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All Journal JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Chalifa Chazar
Institut Teknologi Nasional Bandung
Religion Humanities Computer Science & IT Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science Nursing Public Health Social Sciences

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Rancang Bangun Sistem Pendaftaran Santri Pondok Pesantren Baitul Hidayah Kota Bandung Chalifa Chazar; Arief Akhmad Fauzi; Mohammad Nurendi; Rakha Dwi Pradifa; Irma Amelia Dewi
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 6 No. 2 (2026): Maret 2026 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/altifani.v6i2.1114

Abstract

Pondok Pesantren Baitul Hidayah menghadapi kesulitan administrasi karena penggunaan formulir manual dan Google Form yang menyebabkan redundansi data dan verifikasi yang lambat. Tujuan penelitian ini adalah untuk membuat sistem pendaftaran santri baru yang terintegrasi dengan internet. Metode Agile digunakan untuk membuat sistem yang menggunakan teknologi Laravel, Nuxt.js, Vue.js, Tailwind CSS, dan MySQL. Portal informasi publik tentang fasilitas dan kegiatan, pendaftaran online, dan manajemen data oleh admin adalah fitur utama. Hasilnya, sistem ini meningkatkan efisiensi manajemen, mengurangi kesalahan pencatatan, dan mempercepat proses verifikasi data santri. 
Penerapan ResNeXt dan Long Short-Term Memory untuk Deteksi Video Deepfake Chalifa Chazar; Firhan Hafiansyah; Milda Gustiana Husada; Uung Ungkawa; Rizka Milandga Milenio
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 2 (2026): May 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.6085

Abstract

Deepfake is a form of facial manipulation in videos that utilizes artificial intelligence-based models to generate highly realistic visual content. The increasing spread of Deepfake has the potential to cause misinformation, manipulate public opinion, and enable the misuse of digital content, making reliable detection systems increasingly necessary. This study develops a face-based Deepfake video detection system by combining spatial and temporal analysis within a unified processing framework. ResNeXt is employed to extract visual facial characteristics from each frame, while Long Short-Term Memory (LSTM) is utilized to learn facial pattern changes across frames in a video sequence. The dataset used is sourced from FaceForensics++, consisting of 1000 original videos and 1000 Deepfake videos. All data are processed through frame extraction and face detection stages. Performance evaluation is conducted using accuracy, precision, recall, and F1-score metrics. The experimental results show that ResNeXt, as the baseline model, achieves 77.67% accuracy and 76.66% F1-score, while integrating LSTM improves system performance to 81.00% accuracy and 80.41% F1-score. These findings indicate that the utilization of temporal information contributes to improved stability and accuracy in face-based Deepfake video detection.
Co-Authors Arief Akhmad Fauzi Firhan Hafiansyah Galih Ashari Rakhmat IRMA AMELIA DEWI La Ode Rafif Setiawan Milda Gustiana Husada Mohammad Nurendi Muhammad Ichwan Rakha Dwi Pradifa Rizka Milandga Milenio UUNG UNGKAWA