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All Journal JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia)
I Dewa Ayu Sri Murdhani
Institut Bisnis dan Teknologi Indonesia
Computer Science & IT

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Accrual-Based Accounting Information System For Financial Compliance Monitoring I Nyoman Darma Kotama; Putu Sugiartawan; I Dewa Ayu Sri Murdhani
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 4 (2026): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Small and medium-sized enterprises (SMEs) often face challenges in implementing efficient and accurate financial reporting systems, primarily due to the limitations of manual accounting processes. These challenges lead to errors, delays, and compliance issues, which hinder timely decision-making and financial transparency. This research proposes the development and evaluation of an accrual-based Accounting Information System (AIS) designed to address these issues by automating financial reporting and compliance monitoring. The motivation behind this study is to improve the financial management practices of SMEs by providing a reliable system that ensures accurate financial reporting and real-time compliance monitoring. The main contribution of this research is the design of an AIS that integrates key financial functions, such as transaction processing, accrual calculations, and compliance checks, to streamline financial operations. Evaluation results from case studies indicate that the system significantly reduced reporting errors by 50%, enhanced compliance accuracy by 25%, and decreased report generation time by 40%. Despite these successes, challenges remain in system integration with legacy accounting software and handling complex financial transactions. Future work will focus on enhancing the scalability of the system, integrating advanced machine learning techniques for predictive financial analysis, and improving the integration process to allow for broader implementation in diverse business contexts. Additionally, the development of a mobile application to improve accessibility to financial reports and compliance alerts will be explored.
Classification of Tuberculosis and Pneumonia Lung Diseases in X-Ray Images Using the CNN Method with VGG-19 Architecture I Dewa Ayu Sri Murdhani; Heru Ismanto; Didit Suprihanto
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 2 (2025): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.269

Abstract

Tuberculosis (TB) and Pneumonia continue to be among the world’s leading causes of morbidity and mortality, particularly in low- and middle-income countries where access to advanced diagnostic tools remains limited. Conventional radiological interpretation, while effective, heavily depends on the experience and precision of radiologists, resulting in potential subjectivity and diagnostic variability. This study proposes a fully automated classification framework for lung disease detection using a Convolutional Neural Network (CNN) based on the VGG-19 architecture. The model aims to enhance diagnostic accuracy and reliability by leveraging deep learning techniques capable of capturing subtle radiographic patterns that may not be readily identifiable by human observers. A dataset of 3,623 chest X-ray images—divided into Normal, Pneumonia, and Tuberculosis classes—was compiled from Kaggle and Mendeley Data repositories. Preprocessing techniques including Contrast Limited Adaptive Histogram Equalization (CLAHE), cropping, resizing, and normalization were employed to enhance contrast and minimize noise. The model was trained and tested under four data-split configurations (80:20, 70:30, 60:40, and 50:50) to assess generalization capability. The 70:30 configuration achieved optimal performance, recording 96% accuracy, 97% precision, 95% recall, and a 96% F1-score. These findings demonstrate that the VGG-19 model can accurately distinguish between TB, Pneumonia, and Normal cases, providing a reliable foundation for AI-driven medical diagnosis. Future research will focus on dataset expansion, interpretability enhancement using Explainable AI (XAI), and the integration of this model into clinical decision-support systems.
Co-Authors Didit Suprihanto, Didit Heru Ismanto I Nyoman Darma Kotama Putu Sugiartawan