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All Journal Journal of Scientech Research and Development Journal of Artificial Intelligence and Legal Technology
Sapriani Gustina
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Humanities Computer Science & IT Education Industrial & Manufacturing Engineering Social Sciences

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ANALISIS ANCAMAN KEAMANAN CYBER DI PT. CITILINK INDONESIA DAN SOLUSI UNTUK MENGAMANKAN OBJEK VITAL Fatihana Nur Salsabillah; Ardy Wicaksono; Sapriani Gustina; Landung Sudarmana
Journal of Scientech Research and Development Vol 6 No 1 (2024): JSRD, June 2024
Publisher : Ikatan Dosen Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56670/jsrd.v6i1.511

Abstract

Penelitian ini menganalisis ancaman cyber di PT. Citilink Indonesia dan mengembangkan solusi untuk melindungi aset vital perusahaan. Dengan data dari tim keamanan, dokumentasi internal, dan pengujian sistem, penelitian ini melakukan analisis SWOT dan perbandingan kebijakan dengan standar ISO 27001:2022. Pengujian penetrasi mengungkapkan kerentanan, terutama terhadap serangan phishing. Hasil analisis menunjukkan kekuatan Citilink dalam menghadapi tantangan cyber. Rekomendasi strategi mencakup peningkatan kesadaran karyawan, optimasi teknologi, dan penguatan infrastruktur cyber untuk meningkatkan perlindungan dan kesiapan perusahaan.
Explainable XGBoost for Indonesian Hoax Detection under the Electronic Transactions Law Rizka Nadialif; Landung Sudarmana; Selvi Dwi Hartiyani; Sapriani Gustina; Ardy Wicaksono; Agatha Pricillia Sekar Tamtomo
Journal of Artificial Intelligence and Legal Technology Vol. 2 No. 2 (2026): August 2026
Publisher : Sah Publisher

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Abstract

The rapid circulation of misleading information in digital spaces creates a need for screening tools that are accurate, transparent, and suitable for human review. This study develops a reproducible Indonesian hoax-detection pipeline and examines whether model explanations can support cautious legal review. The experiment uses a political-hoax text corpus with fixed training, validation, and test splits. The primary classifier combines word- and character-level TF-IDF features with XGBoost, while a frozen-encoder IndoBERT-Lite model is evaluated as a CPU pilot. TreeSHAP summarizes global and local feature contributions, and LIME is used to inspect borderline predictions. On the held-out test set, XGBoost achieved 0.9725 accuracy, 0.9724 macro-F1, 0.9957 ROC-AUC, and a 0.0235 Brier score; the constrained IndoBERT pilot reached a validation macro-F1 of 0.3343 and is not treated as a final benchmark. The most influential features included source and article-genre markers such as “baca juga,” “referensi,” “Kompas,” “Facebook,” and “foto hoaks,” indicating that the model may learn publisher or writing-style shortcuts in addition to claim-related signals. The audit also identified normalized duplicate overlap across the training-validation and training-test splits. The resulting system should therefore support triage, explanation, and documentation by trained reviewers, not serve as a standalone basis for determining the truth of a claim or establishing an Electronic Information and Transactions Law violation.
Co-Authors Agatha Pricillia Sekar Tamtomo Ardy Wicaksono Ardy Wicaksono Fatihana Nur Salsabillah Hartiyani, Selvi Dwi Landung Sudarmana Rizka Nadialif