Currently the detection of learning styles from the external aspect has not produced optimal results. This research tries to solve the problem by using an internal approach. The internal approach is one that derives from the personality of the learner. One of the personality traits that each learner possesses is prior knowledge. This research starts with the prior knowledge generation process using the Latent Semantic Indexing (LSI) method. LSI is a technique using Singular Value Decomposition (SVD) to find meaning in a sentence. LSI works to generate the prior knowledge of each learner. After the prior knowledge is raised, then one can predict learning style using the artificial neural network (ANN) method. The results of this study are more accurate than the results of detection conducted with an external approach.

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Abstract-A learning style is an issue related to learners. In one way or the other, learning styles could assist learners in their learning activities. If the learners ignore their learning styles, it may influence their effort in understanding teaching materials. To overcome these problems, a model for reliable automatic learning style detection is needed. Currently, there are two approaches in automatically detecting learning styles: data driven and literature based. Learners, especially those with changing learning styles, have difficulties in adopting these two approaches since they are not adaptive, dynamic and responsive (ADR). To solve the above problems, a model using agent learning approach is proposed. Agent learning performs four phased activities, i.e. initialization, learning, matching and recommendations to decide which learning styles are used by the students. Furthermore, the system will provide teaching materials which are appropriate for the detected learning style. The detection process is performed automatically by combining data-driven and literature-based approaches. The detected learning style used for this research is VARK (Visual, Auditory, Read/Write, and Kinesthetic). This learning style detection model is expected to optimize the learners in adhering with the online learning.Index Terms-detection model, VARK, reinforcement learning.

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There are so many ways to process employee attendance, one of which is to use the manual method. So far, there are still many large companies that still implement attendance manually, but this causes a lot of time leaks or other violations, which of course makes it less effective and efficient and causes attendance information to be inaccurate. In a company with quite a lot of employees, it is very necessary to have proper, fast, accurate attendance management. An accurate methodology for solving problems in this modern era with the use of QR barcodes because it will really help companies to attend to employees in real time. The system is made with the PHP programming language and uses the MYSQL database. The purpose of QR Code (Quick Response Code) technology in companies is as a tool in processing employee attendance data, employee identity cards and also processing employee data which is beneficial for employees because they can carry out computerized attendance activities. The results to be obtained from the research and implementation of this system are by entering several examples of employee data as an experimental form of attendance transactions, and the attendance application program is made to run properly. The system created produces several features in the form of user features, checking QR codes for attendance, generating QR codes from each employee card, recapitulation and attendance reports on the system, and employee data in the form of employee names, positions, work shifts and work location placements

The global skincare industry is experiencing rapid growth, with Indonesia's skincare market expected to be the fastest-growing in the Asia-Pacific region, mainly contributed by the middle-class population. This phenomenon leads to a highly saturated market, requiring brands to differentiate themselves through packaging, increasing companies' investment in packaging. Generational preferences influence packaging design, as Generation Z favors simple and sustainable packaging. This study aims to identify the cosmetic packaging attributes significantly impacting Generation Z's purchase intention. This research identifies visual packaging attributes such as packaging material, closure types, shape, color tone, and graphics as significant factors in customer purchase intention. The study uses a quantitative approach where a questionnaire is distributed to Generation Z residing in Jabodetabek and Bandung. Regression and conjoint analyses are employed to analyze the influence of the independent variable. The regression results indicate that packaging material, shape, and color tone significantly impact customer purchase intention. Conjoint analysis reveals that packaging material is the most important variable, followed by packaging shape, closure, and color tone. The most preferred packaging profile combination comprises glass material, dispensing closure, bottle shape, and tertiary Color. Even though both method results provide different rank significance results, the conjoint analysis provides more relevant results as it depicts packaging as a whole similar to real situations. The findings of this study can be utilized to determine which important packaging attributes need to be focused on during the designing process.