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All Journal Jurnal Transformatika Mimbar Agribisnis: Jurnal Pemikiran Masyarakat Ilmiah Berwawasan Agribisnis Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) Hippocampus: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Informasi Prosiding Seminar Hasil Penelitian dan Pengabdian Kepada Masyarakat Jurnal Sistem Informasi (JASISFO)
Astuti, Theresia Widji
Politeknik Negeri Sambas
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PORTAL INFORMASI PELAYANAN CALON JAMAAH HAJI BERBASIS WEB PADA KEMENTERIAN AGAMA KABUPATEN SAMBAS Nurbaiti; Widji Astuti, Theresia; Lena, Sonty
Jurnal Sistem Informasi (JASISFO) Vol. 3 No. 2 (2022): September 2022
Publisher : Politeknik Negeri Sriwijaya

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Abstract

The Ministry of Religion of the Sambas Regency is a Vertical Agency of the Ministry of Religion domiciled in the Regency. One of the tasks of the Ministry of Religion of Sambas Regency is to provide services, guidance and guidance in the field of Hajj and Umrah. For services at the Ministry of Religion of Sambas Regency to prospective pilgrims, it is still not effective and efficient. The method used is Prototype with the stages of collecting requirements, building prototyping, evaluating prototyping, coding the system, testing the system, evaluating the system, and using the system. The Web-Based Information Portal for Prospective Hajj Pilgrims Services at the Ministry of Religion of Sambas Regency is built with functionalities according to user needs, namely: information on the schedule of Hajj rituals, information on Hajj travel schedules, information on estimated Hajj departures, information on material for Hajj rituals, information on data for prospective pilgrims, information health data of prospective pilgrims, message information and report information.
Artificial Intelligence-Based Automatic Text Detection System Using Multi-Layer Pattern Recognition Kartika Imam Santoso; Santoso, Kartika; Edi Widodo; Theresia Widji Astuti
Jurnal Transformatika Vol. 23 No. 2 (2026): January 2026
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v23i2.13256

Abstract

The rapid advancement of generative AI models such as ChatGPT, Claude, and Gemini raises serious concerns about the authenticity of academic and professional documents. This study develops a detection system that uses a combination of linguistic, structural, and statistical pattern analysis to identify AI-generated text and classify the responsible AI model. The system analyzes more than 12 different parameters from uploaded documents (PDF, DOCX, TXT formats). The detection engine operates through seven analytical layers: signature detection, linguistic analysis, word pattern analysis, structural analysis, feature pattern analysis, vocabulary and grammar assessment, and AI fingerprinting. The scoring mechanism provides a general AI probability score (0-100%) and individual probability scores for 10 different AI models. In testing with 100 documents, the system achieved 76.8% accuracy in identifying AI-generated text and 87.3% accuracy in classifying the source AI model. Sentence entropy analysis, paragraph uniformity assessment, and distinctive linguistic markers proved most effective. This study demonstrates that multi-layer pattern recognition is a viable approach for detecting and classifying AI-generated text, with implications for academic integrity, content verification, and digital forensics.
Co-Authors Agam, Beryaldi Ahmad Ridho Dian Sari Edi Widodo Fachruddin Fachruddin Fathushahib fathushahib, fathushahib Heldi Hastriyandi Kartika Imam Santoso Kristiandi, Kiki Laipaka, Robertus Lang Jagat Lena, Sonty Marselina, Maya Muhammad Usman, Muhammad Nur, Ria Hayatun Nurbaiti Nurul Fatimah Yunita Rini Fertiasari Santoso, Kartika Santoso, Pande Putu Agus Sanusi Sanusi Soni Soni, Soni Sri Wahyuni Suhendra Vanie Wijaya