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All Journal Jurnal Ilmiah Teknik Informatika (TEKINFO)
Tika Endah Lestari
Universitas Sampoerna
Chemical Engineering, Chemistry & Bioengineering Decision Sciences, Operations Research & Management Education Industrial & Manufacturing Engineering Social Sciences Transportation

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Student Perceptions Of AI-Based Decision Support Systems: A Descriptive Statistical Analysis Afrigh Elena Shabra; Tika Endah Lestari
Jurnal Ilmiah Teknik Informatika (TEKINFO) Vol. 27 No. 1 (2026): TEKINFO Vol 27 No 1 April 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

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Abstract

The rapid development of artificial intelligence (AI) has influenced many aspects of students’ academicactivities, especially in decision-making processes. Many university students now use AI-based tools to helpthem understand assignments, generate ideas, and evaluate information during their studies. This study aimsto examine students’ reliance on AI-based decision support systems and identify several related factors,including trust and critical thinking. A quantitative survey method was used in this research, and data werecollected from 60 university students from different academic backgrounds in the Jabodetabek area throughan online questionnaire distributed using Google Forms. The collected data were analyzed using descriptivestatistics. The findings showed that AI tools were widely used among students, with many respondentsdemonstrating moderate to high levels of reliance on AI systems. Although students tended to trust AIgeneratedoutputs, many respondents still evaluated the information critically before using it in academictasks. However, some respondents also believed that excessive AI usage could reduce independent thinkingabilities. Overall, the findings suggest that AI provides both benefits and potential risks in academic contexts.Therefore, students should use AI in a balanced and critical manner so that technology can support learningwithout reducing independent thinking and decision-making abilities.
Role of Recommendation Systems in Digital Music Streaming Platforms: User Decision-Making Among Sampoerna University Students Marina Indira Candera; Tika Endah Lestari
Jurnal Ilmiah Teknik Informatika (TEKINFO) Vol. 27 No. 1 (2026): TEKINFO Vol 27 No 1 April 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

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Abstract

This study explored the influence of recommendation systems in digital music streaming platforms on the decision-making processes of students at Sampoerna University. Utilizing a socio-technical framework, the research examined the tension between algorithmic efficiency and user exploration. Data were collected via a quantitative survey of 41 students, which revealed that while platforms like Spotify (80.5%) and YouTube Music (51.2%) were dominant, users experienced a distinct "algorithmic paradox." Although the most frequent rating for recommendation accuracy was an 8 out of 10 (reported by 41.5% of respondents), a significant 68.3% of students reported experiencing algorithmic boredom due to repetitive suggestions. These findings suggested that current systems, while effective at reducing cognitive load through features like auto-play (68.3%) and personalized playlists (53.7%), often trapped users in "filter bubbles" or "taste tautologies." The study concluded that the next generation of music Information System must evolve beyond simple predictive accuracy to incorporate context-aware diversity and serendipity, thereby balancing the efficiency of automation with the human need for discovery and autonomy.
Co-Authors Afrigh Elena Shabra Marina Indira Candera