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All Journal IJCIT (Indonesian Journal on Computer and Information Technology) Indonesian Journal on Software Engineering (IJSE) EVOLUSI : Jurnal Sains dan Manajemen Journal of Information Systems and Informatics Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Kalbiscientia Jurnal Sains dan Teknologi Jurnal Sistem Informasi Akuntansi Conten : Computer and Network Technology Indonesian Journal of Networking and Security - IJNS J-Intech (Journal of Information and Technology) Community Empowerment Journal Journal of Information Technology
Andrian Eko Widodo
Universitas Bina Sarana Informatika
Religion Arts Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering

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Perancangan Sistem Informasi Akuntansi Penjualan Handphone pada Toko Java Phone Fanny Fatma Wati; Andrian Eko Widodo; Nadiyah Hidayati; Mawadatul Maulidah; Recha Abriana Anggraini
Jurnal Sistem Informasi Akuntansi (JASIKA) Vol. 6 No. 1 (2026): Mei 2026
Publisher : LPPM UBSI Kampus Kota Tegal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jasika.v6i1.12701

Abstract

The rapid development of information technology encourages business actors to improve the quality of data management, including in the field of mobile phone sales. Java Phone Store still uses a manual system for recording sales transactions, which has the potential to cause recording errors, delays in report preparation, and less accurate financial information. Therefore, it is necessary to design an Accounting Information System for Sales that can overcome these problems and improve operational efficiency.This study aims to design a computerized sales accounting information system for Java Phone Store. The research methods used include observation, interviews, and literature study to collect relevant data. The system development method uses the waterfall model, which consists of requirement analysis, system design, implementation, and system testing stages.The results show that the designed system is able to manage product data, sales transactions, and generate sales reports automatically, quickly, and accurately. With this system, data processing becomes more efficient and minimizes errors that occur in the manual system. In conclusion, the implementation of this sales accounting information system is important to improve operational performance and the quality of financial information at Java Phone Store.
Detection of SQL Injection, XSS, and Command Injection Attacks in Web Payloads Using SVM, Random Forest, and XGBoost Andrian Eko Widodo; Fabriyan Fandi Dwi Imaniawan
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1655

Abstract

Web application attacks, including SQL Injection (SQLi), Cross-Site Scripting (XSS), and Command Injection (CmdI), remain major threats to digital services. This study develops and evaluates an adversarial-aware protocol for multi-class malicious payload detection, focusing on accuracy, robustness against non-adaptive mutations, and practical inference feasibility. The protocol compares LinearSVC, Random Forest, and XGBoost with character-level neural baselines, namely character CNN and BiLSTM, and a transparent rule-based comparator. Evaluation integrates stratified sampling, deduplicated validation, mutation testing, SHAP-based interpretation, and end-to-end throughput measurement. Experiments used 49,998 stratified records from the SQLi-XSS-CommandInjection dataset in Google Colaboratory. On the internal test set, XGBoost obtained the best performance, achieving 99.28% accuracy and 99.32% macro F1-score. After removing 878 exact duplicate records for stricter re-evaluation, XGBoost maintained 99.21% accuracy and 99.24% macro F1-score, indicating that the findings were not driven solely by duplicate leakage. The complete preprocessing, feature extraction, and prediction pipeline reached an average CPU inference time of 0.832 ms per sample. SHAP analysis of Random Forest highlighted injection operators, script fragments, keyword hits, and structural tokens as discriminative features. The results provide a controlled benchmark, although validation on real HTTP logs remains future work.
Co-Authors Abidin, Muhammad Ali Zaenal Ahmad Fauzi Alif Qurnia Saputra - AMIK BSI Purwokerto Angga Ardiansyah Aprih Widayanto Aryo Kencono Astuti, Rachmawati Darma Aziz Setyawan Hidayat, Aziz Setyawan Dany Pratmanto Dhea Savitri Dwi Yuli WibowO - AMIK BSI Purwokerto, Dwi Yuli WibowO Dzulchan Abror Eko Yulianto Fabriyan Fandi Dwi Imaniawan Fakihotun Titiani Fandhilah Fandhilah Fanny Fatma Wati Fariqotun Nisa Fatmawati, Kulum Husni Mubarok Mahmud Safudin Mawadatul Maulidah Mohammad Rizal Nurzaien Nadiyah Hidayati Nurlaelatul Maulidah Puspa Fortuna Zulfa Ratna Kurnia Sari Recha Abriana Anggraini Ria Rizqi Amalia Rousyati Rousyati Rousyati, Rousyati Saputra, Rangga Diva Sardiarinto Sardiarinto Sari, Ratna Kurnia Sopian Aji Suleman Suleman - AMIK BSI Tegal Suleman Suleman Suleman Suleman Suleman Suleman Suleman Suleman Supriatiningsih Supriatiningsih Vadlya Maarif Warjiyono Warjiyono Yamin Nuryamin