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ANALISIS PENERIMAAN PENGGUNA DIGITAL SIGNATURE PADA SISTEM SAKTI DENGAN PENDEKATAN TECHNOLOGY ACCEPTANCE MODEL (TAM) Muhamad Geby Ramadan Roring; Pratiwi Rachmadi; Agnes Novita Ida Safitri; Lucia Sri Istiyowati; Deden Prayitno
JRIS : Jurnal Rekayasa Informasi Swadharma Vol 6, No 2 (2026): JURNAL JRIS EDISI JULI 2026
Publisher : Institut Teknologi dan Bisnis (ITB) Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jris.vol6no2.1257

Abstract

This study aims to analyze the influence of Perceived Ease of Use and Perceived Usefulness on attitudes toward using and Actual Usage of Digital Signatures (DS) in the implementation of the SAKTI system. The research method is a quantitative approach, using the Technology Acceptance Model (TAM) as the basis for analysis. The research variables are limited to the use of DS issued by the National Cyber and Crypto Agency (BSSN) in state budget disbursement transactions through the SAKTI system. The results indicate that Perceived Ease of Use and Perceived Usefulness significantly influence attitudes toward using and Actual Usage of DS in the SAKTI system. Furthermore, the results indicate that attitudes toward using a function as a partial mediator of the influence of Perceived Ease of Use and Perceived Usefulness on Actual Usage of Digital Signatures in the SAKTI system. DS implementation has been proven to increase efficiency, security, transparency, and reduce the risk of misuse of physical documents in state financial transactions. This research provides a practical contribution to supporting a more modern, adaptive, and responsive digital transformation of state finances.Penelitian ini bertujuan untuk menganalisis pengaruh Perceived Ease of Use dan Perceived Usefulness terhadap Attitude Toward Using serta Actual Usage Digital Signature (DS) dalam implementasi Sistem SAKTI. Metode penelitian yang digunakan adalah pendekatan kuantitatif dengan memanfaatkan kerangka Technology Acceptance Model (TAM) sebagai dasar analisis. Variabel penelitian dibatasi pada penggunaan DS yang diterbitkan oleh Badan Siber dan Sandi Negara (BSSN) dalam transaksi pencairan dana APBN melalui Sistem SAKTI. Hasil penelitian menunjukkan bahwa Perceived Ease of Use dan Perceived Usefulness berpengaruh signifikan terhadap sikap terhadap Attitude Toward Using dan Actual Usage DS dalam Sistem SAKTI. Selanjutnya, hasil penelitian menunjukkan bahwa Attitude Toward Using berfungsi sebagai mediator parsial (partial mediation) pengaruh Perceived Ease of Use dan Perceived Usefulness terhadap Actual Usage Digital Signature dalam Sistem SAKTI. Implementasi DS terbukti mampu meningkatkan efisiensi, keamanan, transparansi, serta mengurangi risiko penyalahgunaan dokumen fisik dalam transaksi keuangan negara. Penelitian ini memberikan kontribusi praktis dalam mendukung transformasi digital keuangan negara yang lebih modern, adaptif, dan responsif
Hybrid Rule-Based and Anomaly Detection Model for Wholesale Sales Risk Classification Dwi Atmodjo WP; Winny Purbaratri; Lely Priska D Tampubolon; Nani Krisnawaty Tachjar; Deden Prayitno; Budi Indiarto; M Iman Wahyudi
Journal of Renewable Energy and Smart Device Vol. 4 No. 1 August 2026
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66314/joresd.v4i1.1184

Abstract

Large-scale wholesale transaction systems face increasing risks from suspicious purchasing patterns, abnormal customer behavior, and operational inconsistencies, while conventional rule-based methods may fail to identify previously unseen patterns. This study develops a decision-level hybrid risk classification model that combines expert-derived business rules with post-hoc anomaly decisions from Isolation Forest and DBSCAN. The modules are orchestrated in Apache Airflow and executed through a scheduled daily DAG for batch transaction monitoring. The model was evaluated retrospectively using 12,476 wholesale transactions recorded over 12 months in a single company. The business rules and ground-truth labels were elicited from the same expert pool; therefore, the separation between model development and evaluation was implemented at the data level through independent training, validation, and test subsets. The Hybrid Rule + Isolation Forest configuration achieved the highest accuracy at 87.5%, compared with 74.3% for the authors' rule-based baseline. The resulting 13.1-percentage-point gain should be interpreted as an internal ablation result rather than a comparison with a state-of-the-art external model. For operational efficiency, the automated workflow processed a batch of 1,000 transactions in 42 seconds, compared with approximately 3 hours of manual processing. These findings suggest that combining interpretable business rules with anomaly detection at the decision level can improve risk classification while retaining operational transparency. However, because the evaluation used data from only one wholesale company and shared expert sources for rules and labels, validation across independent companies and expert groups is required before broader generalization.