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All Journal KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Jurnal Pendidikan MIPA
Purwita, Anggraeni Widya
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Computer Science & IT Education Other

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Analisis Transformasi Digital Pelayanan Kesehatan Publik melalui Implementasi Aplikasi SiKuat di Puskesmas Kota Sidoarjo Purwita, Anggraeni Widya; Izzati, Berlian Maulidya; Cinthya, Monica; Elfaiz, Ersha Aisyah; Abdillah, Rifqi
KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Vol 6, No 1 (2025)
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.kernel.2025.v6i1.7784

Abstract

Digital transformation is an important part of improving the quality of health services, especially through the implementation of information systems. SiKuat is an information system developed to support the efficiency of the Puskesmas. The purpose of this study is to analyze the implementation process of SiKuat using the Diffusion of Innovation theory as an analytical framework with a qualitative approach with a case study. From the results of interviews, observations, and document analysis, it shows that SiKuat has a relative advantage in accelerating and efficient service time, as well as observability whose benefits are easily observed directly by users, especially during queues and waiting times for services. However, obstacles occur in the aspect of system compatibility with the flow and work procedures that are customary, and the complexity and trial capabilities there is no trial phase before SiKuat is fully implemented during operations because the system is relatively complicated. Without increasing training, adjustment systems, and gradual implementation of strategies, the implementation of SiKuat is at risk of being limited to the initial user group. Strengthening the five innovation attributes in a balanced manner is needed to expand the institutional diffusion system to the early majority phase.
Integrating Trust and Perceived Performance into the Expectation-Confirmation Model: A Mixed-Methods Study on Generative AI Persistence Purwita, Anggraeni Widya; Sari, Anisa Yunita
Jurnal Pendidikan MIPA Vol 26, No 4 (2025): Jurnal Pendidikan MIPA
Publisher : FKIP Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jpmipa.v26i4.pp2637-2650

Abstract

The rapid adoption of Generative AI in tertiary education has changed how students obtain, process, and assess learning information, but little is known about how satisfied they will be in the long term and the persistence intention towards such technologies. This research paper discusses why students were satisfied and wanted to continue using AI-based learning tools, according to the Expectation Confirmation Theory (ECT). A sequential mixed-methods design was used to collect quantitative data from 106 university students across Education, Engineering Computer Science, and Health Sciences majors. Quantitative data were analyzed using PLS-SEM, and an eventual semi-structured interview of eight subjects was used to validate the quantitative data. The findings suggest that all the variables of expectation, perceived performance, confirmation, and satisfaction are important predictors of continuance intention. However, perceived performance is the most effective predictor. There is a statistically significant but weak relationship between expectations and confirmation, and students' confirmation is likely influenced more by their experience with AI performance than by their initial expectations. These findings are also supported by qualitative evidence indicating that the reliability, contextual relevance, and trustworthiness of AI systems strongly impact student satisfaction and confidence in AI-based learning. The study highlights the significance of perceived performance and trust as key factors in maintaining the use of AI in education. In theory, it uses the Expectation Confirmation Theory, incorporating ethical awareness and reliability as contextual factors that affect satisfaction and continuance intention. In practice, this means that AI developers and teachers need to be more transparent about their algorithms, accurate, and ethically literate to build trust and foster meaningful interaction with AI in higher education.    Keywords: expectation confirmation theory, gen ai, student satisfaction, continuance intention, higher education.
Co-Authors Abdillah, Rifqi Berlian Maulidya Izzati Cinthya, Monica Elfaiz, Ersha Aisyah Sari, Anisa Yunita