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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan E-Jurnal Akuntansi
Permatasari, Novia
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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PREDICTING DIABETES MELLITUS USING CATBOOST CLASSIFIER AND SHAPLEY ADDITIVE EXPLANATION (SHAP) APPROACH Permatasari, Novia; Asy Syahidah, Shafiyah; Leofiro Irfiansyah, Aldo; Al-Haqqoni, M. Ghozy
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 2 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (468.484 KB) | DOI: 10.30598/barekengvol16iss2pp615-624

Abstract

Diabetes mellitus as a metabolic disease characterized by hyperglycemia can be dangerous if it cannot be handled properly. Early detection of existing symptoms can reduce the impact of delays in treatment. This study aims to carry out early-detection patients with diabetes mellitus using a machine learning approach through data from MIT’s GOSSIS (Global Open Source Severity of Illness Score). By using Shapley Additive Explanation (SHAP) which enables prioritization of feature that determine compound classification, this study shows that the CatBoost classifier has 14 features that significantly can be used for classification with feature ‘d1_glucose_max’ or the highest glucose concentration of the patient in their serum or plasma during the first 24 hours of their unit stay has the highest impact to classify diabetes mellitus patients, then followed by age and glucose APACHE. The selected features are then classified and get the validation AUC score of 86.86%.
Analysis of Artificial Intelligence (AI) Technology Acceptance Among Accounting Employees: A Model Based on UTAUT-3 Permatasari, Novia; Rahmawati, Mia Ika
E-Jurnal Akuntansi Vol. 35 No. 9 (2025)
Publisher : Fakultas Ekonomi dan Bisnis Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/EJA.2025.v35.i09.p18

Abstract

This study aims to analyze the factors that influence the acceptance and use of artificial intelligence (AI) technology by accounting employees using the UTAUT-3 model. Using a quantitative approach, data were collected from 162 accounting employees of an Internet Service Provider (ISP) company in East Java via a questionnaire and analyzed using PLS-SEM via SmartPLS 4. The results indicate that performance expectations, effort expectations, and social impact positively influence behavioral intention, while facility conditions, hedonic motivation, habit, and personal innovation do not have a significant effect. Habit influences actual usage behavior, but behavioral intention does not have a significant effect. These findings indicate the dominance of functional factors over pleasure or infrastructure in driving AI adoption. This study enriches the behavioral accounting literature and provides managerial implications for organizations in strategically adopting AI in financial reporting.
Co-Authors Al-Haqqoni, M. Ghozy Asy Syahidah, Shafiyah Leofiro Irfiansyah, Aldo Rahmawati, Mia Ika