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All Journal Jurnal Ilmiah Informatika Komputer JUTI: Jurnal Ilmiah Teknologi Informasi Seminar Nasional Informatika (SEMNASIF) Sinkron : Jurnal dan Penelitian Teknik Informatika Journal on Information Systems, Technology of Information and Communications (JoISTIC) Syntax Literate: Jurnal Ilmiah Indonesia Jurnal ULTIMA InfoSys Journal of Information Systems and Informatics Jurnal Informatika dan Sistem Informasi (JUISI) Insyst : Journal of Intelligent System and Computation Jurnal Locus Penelitian dan Pengabdian
Lisana Lisana
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Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Staying The Course: A Systematic Literature Review On Mooc Learners Lisana, Lisana; Susanto, Alvin Fernando
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v10i8.61365

Abstract

Massive Open Online Courses have transformed access to education by offering flexible, learning opportunities. However, while initial adoption is widespread, sustaining learner engagement remains a critical challenge, marked by persistently low completion rates. This study addresses the growing academic concern around MOOC continuance intention, especially why learners choose to continue or discontinue participation. The purpose of this systematic literature review is to synthesize empirical research on MOOC continuance intention using the PRISMA framework. A total of 41 empirical articles indexed in Scopus, Web of Science, and Semantic Scholar were reviewed. This study answers four key research questions: What methods and measurements are commonly used? Which theoretical frameworks used? Which countries conducting studies? What key factors influence continuance intention? Findings reveal that quantitative methods using survey data are most prevalent, alongside studies using online reviews and mixed-method approaches. The Expectation-Confirmation Model emerges as the most frequently applied theoretical framework. China leads in publication volume, reflecting national policy support for MOOCs. Key influencing factors identified include perceived usefulness, satisfaction, perceived ease of use, confirmation, attitude, system quality, and perceived reputation. This review consolidates fragmented findings, builds a cohesive knowledge base, and offers practical insights for designing more effective, engaging, and sustainable MOOCs.
PENERAPAN ALGORITMA QUANTUM TREEMAPS DAN BUBBLEMAPS PADA ZOOMABLE IMAGE BROWSER Lisana Lisana
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 6, No 2 Juli 2007
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (316.179 KB) | DOI: 10.12962/j24068535.v6i2.a190

Abstract

Pada umumnya, aplikasi penampil gambar (image browser) menampilkan gambar dengan menggunakan miniatur gambar (thumbnail) yang diatur dalam bentuk grid, dimana pengguna dapat memilih gambar yang hendak dilihat dengan menggunakan mouse.  Meskipun aplikasi penampil gambar sangat populer, mereka memiliki beberapa kekurangan.  Misalnya, mereka hanya bisa menampilkan gambar yang terletak dalam satu direktori saja.  Jika dalam direktori tersebut ada sangat banyak gambar dan tidak cukup untuk ditampilkan semuanya dalam satu layar, maka pengguna harus menggunakan scrollbar untuk melihat sisanya.  Untuk beberapa kasus, miniatur gambar yang ditampilkan terlalu kecil sehingga pengguna tidak tahu apa isi dari gambar tersebut.  Untuk mengatasi kekurangan-kekurangan diatas, digunakan Zoomable User Interface (ZUI) yang terintegrasi didalam sebuah aplikasi penampil gambar yang diberi nama Zoomable Image Browser yang dapat menampilkan gambar dengan menggunakan miniatur gambar tanpa scrollbar meskipun jumlahnya banyak.  Selain itu, pada aplikasi ini pengguna dapat menampilkan gambar-gambar dari banyak direktori sekaligus.  Untuk menyusun kelompok-kelompok gambar tersebut secara otomatis, digunakan dua algoritma, yaitu algoritma quantum treemaps dan algoritma bubblemaps.  Quantum treemaps adalah algoritma yang membagi daerah yang ada dalam bentuk persegi panjang. Persegi panjang yang dihasilkan akan dibagi-bagi lagi menjadi persegi panjang yang lebih kecil dan begitu seterusnya. Algoritma bubblemaps bekerja dengan mengisi sel-sel yang ada dalam sebuah grid, menyimpan tanda dari sel-sel yang ada, sehingga dapat diketahui bahwa sel tersebut akan ditempati oleh gambar dari kelompok yang mana. Kesimpulan yang dapat diambil adalah karena algoritma bubblemaps menghasilkan daerah dengan bentuk acak, maka pengguna akan kesulitan dalam menemukan kelompok gambar tertentu, tetapi tidak menghasilkan tempat kosong pada kelompok gambar. Sedangkan algoritma quantum treemaps menghasilkan daerah berbentuk persegi panjang untuk menampung kelompok-kelompok gambar, sehingga pada sebagian besar daerah kelompok gambar terdapat tempat kosong, tetapi pengguna akan lebih mudah membedakan kelompok-kelompok gambar.Kata Kunci: Quantum Treemaps, Bubblemaps, Zoomable Image Browser
Adoption of Artificial Intelligence in Vocational High Schools: A Systematic Review of Teachers’ Perspectives Mafrukhah, Erny; Lisana, Lisana
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15701

Abstract

The purpose of this study is to examine Vocational High School teachers' perceptions of the adoption of artificial intelligence (AI) in vocational education. Using a systematic literature review (SLR) approach with the PRISMA 2020 protocol, 1,346 articles were identified from six main databases: ACM, IEEE Xplore, ScienceDirect, Google Scholar, and Semantic Scholar. Of the 1,346 articles identified, 263 were excluded at an early stage because they did not meet basic criteria, such as authorship, publication language, and document type. A total of 1,083 articles were screened, yielding 208 reports for in-depth screening. After the screening and feasibility assessment, 29 studies were included in the final analysis. The analysis showed that 72% of studies reported positive results, 24% reported moderate results, and 3% reported exploratory results. The dominant factors influencing teachers' perceptions included infrastructure readiness, digital competence, institutional support, the relevance of the vocational curriculum, and ethical and privacy issues. These findings emphasize the need for a holistic strategy for implementing AI in vocational schools that encompasses teacher training, education policy, and ethical considerations.
Higher Education Students’ Use and Continuous Intention to Use Generative AI: A Systematic Literature Review Pranoto, Velicia Marveline; Lisana
Jurnal Locus Penelitian dan Pengabdian Vol. 5 No. 3 (2026): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v5i3.5275

Abstract

This systematic literature review aims to examine existing studies on higher education students’ use and continuous intention to use generative AI to support their studies. Using PICOTQ to build the research question, as well as establish inclusion and exclusion criteria. PRISMA framework was used for the selection process. From 33 records, it was found that most studies were conducted in 2024. China was the country with most studies about generative AI. Most study populations were university students. The generative AI tool most frequently examined was ChatGPT. Most papers employed the UTAUT framework, and the most used factor in the studies was perceived usefulness. Future research should explore relevant moderators, and consider diverse student populations and generative AI tools beyond ChatGPT.
Co-Authors Bena, Dea Almira Christy, Cathrine Abigael Kevin kevin Mafrukhah, Erny Novandyanta, Evan Pranoto, Velicia Marveline Steven, Liauwnad Susanto, Agung Satria Susanto, Alvin Fernando Sutanto, Jessica Levina