satisfaction is in a good category, although there are some aspects that need to be improved, especially in terms of information and service quality. This research is expected to provide suggestions for further development of the school website, in order to improve user experience and support the learning process.

In the digital era, the utilization of information technology has become a primary need to support various aspects of education. One of the innovations implemented in educational institutions is the use of e-learning platforms, which allow learners to study flexibly without being hindered by distance and time. Universitas Amikom Purwokerto has developed the E-Learning Support System (ELLS) website as an online learning tool accessible by students and lecturers. This study aims to analyze the service management of ELLS using the ITIL V3 framework, which provides a structured guide in managing and optimizing services to better meet user needs in a sustainable manner. The methodology used includes observation, interviews, and surveys distributed to service users. The results show that the implementation of ITIL V3 can improve service effectiveness, reduce the risk of disruptions, and provide a positive experience for users. This research is expected to provide recommendations for improving e-learning service management at Universitas Amikom Purwokerto and serve as a reference for other educational institutions looking to enhance the quality of their services.

KPRI KOKARNABA Baturraden faces challenges in managing increasingly complex sales data, particularly in identifying the most in-demand products to maximize profit. This study aims to analyze sales patterns using the Naïve Bayes algorithm as a probability-based classification method. The collected sales data were analyzed to identify categories of best-selling and less popular products within the cooperative. The results indicate that the Naïve Bayes algorithm has an accuracy rate of 77.56% in predicting product categories. This research is expected to assist the cooperative in optimizing stock management and improving member satisfaction.