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All Journal Journal of Information Systems and Informatics Journal of Intelligent Networks and IoT Global
Billanivo, Reynaldi Rizki
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Computer Science & IT

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Prediksi Cuaca Menggunakan Sensor Cahaya, Kecepatan Angin, dan Arah Angin dengan Metode Neuro-Fuzzy Billanivo, Reynaldi Rizki; Fajri, Ricky Maulana; Tasmi; Ferdiansyah
Journal Of Intelligent Networks and IoT Global Vol 3 No 2 (2025)
Publisher : Universitas Indo Global Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jinig.v3i2.6592

Abstract

Prediksi cuaca merupakan elemen yang sangat penting dalam berbagai sektor, termasuk pertanian dan transportasi. Salah satu contohnya adalah membantu petani untuk menunda penanaman tanaman sebelum hujan deras terjadi. Penelitian ini menggunakan metode Neuro-Fuzzy yang dipadukan dengan tiga jenis sensor yakni sensor cahaya, sensor kecepatan angin, dan sensor arah angin, yang digunakan untuk peramalan meteorologi. Penelitian ini memanfaatkan dataset Misol dan dataset BMKG. Model Neuro-Fuzzy mencapai akurasi rata-rata sebesar 0,71, presisi 0,4075, recall 0,3185, dan skor F1 sebesar 0,337.
Forensic Analysis of AI-Generated Image Alterations Using Metadata Evaluation, ELA, and Noise Pattern Analysis Ferdiansyah, Ferdiansyah; Deazwara, Muhammad Rizki Akbar; Billanivo, Reynaldi Rizki; Ardiansyah, M.; Ilham, Ilham
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1362

Abstract

This study develops a forensic workflow to assess the authenticity of digital images, addressing the challenge of distinguishing AI-generated content from real photographs. The goal is to analyze metadata, compression behavior, and noise characteristics to identify synthetic images. The dataset includes eight images: two original Xiaomi 14T Pro photos and six AI-generated variants from Gemini, ChatGPT, and Copilot. Metadata was extracted using ExifTool version 13.25 on Kali Linux, while Error Level Analysis (ELA) and Noise Pattern Analysis (NPA) were performed with consistent parameters on the Forensically platform. Authentic images displayed complete EXIF metadata, uniform compression patterns, and stochastic sensor noise. In contrast, AI-generated images lacked EXIF data, included XMP or C2PA provenance, exhibited localized compression anomalies, and showed smoother, more structured noise patterns. The study presents a practical and reproducible forensic workflow that integrates metadata evaluation, ELA, and noise analysis to detect synthetic content. The findings demonstrate that despite their visual realism, AI-generated images still leave detectable forensic traces, offering valuable tools for image authenticity verification.
Co-Authors Deazwara, Muhammad Rizki Akbar Ferdiansyah Ferdiansyah Ferdiansyah Ilham Ilham M. Ardiansyah Ricky Maulana Fajri Tasmi