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All Journal Indonesian Journal Economic Review (IJER) JTH: Journal of Technology and Health Investasi : Inovasi Jurnal Ekonomi Dan Akuntansi
Bryant Ritchie Trisnodjojo
Universitas Airlangga
Economics, Econometrics & Finance Education Health Professions Library & Information Science Medicine & Pharmacology Nursing

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Analisis Fraud Detection Menggunakan Machine Learning pada Sistem Akuntansi Modern Yenny Verawati; Sri Wahyuni Israfatin Bobihu; Iip Dyah Kusumaningati; Bryant Ritchie Trisnodjojo; Jessica Gita Elvira Thanos
INVESTASI : Inovasi Jurnal Ekonomi dan Akuntansi Vol. 4 No. 2 (2026): Artikel Penelitian
Publisher : Soratekno Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59696/investasi.v4i2.249

Abstract

Perkembangan transformasi digital dalam bidang akuntansi telah meningkatkan efisiensi pengelolaan transaksi keuangan, namun di sisi lain juga memperbesar risiko terjadinya kecurangan (fraud) yang semakin kompleks dan sulit dideteksi melalui metode audit konvensional. Penelitian ini bertujuan untuk menganalisis penerapan algoritma Machine Learning dalam mendeteksi indikasi fraud pada sistem akuntansi modern secara lebih cepat, akurat, dan adaptif terhadap pola transaksi yang dinamis. Metode penelitian menggunakan pendekatan kuantitatif dengan memanfaatkan data transaksi keuangan yang telah diklasifikasikan ke dalam kategori fraud dan non-fraud. Tahapan penelitian meliputi proses data preprocessing, seleksi fitur, pelatihan model, serta evaluasi kinerja menggunakan beberapa algoritma Machine Learning, seperti Logistic Regression, Random Forest, dan Extreme Gradient Boosting (XGBoost). Kinerja model dievaluasi berdasarkan metrik akurasi, presisi, recall, dan F1-score untuk menentukan algoritma yang paling efektif dalam mendeteksi transaksi mencurigakan. Hasil penelitian menunjukkan bahwa pendekatan Machine Learning mampu mengidentifikasi pola anomali dan indikasi fraud dengan tingkat akurasi yang tinggi dibandingkan metode berbasis aturan (rule-based system). Selain itu, model yang dikembangkan mampu mengurangi tingkat kesalahan deteksi serta meningkatkan kemampuan sistem dalam mendukung proses pengambilan keputusan audit. Temuan ini menunjukkan bahwa integrasi Machine Learning pada sistem akuntansi modern dapat menjadi solusi yang efektif untuk memperkuat pengendalian internal dan mitigasi risiko fraud di era digital.
The Role of Business Sustainability in the Financial Management Governance of Micro, Small, and Medium Enterprises in the Digital Era Bryant Ritchie Trisnodjojo; Nirmadarningsih Hiya; Supiati; Renaldi; Nurnaningsih Utiarahman
Indonesian Journal Economic Review (IJER) Vol. 6 No. 2 (2026): June
Publisher : Divisi Riset, Lembaga Mitra Solusi Teknologi Informasi (L-MSTI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59431/ijer.v6i2.849

Abstract

The purpose of this study is to explain the role of business sustainability in the financial management governance of micro, small, and medium enterprises in the digital era. This research employed a descriptive qualitative approach, with secondary data such as books and journal articles as references to aid analysis. The research results show that in the digital era, the concept of business sustainability is no longer merely a trend or a moral responsibility, but rather a strategic pillar that determines the life or death of micro, small, and medium enterprises. When integrated into financial management governance, sustainability serves as a navigator, ensuring that the use of digital technology not only pursues short-term profits but also long-term resilience. To survive and compete in the modern market, micro, small, and medium-sized enterprises are advised to immediately transition from manual financial record-keeping to digital-based systems such as smart accounting applications and integrated automated cashier platforms. This digitalization plays a crucial role because it provides full visibility of cash flow in real time, minimizes the risk of human error, and provides accurate data for rapid strategic decision-making.
AUTOMATED ESSAY SCORING FOR STUDENT EXAMS USING DEEP NLP MODELS Andi Kaimuddin; Bryant Ritchie Trisnodjojo; Muhamad Ziaul Haq; Nursalim; Riezky Purnama Sari
JTH: Journal of Technology and Health Vol. 4 No. 1 (2026): July: JTH: Journal of Technology and Health
Publisher : CV. Fahr Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61677/jth.v4i1.858

Abstract

The increasing use of essay-based examinations in higher education has created significant challenges in maintaining efficient, objective, and consistent assessment processes. Manual essay grading is time-consuming and susceptible to subjective judgment, particularly when evaluating large numbers of student responses. Therefore, this study aims to develop and evaluate an Automated Essay Scoring (AES) system based on Bidirectional Encoder Representations from Transformers (BERT) to improve the accuracy and consistency of student essay assessment. This research employed an experimental quantitative approach using 2,500 student essay responses, of which 2,340 valid responses were retained after preprocessing and data cleaning. The dataset was divided into training, validation, and testing subsets using a 70:15:15 ratio. The proposed model was fine-tuned using the AdamW optimizer with a learning rate of 2 × 10⁻⁵, a batch size of 16, and 8 training epochs. Model performance was evaluated using Quadratic Weighted Kappa (QWK), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The experimental results demonstrate that the proposed BERT model achieved a QWK score of 0.872, indicating strong agreement with human evaluators, while obtaining an MAE of 0.418 and an RMSE of 0.593, reflecting relatively low prediction errors. Comparative evaluation also showed that the proposed BERT model outperformed conventional baseline approaches in automated essay scoring, confirming the effectiveness of contextual language representations for understanding semantic information in student essays. These findings indicate that the proposed framework provides a reliable and efficient solution for automated essay assessment, offering practical benefits for improving scoring consistency, reducing lecturers' workload, and supporting the implementation of intelligent assessment systems in higher education.
Co-Authors Andi Kaimuddin Iip Dyah Kusumaningati Jessica Gita Elvira Thanos Muhamad Ziaul Haq Nirmadarningsih Hiya Nurnaningsih Utiarahman Nursalim Renaldi Riezky Purnama Sari Sri Wahyuni Israfatin Bobihu Supiati Yenny Verawati