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All Journal JSiI (Jurnal Sistem Informasi) JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Pengabdian kepada Masyarakat Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Chalifa Chazar
Institut Teknologi Nasional Bandung
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Nursing Public Health Social Sciences Other

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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.
SIENTONG SEBAGAI MEDIA EDUKASI IOT DAN UPAYA MANAJEMEN KEBERSIHAN PROAKTIF DI LINGKUNGAN SEKOLAH Chalifa Chazar; Mohamad Dedrick Finnegan; Devit Saputra; Freddy Kristian Putra; Oki Ariansyah
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 5 No. 9 (2026): Februari 2026
Publisher : Bajang Institute

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Abstract

Proyek pengabdian kepada masyarakat (PKM) ini menerapkan SiEntong (Sistem Entitas Tong Sampah Pintar Otomatis dan Monitoring Isi Kapasitas Tong Sampah) di SMA BPI 1 Bandung sebagai media edukasi STEM dan manajemen kebersihan proaktif. SiEntong dikembangkan menggunakan mikrokontroler ESP32 dan sensor ultrasonic, dengan data kapasitas sampah ditampilkan secara real-time pada dashboard web. Metode melibatkan pengembangan sistem, implementasi dan evaluasi kualitatif melalui observasi dan diskusi interaktif. Hasil kegiatan menunjukan bahwa sistem SiEntong berfungsi optimal dan efektif meningkatkan pemahaman praktis peserta mengenai konsep IoT. Umpan balik positif diterima terkait efisiensi dan manfaat sistem. Namun, teridentifikasi risiko pengguna awam membuka tutup sampah secara manual karena ketidaktahuan. Keberhasilan edukasi tercapai, tetapi keberlanjutan sistem sangat bergantung pada mitigasi risiko user-centric design dan sosialisasi berkelanjutan.
Analisis Polarisasi Opini BBM Menggunakan GCN dan PMI Chalifa Chazar; Dea Amelia Azzahra; Asep Nana Hermana; Marisa Premitasari
Jurnal Sistem Informasi Vol. 13 No. 1 (2026)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/8pz6gc44

Abstract

This study analyzes the polarization of public opinion regarding the removal of fuel subsidies in Indonesia using a graph-based text classification approch that integrates Pointwise Mutual Information (PMI) and Graph Convolution Networks (GCN). One of the main challenges in sentiment analysis of social media data lies in capturing contextual and semantic relationships within short and noisy texts, which often limits the performance of conventional classifications methods. A dataset consisting of 602 tweets was preprocessed and represented as a graph, where word-word relationships were weighted using PMI and document-word connections were weighted using TF-IDF. Two model configurations were evaluated: a baseline GCN without PMI and a PMI-enhanced GCN, both trained using a 70:15:15 train-validation-test split with indentical hyperparameter settings. Experimental results indicate that the baseline GCN achieved a maximum accuracy of 65%, while the integration of PMI improved performance, yielding the highest the accuracy of 73%. These results demonstrate that incorporating PMI enriches graph representations and enhances the effectiveness of GCN in sentiment classification of political opinion data from social media.
Co-Authors Arief Akhmad Fauzi Asep Nana Hermana Dea Amelia Azzahra Devit Saputra Firhan Hafiansyah Freddy Kristian Putra Galih Ashari Rakhmat IRMA AMELIA DEWI La Ode Rafif Setiawan Marisa Premitasari, Marisa Milda Gustiana Husada Mohamad Dedrick Finnegan Mohammad Nurendi Muhammad Ichwan Oki Ariansyah Rakha Dwi Pradifa Rizka Milandga Milenio UUNG UNGKAWA