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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) bit-Tech
Maysha Permata Putri
Universitas Buddhi Dharma
Computer Science & IT

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User Interface Experience Analysis of PMB Online Buddhi Dharma Using System Usability Scale Junaedi; Ardiane Rossi Kurniawan Maranto; Maysha Permata Putri; Suwitno
bit-Tech Vol. 6 No. 2 (2023): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v6i2.1051

Abstract

In the era of advanced digital technologies, the admission process for new students (PMBs) has become a critical aspect of education. To streamline and expedite this process, educational institutions are increasingly utilizing online enrollment applications. One such application, PMB Online Buddhi Dharma, plays a crucial role in this context. However, the success of these applications is not solely determined by technical ease; user experience, particularly the User Interface (UI), plays a pivotal role in influencing user satisfaction and efficiency. This study employs the System Usability Scale (SUS) method to comprehensively analyze the UI of the PMB Online Buddhi Dharma application, providing insights into usability and user satisfaction. Drawing from previous studies utilizing SUS in similar contexts, the research aims to contribute to the development and enhancement of the application's UI. This research evaluates too the effectiveness of Buddhi Dharma University's PMB Online in meeting the digital registration needs of prospective students, emphasizing ease of use and user acceptance. Through the SUS method, the study assesses user satisfaction and ease of use, obtaining an average SUS score of 78 from 30 respondents. This score categorizes Buddhi Dharma University Online PMB as "good," indicating a commendable level of acceptance from users, predominantly prospective students. The research concludes with implications for the application's further improvement and development, emphasizing the importance of user-friendly interfaces in digital admission processes.
Talent Development Center Recommendation System Using Content-Based Filtering Maysha Permata Putri; Benny Daniawan
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 18, No 1 (2026): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v18i1.32816

Abstract

The development of digital technology has led to increased gadget usage among children, often resulting in a decline in interest in productive physical and social activities. The Indonesian Child Protection Commission reported that over 71.3% of school-age children using gadgets daily. This condition highlights the need for efforts to redirect children's attention toward more beneficial activities, one of which is talent development. Early talent development is crucial for supporting personal potential and future career paths. However, limited information often becomes an obstacle in choosing the right place for talent development that suits an individual's needs and interests. This study aims to design a system that can provide talent development center recommendations for seekers. By implementing the Content-Based Filtering (CBF) method, the system matches user preferences—such as interests, skills, and preferred types of activities expressed in keywords—with the descriptions of available talent development center. Weighting is carried out using the Term Frequency–Inverse Document Frequency (TF-IDF) algorithm to enhance the relevance of the recommendations by calculating the similarity level between talent development center descriptions based on keyword weights. This approach allows the system to provide more personalized recommendations without relying on other users' data. The testing conducted in this study, using 7 sample talent development places, resulted in 5 recommendations with the top recommendation being Chic’s Musik, which had the highest TF-IDF value of 1.9029.Index Terms— Content-Based Filtering, Recommendation System, TF-IDF, Talent Development
Co-Authors Ardiane Rossi Kurniawan Maranto Benny Daniawan Junaedi Suwitno