Article Per Year (5 Year)
p-Index From 2021 - 2026
4.731
P-Index
This Author published in this journals
All Journal Jurnal EMT KITA Sains Manajemen:Jurnal Manajemen UNSERA JIM UPB (Jurnal Ilmiah Manajemen Universitas Putera Batam) J-MAS (Jurnal Manajemen dan Sains) Owner : Riset dan Jurnal Akuntansi Jurnal Sains Sosio Humaniora Almana : Jurnal Manajemen dan Bisnis JASa (Jurnal Akuntansi, Audit dan Sistem Informasi Akuntansi) Jurnal Mantik JURNAL PENELITIAN EKONOMI DAN AKUNTANSI (JPENSI) Jurnal GeoEkonomi Jurnal Sains Manajemen dan Bisnis Indonesia TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Jurnal Riset Bisnis dan Manajemen Enrichment : Journal of Management EKONOMI, KEUANGAN, INVESTASI DAN SYARIAH (EKUITAS) Jurnal Scientia Jurnal Pengabdian Masyarakat Nusantara Jurnal Ekonomi Jurnal Abdimas Kartika Wijayakusuma Jurnal Ekonomi Perjuangan (JUMPER) AJAD : Jurnal Pengabdian kepada Masyarakat International Journal of Management Science and Information Technology (IJMSIT) Jurnal Manajemen Formosa Journal of Applied Sciences (FJAS) Jurnal Ilmu Sosial dan Humaniora Paradigma
Erpurini, Wala
Unknown Affiliation
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

Published : 38 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Paradigma

 1 
Fine-Tuned Autoencoder Neural Network for Anomaly Detection in Accounting Transactions Nur Alamsyah; Budiman, Budiman; Rahmani, Hani Fitria; Erpurini, Wala
Paradigma - Jurnal Komputer dan Informatika Vol. 27 No. 2 (2025): September 2025 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v27i2.8697

Abstract

Anomaly detection in accounting transactions plays a crucial role in identifying irregularities that may signal fraud, errors, or unusual financial behavior. Traditional rule-based and statistical methods often struggle to detect complex and hidden patterns in large-scale financial datasets. This paper presents a fine-tuned Autoencoder Neural Network for detecting anomalies in structured accounting records. The model processes feature such as date, account type, debit, credit, transaction category, and payment method. Preprocessing includes handling missing values, encoding categorical data, and extracting temporal features. The Autoencoder architecture was optimized using multiple hidden layers and dropout regularization to prevent overfitting. Reconstruction errors were used to determine anomaly scores, with a dynamic threshold set at the 98th percentile. Experimental results show that the model accurately distinguishes normal and anomalous transactions, identifying 2,000 outliers from a total of 100,000 records. Additional analysis indicates that anomalies often occur during weekends or holidays and involve unusual payment methods. These findings demonstrate the potential of the fine-tuned Autoencoder as a scalable and intelligent anomaly detection framework to support auditors and financial analysts in proactive fraud prevention.
Co-Authors Abdul Holik Abdurahman, Khaerul Rizal Azhar Affandi Budiman Desfitriady Fachri Setiawan Farmasih, Sarah Gustaf Leonandri, Dino Hakim Mulfatama Hani Fitria Rahmani Hardianti Irawan, Noval Indra Irfan Kurnia Ramadhan Irma Devita Haryono Leonandri, Dino Gustaf Lesmana, Asep Maman Rukmana Moch Badrudin Moh. Sidik Priadana Muhammad Rifky Pratama Muhiban, Ayi Noerdiana, Yusep Nur Alamsyah Nur Alamsyah Nur Alamsyah, Nur Nurhasanah, Tiara Nurlaela, Widiya Pratiwi, Arny Resmi Herliani Rana Ardila Rahma Restu Astuti Rimbun Kencana Safitri, Tri Ayu Suci Indahsari Sutreusna, Dejan Syifa Aulia Fajrin Tri Handayani Undang Juju Wapa Samrotul Janah Wia Handayani Zulyanti, Rafikah