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All Journal Jurnal Manajemen Informatika Jayakarta Artificial Intelligence Systems and Its Applications (AISA)
Divi Galih Prasetyo Putri
Universitas Gadjah Mada
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Materials Science & Nanotechnology

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Studi Perancangan Desain Antarmuka Sistem Pencarian Berbasis Kolaborasi untuk Pembelajaran Siswa Radivan Alan Nouruzzaman; Divi Galih Prasetyo Putri
Jurnal Manajamen Informatika Jayakarta Vol 6 No 1 (2026): JMI Jayakarta (February 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jmijayakarta.v6i1.2214

Abstract

Perkembangan teknologi digital yang pesat menyebabkan melimpahnya informasi dan memunculkan tantangan seperti penurunan skor literasi digital serta kebutuhan akan validasi sosial dalam proses pembelajaran. Oleh karena itu, kolaborasi menjadi esensial untuk memungkinkan siswa SD mengevaluasi sumber informasi secara kritis dan membangun pemahaman yang terverifikasi. Penelitian ini bertujuan merancang dan mengevaluasi prototipe antarmuka (UI/UX) sistem pencarian berbasis kolaborasi untuk siswa SD menggunakan metode Design Thinking, dengan harapan sistem ini berfungsi sebagai e-learning yang efektif dalam memperkuat literasi dan motivasi belajar siswa. Evaluasi dua iterasi prototipe pada siswa SD menunjukkan peningkatan signifikan pada usability, dimana Success Rate meningkat menjadi 87,07% (dari 53,73%) dan Completion Time berkurang menjadi 39,92 detik. Berdasarkan kuesioner UEQ (User Experience Questionnaire), aspek fungsionalitas seperti Perspicuity (1,73), Efficiency (1,75), dan Dependability (1,92) meningkat tajam. Meskipun aspek daya tarik (Attractiveness 1,90) dan inovasi (Novelty 1,05) menunjukkan perlunya pengembangan visual lebih lanjut, hasil IMI (Intrinsic Motivation Inventory) mengkonfirmasi efektivitas sistem dalam memicu motivasi intrinsik, dengan peningkatan pada semua dimensi, termasuk Interest/Enjoyment (5,76) dan Value/Usefulness (5,61). Secara keseluruhan, prototipe sistem ini berhasil memperkuat literasi dan motivasi intrinsik, menjadikannya solusi e-learning yang efektif.
Applying Explainable Artificial Intelligence Principles to Interface Design: Improving User Trust and Understandability in a Chicken Weight Monitoring System Divi Galih Prasetyo Putri; Maritza Angelina Az Zahra; Margareta Hardiyanti
Artificial Intelligence Systems and Its Applications Vol. 2 No. 1 (2026): Vol. 2, No. 1, June 2026
Publisher : CV Cognispectra Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65917/aisa.v2i1.72

Abstract

Artificial Intelligence (AI) has been increasingly adopted in smart farming to support monitoring and decision-making processes. However, many AI-based systems still operate as black boxes, making their outputs difficult for end users to understand and potentially reducing user trust. Although Explainable Artificial Intelligence (XAI) has been proposed to improve transparency, studies integrating XAI principles into interface design and evaluating their effects on user experience remain limited, particularly in smart farming contexts. This study investigates the implementation of XAI principles in redesigning the interface of a chicken weight monitoring system and evaluates their effects on user trust, understandability, and usability. A concurrent embedded mixed methods approach with a within-subject and counterbalanced design was conducted involving 16 participants. The redesigned interface incorporated human-centered XAI principles and was evaluated using the Trust in Automation Scale (TiAS), an understandability questionnaire, and the System Usability Scale (SUS). The results showed statistically significant improvements across all evaluated aspects (p < 0.001). Trust increased from 50.52 to 73.82, understandability from 54.25 to 81.88, and usability from 43.28 to 74.38, with large effect sizes observed in all measurements. Qualitative findings indicated that clearer and contextual explanations improved users’ interpretation of system outputs. These findings suggest that integrating XAI principles into interface design can support more transparent and understandable interaction in AI-based monitoring systems.
Co-Authors Margareta Hardiyanti Maritza Angelina Az Zahra Radivan Alan Nouruzzaman