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All Journal Indonesian Journal of Artificial Intelligence and Data Mining JURTEKSI
Sabaniyah, Arbangi Puput
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Computer Science & IT

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Early Prediction of Stroke Disease Diagnosis Patients Using Data Mining Algorithm Comparison Subarkah, Pungkas; Damayanti, Wenti Risma; Sabaniyah, Arbangi Puput
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.25955

Abstract

Stroke constitutes a medical emergency of paramount significance, characterized by a notably elevated mortality rate, and stands as the foremost cause of mortality within hospital settings. The dataset employed for this analysis is sourced from Kaggle, denoted as the Brain Stroke Dataset, encompassing a total of 4981 records. This research aims to carry out early prediction of stroke sufferers using several algorithms including the ANN algorithm, CART, KNN, and the NBC algorithm. The results obtained in the ANN algorithm obtained an accuracy of 93.53%, in the CART algorithm of 95.02%, in the KNN algorithm of 91.09% and in the NBC algorithm of 88.44%. With the outcomes of this research, the use of the cart set of rules may be used for early evaluation of stroke sufferers because it has a good degree of accuracy and is listed inside the excellent type kind
ANALYSIS OF THE ACCEPTANCE OF THE SINAGA ATTENDANCE APPLICATION AT SMA NEGERI 1 JATILAWANG USING THE TECHNOLOGY ACCEPTANCE MODEL (TAM) Sabaniyah, Arbangi Puput; Yunita, Ika Romadhoni; Subarkah, Pungkas
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 1 (2025): Desember 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i1.4205

Abstract

This study analyzes the acceptance of teachers and ASN employees of the SINAGA (Sistem Informasi Layanan Kepegawaian) attendance application at SMA Negeri 1 Jatilawang using a modified Technology Acceptance Model (TAM). The model was extended by incorporating two external variables: Information Quality and Complexity. This explanatory quantitative research employed the Structural Equation Modeling–Partial Least Square (SEM-PLS) method involving 60 respondents who are civil servants, consisting of teachers and administrative staff. The results reveal that Information Quality has a positive and significant influence on both Perceived Usefulness (PU) and Perceived Ease of Use (PEOU), while Complexity does not show a significant effect on either variable. Furthermore, PEOU and PU have a positive impact on Attitude Toward Use (ATU), which subsequently affects Behavioral Intention to Use (BIU). Behavioral intention, in turn, strongly influences Actual Use (AU). These findings indicate that teachers’ acceptance of the SINAGA digital attendance system in educational settings is primarily driven by information quality and users’ positive attitudes rather than by system complexity. Theoretically, this study contributes to the expansion of TAM application in the educational context. Practically, it provides valuable insights for improving the effectiveness of SINAGA implementation through better information quality and enhanced user experience.
Co-Authors Damayanti, Wenti Risma Pungkas Subarkah Yunita, Ika Romadhoni