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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Innovative: Journal Of Social Science Research
Baridah, Lailam
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Humanities Computer Science & IT Law, Crime, Criminology & Criminal Justice Public Health Other

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Proses Pengauditan Sistem Informasi Keuangan dan Peluang di Era Digital Fajrillah, Fajrillah; Baridah, Lailam; Nur Aini, Sakina; Salsabila, Nabila; Daulay, M.Endar Mahmuda
Innovative: Journal Of Social Science Research Vol. 4 No. 2 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i2.10007

Abstract

Era digital membawa perubahan signifikan dalam berbagai aspek, termasuk proses audit sistem informasi keuangan. Penggunaan teknologi informasi yang semakin canggih dalam sistem keuangan menghadirkan peluang dan tantangan bagi auditor. Tujuan artikel ini adalah untuk menganalisis proses pengauditan sistem informasi keuangan di era digital, serta mengidentifikasi peluang dan tantangan yang dihadapi auditor. Penelitian ini menggunakan pendekatan kualitatif dengan metode studi kasus dan dengan menganalisis berbagai literatur dan jurnal ilmiah terkait dengan proses audit dan era digital. Hasil penelitian menunjukkan bahwa era digital menghadirkan beberapa peluang bagi auditor. Proses pengauditan sistem informasi keuangan di era digital membutuhkan adaptasi dan pengembangan metodologi audit yang tepat. Auditor perlu meningkatkan keterampilan mereka dalam teknologi digital untuk memanfaatkan peluang dan mengatasi tantangan di era digital.
Predicting Student Learning Outcomes in Vocational Computer and Network Engineering Using Naïve Bayes Baridah, Lailam; Putri, Raissa Amanda
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.38333

Abstract

This study applied the Naïve Bayes algorithm to predict student learning outcomes in the Basic Computer and Network Engineering subject at SMKN 1 Sipispis. A quantitative approach was employed, using data from 311 students, which consisted of both academic variables (assignments, midterm exams, and final exams) and non-academic variables (attendance, attitude, and learning interest). The dataset was preprocessed by cleaning, encoding, and splitting into training and testing sets using several ratios (90/10, 80/20, 70/30, and 60/40). The Naïve Bayes model was trained and evaluated using accuracy, precision, recall, and F1-score metrics. The best performance was achieved with the 80/20 data split, yielding an accuracy of 74.6%, demonstrating the model’s ability to capture probabilistic relationships between academic and non-academic factors. These findings indicate that the Naïve Bayes algorithm can effectively classify student performance levels such as Fair, Good, and Excellent, providing a reliable foundation for an automated decision support system. The developed web-based system can help teachers identify students at risk of declining performance early, enabling more adaptive and data-driven educational interventions
Co-Authors Daulay, M.Endar Mahmuda Fajrillah, Fajrillah Nur Aini, Sakina Raissa Amanda Putri Salsabila, Nabila