Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Computer Science Research
Galet Guntoro Setiaji
Universitas Semarang, Semarang
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Kuantifikasi Risiko Introspection pada Tiga Kategori Otorisasi OWASP: Studi Komparatif REST API dan GraphQL Naufal Hanif Athallah; Galet Guntoro Setiaji; Ahmad Rifa’i
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.9874

Abstract

The advancement of Application Programming Interfaces (APIs) demands measurable architectural-level security evaluation. This study quantifies the security risks of REST API and GraphQL based on three authorization categories from the OWASP API Security Top 10 2023 (API1, API3, and API5). The exclusive limitation to these three categories was established to focus purely on access control logic flaws rather than infrastructure-level vulnerabilities. The experiment utilizes TixVuln, a parallel-architecture testbed instrument explicitly designed to eliminate external database bias a comparative advantage not present in standard single-architecture vulnerable applications. Authorization evaluation was executed contextually to avoid the high false-negative rates typically produced by automated security scanning tools (SAST/DAST) in business logic testing. Quantification results using the OWASP Risk Rating Methodology reveal a novelty that GraphQL experiences a risk category escalation from Medium to Critical levels in API3 and API5 compared to REST API. This significant leap in the Ease of Discovery metric is absolutely triggered by the operational schema exposure through the introspection feature. Mitigation testing validates that implementing field whitelisting and resolver-level Role-Based Access Control is imperative to suppress inherent risks in single-endpoint architectures. The main contribution of this research is the provision of an isolated empirical evaluation framework that quantitatively proves the flexibility of GraphQL architecture is directly proportional to the increased fatality of authorization risks if the schema discovery feature is not strictly configured.
Analisis Komparasi Kinerja LSTM dan CNN dalam Deteksi Spam Email Berbasis Deep learning Maugy Al Kautsar; Galet Guntoro Setiaji; Ahmad Rifa'i
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.572

Abstract

Spam email remains a critical issue in digital communication due to its potential misuse in spreading false information and online fraud. This study aims to evaluate and compare the performance of two deep learning models Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for text-based spam email classification. The dataset used in this study was obtained from Kaggle and contains 5,572 labeled email entries categorized as spam and non-spam. The preprocessing stage included labeling, cleaning, lowercasing (casefolding), tokenization, stopword removal, and stemming. The data was split into training and testing sets with a 70:30 ratio. Both models were trained using the same configuration and evaluated using accuracy, loss, confusion matrix, and F1-score metrics. The results indicate that the LSTM model achieved the highest accuracy of 98.72% with a loss value of 0.0377, outperforming the CNN model, which achieved 87.78% accuracy and a loss of 0.3659. Based on these findings, LSTM demonstrated superior performance in detecting spam emails using text-based input. This research is expected to serve as a reference for developing more accurate and effective spam detection systems in the future.
Peningkatan Transparansi Tata Kelola Keuangan Desa melalui Pengembangan Sistem Berbasis Web dengan Menerapkan Model Agile Septiano Cepeda Da Costa; Galet Guntoro Setiaji; Ahmad Rifa'i
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.821

Abstract

This research discusses village financial management as a primary aspect in supporting village progress and community welfare. Transparent and effective budget management is crucial for ensuring that village funds are utilized optimally for development programs. However, several obstacles persist, including delays in financial reporting and limited access to information for residents. The purpose of this research is to design and develop a web-based village financial information system using the Laravel framework and an Agile methodology to improve transparency, data accessibility, and the effectiveness of real-time financial reporting. The Agile method was selected for its iterative and collaborative development process, which is responsive to user needs. This approach allows both residents and village officials to participate actively in the development process, ensuring the resulting system serves as a viable solution to existing problems. Functional testing results using the Black Box method indicate that the system facilitates easier financial reporting, provides accurate data presentation, and supports accountability in village fund management. This system is expected to increase community participation in budget oversight and serve as a technological solution for the digitalization of village financial management. Ultimately, this system aims to make village budget management transparent, fast, and accessible to all residents at any time, thereby fostering community trust and engagement in village development.
Co-Authors Ahmad Rifa'i Ahmad Rifa’i Maugy Al Kautsar Naufal Hanif Athallah Septiano Cepeda Da Costa