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ANALISIS AI, SISTEM, DAN UI/UX TIKTOK BERBASIS TAM: SYSTEMATIC LITERATURE REVIEW Agizka Rizqta Gusanova; Suprih Widodo; Ulva Elviani
J-SIKA|Jurnal Sistem Informasi Karya Anak Bangsa Vol. 7 No. 02 (2025): Jurnal Sistem Informasi Karya Anak Bangsa (J-SIKA) Vol 7 No 2 edisi Desember 2
Publisher : Program Studi Sistem Informasi

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Abstract

Abstrak Dominasi TikTok dalam lanskap media sosial global sering kali hanya dilihat dari perspektif fenomena budaya, sementara arsitektur teknis yang mendasarinya masih jarang dieksplorasi secara mendalam. Penelitian ini bertujuan untuk menganalisis bagaimana integrasi teknologi di balik layar memengaruhi adopsi pengguna melalui perspektif Technology Acceptance Model (TAM). Menggunakan metode Systematic Literature Review (SLR) dengan protokol PRISMA, penelitian ini menyeleksi dan menganalisis 10 literatur primer yang diterbitkan antara tahun 2020 hingga 2025. Hasil analisis mengungkapkan bahwa persepsi pengguna dibentuk oleh tiga pilar teknis utama. Pertama, persepsi kegunaan (Perceived Usefulness) dikonstruksi oleh algoritma AI berbasis Multimodal Data Fusion yang menjamin personalisasi konten secara presisi. Kedua, persepsi kemudahan penggunaan (Perceived Ease of Use) dicapai melalui sinergi antara desain antarmuka imersif yang meminimalkan beban kognitif dan kinerja sistem berbasis Edge Caching serta Prefetching yang mengeliminasi latensi jaringan. Penelitian ini menyimpulkan bahwa keberhasilan retensi pengguna TikTok adalah hasil dari rekayasa teknis yang menciptakan pengalaman tanpa hambatan (frictionless), di mana stabilitas infrastruktur berfungsi sebagai anteseden vital bagi kepuasan psikologis pengguna. Kata Kunci: TikTok, Technology Acceptance Model (TAM), Kecerdasan Buatan, Desain Antarmuka, Kinerja Sistem. Abstract TikTok's dominance in the global social media landscape is often viewed merely as a cultural phenomenon, while the underlying technical architecture remains underexplored. This study aims to analyze how the integration of backend technologies influences user adoption through the perspective of the Technology Acceptance Model (TAM). Using a Systematic Literature Review (SLR) method with the PRISMA protocol, this study selected and analyzed 10 primary literatures published between 2020 and 2025. The results reveal that user perception is shaped by three main technical pillars. First, Perceived Usefulness is constructed by AI algorithms based on Multimodal Data Fusion which ensures precise content personalization. Second, Perceived Ease of Use is achieved through a synergy between immersive interface design that minimizes cognitive load and system performance based on Edge Caching and Prefetching strategies that eliminate network latency. This study concludes that TikTok's user retention success is the result of technical engineering that creates a frictionless experience, where infrastructure stability serves as a vital antecedent to user psychological satisfaction. Keywords: TikTok, Technology Acceptance Model (TAM), Artificial Intelligence, User Interface, System Performance.
Does TikTok Really Matter for Academic Achievement? Self-Regulation and Use Duration Among University Students Agizka Rizqta Gusanova; Rizki Hikmawan
International Journal of Post Axial: Futuristic Teaching and Learning Vol. 4 No. 4 December 2026: International Journal of Post-Axial
Publisher : Yayasan Azhar Amanaa Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59944/postaxial.v3i4.2082

Abstract

The increasing use of TikTok among university students has raised questions about its relationship with academic achievement. Previous studies have reported mixed findings. This study examined the association between self-regulated learning (SRL)-based TikTok use regulation and semester grade point average (GPA) in the full sample and two daily-use duration groups. It extends previous research by examining how students plan, control, and evaluate TikTok use beyond duration or problematic use alone. Separate subgroup analyses explored the association within each duration group. A quantitative, cross-sectional survey involved 197 university students. Analyses included descriptive statistics, Cronbach’s alpha, Pearson correlation, simple linear regression, and robustness checks. The full-sample association was near zero and nonsignificant (r = −0.038, p = 0.600). Associations were also nonsignificant in the groups reporting more than two hours and two hours or less of daily use. Spearman correlations were nonsignificant, and HC3 and bootstrap analyses provided no evidence of significant linear coefficients, although diagnostic concerns limit interpretation. The measured regulation score alone provided little information about differences in semester GPA in this sample. Broader academic behaviors warrant further investigation to clarify the practical relevance of digital self-regulation. These findings do not establish that TikTok use is academically harmless or that associations are statistically equivalent across duration groups