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All Journal JURNAL SISTEM INFORMASI BISNIS Semantik INFOKAM Jurnal Transformatika Scientific Journal of Informatics JIEET (Journal of Information Engineering and Educational Technology) Go Infotech: Jurnal Ilmiah STMIK AUB Transformasi JIPITI: Jurnal Pengabdian kepada Masyarakat Go Infotech: Jurnal Ilmiah STMIK AUB Jurnal Sistem Informasi (JASISFO) Julia Jurnal
Kartika Imam Santoso
STMIK Bina Patria
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Journal : Jurnal Transformatika

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ALGORITMA RANDOM FOREST, DECISION TREE, DAN XGBOOST UNTUK KLASIFIKASI STUNTING PADA BALITA Dhika Malita; DHIKA MALITA PUSPITA ARUM; KARTIKA IMAM SANTOSO; ANDRI TRIYONO; EKO SUPRIYADI; AGUS SUSILO NUGROHO; Widodo, Edi
Jurnal Transformatika Vol. 23 No. 1 (2025): July 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v23i1.12202

Abstract

At the age of toddlers, children need special attention because their brains develop around 80%. Stunting is a form of long-term nutritional deficiency that occurs during the growth and development of children, which are marked with height that is not appropriate or less compared to children their age based on the standard WHO. This condition can adversely affect the cognitive development and health of children. Identifying toddlers who are at risk of experiencing stunting at an early stage is very important to reduce the adverse effects that can affect their quality of life in the future. Traditional methods are less effective in predicting stunting because they often ignore the complex factors that affect the nutritional status of toddlers. This study aims to classify stunting toddlers using Random Forest, Decision Tree, and Extreme Gradient Boost (XGBOOST) algorithms. The results obtained showed that the accuracy of the Random Forest algorithm received the highest accuracy of 99.72 %, Extreme Gradient Boost (XGBOOST) at 99.58 %, and Decision Tree received 98 87 %accuracy.
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 Agus Susilo Nugroho Al Munawar, Habib Riziq Anak Agung Gede Sugianthara Andri Triyono Anggraini, Ani Anjar Septinegara aprilianto, dwi kurniawan Arif Ikhsanudin Arum, Dhika Malita Puspita Chrisna Hidayat Cisilia Sundari Condro Wibowo, Agus Dhika Malita Dhika Malita Puspita Dwi Astuti, Rizky Edi Widodo Edi Widodo, Edi Eko Sediyono Eko Supriyadi Emmanuel, Rheimanda Devin Emmanuel, Rheimanda Devin Emmanuel Estu Tunjung Pambayun Farida Yunita Iis Mariam Kapti . Kisnandhya Putra, Afif Malita Puspita Arum, Dhika Mendi Sangkoyrala Hidayat Muhamad Nur Rais, Muhamad Nur Muhammad Abdul Muin Muhammad Ilyas Musyarof, Muhammad Nabil Nabil, Muhammad Nabil Musyarof Nafi Projo Kusumo, Nafi Projo Najhan Atifa, Akhie Naufal Islam, Nibroos Neda Cisya Tama, Freshma Nugroho, Agus Susilo Prasetyo Nugroho Prihati . Putri Kusumasari, Putri Rahayu, Sari Rodliyati Karima, Isyatin Rosita Erfiyana Saniya, Zahra Fatma Santoso, Kartika Satriya, Ilham Jati Sri Waluyo Sugeng Wahyudion0 Sugeng Wahyudiono, Sugeng Sukris Sutiyatno Sunarni Sunarni Theresia Widji Astuti Triyono, Andri Widorani, Efta Yeza Febriani Yusuf , Hikmaturridho Yusuf Mufarihin, Ahmad