Development of Quibot Technology-Based Learning Media to Improve Student Motivation and Learning Outcomes

Palma Juanta — 2026-02-26

This study examines the effect of Quibot, a chatbot-based learning medium, on students’ learning outcomes and learning motivation in mathematics learning at the junior high school level. The study was motivated by the limited integration of interactive digital media and its impact on students’ academic performance. A quantitative quasi-experimental design was employed involving 60 eighth-grade students of Yayasan Perguruan Sultan Iskandar Muda, divided into an experimental group using Quibot and a control group using conventional learning methods. Learning outcomes were measured using a validated multiple-choice mathematics achievement test, while learning motivation was assessed through a Likert-scale questionnaire. Data were analyzed using normality and homogeneity tests, followed by one-way ANOVA for learning outcomes and Welch’s t-test for learning motivation. The results showed a significant difference in learning outcomes between the two groups, F(1,58) = 4.65, p = 0.035, with a moderate effect size (η² = 0.074). However, no significant difference was found in learning motivation (p = 0.871). These findings indicate that Quibot effectively improves students’ academic achievement but does not significantly influence learning motivation within a short intervention period. This study highlights the role of chatbot-based learning media as an instrumental tool in supporting cognitive learning outcomes.

Implementation of The Topsis Algorithm In A Car Purchase Decision-Making System

Viki Julian Avinda Nur Ependi — 2026-02-26

Private vehicles such as cars and motorcycles are crucial modes of transportation for the movement of goods and people. With technological advancements, car manufacturers offer a wide range of vehicles. Therefore, prospective buyers face challenges in selecting a vehicle that best suits their preferences and criteria. To tackle the issue, this study develops a practical decision support system (DSS) as a user-friendly tool for buyers, with theoretical contributions in the form of a more adaptive TOPSIS application and systematic analysis in car selection. This study focuses on collecting car-related data using 12 criteria, such as price, fuel consumption, safety, and design. The TOPSIS method is then normalized to ensure a fair and objective comparison between criteria. The results show the top alternative ranking, Suzuki 2002 (closeness score of 0.7089 in position 1), and the SUS test result of 85.6, indicating that the system is easy to use and capable of providing recommendations that align with user preferences. Therefore, this study highlights that the TOPSIS method can be an effective tool in supporting car purchase decision-making and making it easier for prospective buyers to choose the car that best suits their needs.

Journal of Technology and Informatics (JoTI)

Development of Quibot Technology-Based Learning Media to Improve Student Motivation and Learning Outcomes

Implementation of The Topsis Algorithm In A Car Purchase Decision-Making System