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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Azhari Shouni Barkah
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Automotive Engineering Computer Science & IT Control & Systems Engineering Engineering

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Design of a Multi-Tenant SaaS-Based Centralized Financial System Using a Silent Accounting Approach Rizki Cahya Putri cahyaputri; Azhari Shouni Barkah; Aulia Suryaning Tyas; Intan Nur Sifa; Purnia Setiawati; Mayza Nurul Khasanatun Nisa; Sri Rahayu; Lina Nur Afifah
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2356

Abstract

Village financial management faces various fundamental challenges, including transaction recording that is still manual, a lack of integration between financial systems and village operational activities, and the absence of a platform capable of serving multiple villages within a single efficient infrastructure. These conditions result in financial reporting processes that are inefficient, error-prone, and difficult to account for. This study aims to design a centralized financial system based on a multi-tenant Software as a Service (SaaS) architecture using the Silent Accounting approach, defined as an automated transaction recording mechanism triggered by operational module activities without manual intervention. This study employs a qualitative descriptive method with a literature review approach. The design yields three main artifacts a system flowchart illustrating the workflow from user authentication, role assignment, and transaction validation through to automatic journal entry and posting to general ledger an Entity Relationship Diagram (ERD) modeling the database structure consisting of seven entities and a Data Flow Diagram (DFD) breaking down the system into five main processes A multi-tenant architecture with a ‘tenant_id’ column ensures data isolation between villages while allowing a single platform to serve multiple village simultaneously. The Silent Accounting mechanism ensures that all village financial activities are recorded consistently, accurately and in real time. The design is expected to serve as the foundation for the development and scalable village financial management platform.
EVALUASI SKEMA FUNGSI TERKOMPRESI PADA SMALL LANGUAGE MODEL MENGGUNAKAN QLORA PADA DOMAIN APARATUR SIPIL NEGARA Alfan Lily Armansyah; Imam Tahyudin; Azhari Shouni Barkah
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8124

Abstract

As government institutions undergo digitalization, demand has grown for intelligent assistants that can resolve personnel inquiries autonomously by means of function calling. Commercial large language models remain difficult to deploy in this setting owing to recurring subscription fees and potential exposure of confidential records, which positions locally hosted Small Language Models (SLMs) as a viable substitute. However, conventional verbose JSON schemas exhaust the context window and degrade the accuracy of small models. This study proposes the Compressed Function Schema (CFS), a concise notation replacing verbose JSON schemas, combined with QLoRA fine-tuning on the Qwen3.5-0.8B model for the personnel domain of the Indonesian Civil Service (ASN). Evaluation was conducted on 173 test samples under three controlled conditions, namely baseline (verbose schema, zero-shot), ablation (compressed schema, zero-shot), and proposed (compressed schema, fine-tuned), using Function Name Accuracy (FNA), Parameter Accuracy (PA), and Exact Match Accuracy (EMA) metrics. Results show that CFS reduces schema tokens from 1,739 to 302 tokens, or 5.76 times more compact, a significant reduction, while the proposed model achieves FNA of 95.95%, PA of 97.20%, and EMA of 91.03%, outperforming the baseline across all metrics. The ablation study reveals that the compressed schema does not function without fine-tuning, indicating that the two are mutually complementary. Accordingly, an 0.8-billion-parameter SLM can operate on a 16 GB commodity GPU with high accuracy for personnel information services.
Co-Authors Alfan Lily Armansyah Aulia Suryaning Tyas Imam Tahyudin Intan Nur Sifa Lina Nur Afifah Mayza Nurul Khasanatun Nisa Purnia Setiawati Rizki Cahya Putri cahyaputri SRI RAHAYU