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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Minda Baharu Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Info Sains : Informatika dan Sains Academic Journal of Computer Science Research (AJCSR) sudo Jurnal Teknik Informatika Blend Sains Jurnal Teknik Wahana Abdi Implementasi Pancasila: Jurnal Pengabdian kepada Masyarakat
Satriawan Desmana
Politeknik Negeri Cilacap
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences Other

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Implementing Proxmox VE-Based High Availability Clustering with Ceph Replication and Performance Testing for Resilient IT Infrastructure in High-Risk Disaster Areas Muin, Muhammad Abdul; Trianto, Rahmawan Bagus; Faiz, Muhammad Nur; Maharrani, Ratih Hafsarah; Desmana, Satriawan
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5591

Abstract

IT infrastructure in disaster-prone areas, particularly along Java's southern coastal region within the Sunda Arc subduction zone, faces significant vulnerability to seismic events and tsunamis that cause critical system downtime, disrupting emergency coordination and exacerbating disaster impacts. This study aims to develop and validate an open-source High Availability (HA) solution using Proxmox Virtual Environment (PVE) ensuring service continuity with Recovery Time Objective (RTO) under 2 minutes and near-zero Recovery Point Objective (RPO). The methodology encompasses four systematic stages: needs analysis identifying infrastructure requirements and disaster risk assessment for Cilacap region; architecture design implementing three-node PVE cluster with Ceph distributed storage (replication factor 3) and Corosync quorum mechanism; system implementation including network bonding, VLAN segmentation, and dedicated 1Gbps Ceph replication network; and comprehensive performance testing through fault injection scenarios (power-off simulation, network partition, storage failure) measuring inter-node latency, disk I/O performance, and failover recovery metrics. Results demonstrate exceptional reliability with 99.92% availability over 72-hour monitoring, Mean Time Between Failures (MTBF) of 24.1 hours, and Mean Time To Recovery (MTTR) of 70 seconds with total downtime of 3.53 minutes across three failover simulations. Inter-node latency remains below 1ms (average 0.372-0.593ms), while disk I/O latency maintains sub-0.5ms performance during failover events. This research contributes to computer science and disaster informatics by providing a validated, replicable open-source blueprint for resilient IT infrastructure in Indonesia's disaster-prone regions, offering practical implementation pathways for integration with national emergency systems including BNPB coordination networks and BMKG early warning infrastructure.
Deteksi Kantuk Pengemudi Berdasarkan Keterbukaan Mata Menggunakan Model Ringan dari Spatiotemporal Pyramidal CNN Angga Maulana Purba; Fajar Mahardika; Satriawan Desmana; Nur Moniroh
sudo Jurnal Teknik Informatika Vol. 4 No. 4 (2025): Edisi Desember
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/sudo.v4i4.1342

Abstract

Keselamatan berkendara merupakan hal yang penting dan menjadi topik yang krusial diperbincangkan termasuk mendeteksi kantuk pengemudi. Dalam Computer Vision salah satu pendekatan yang dilakukan adalah mendeteksi kedipan mata pengemudi. Penelitian tahun 2022 menunjukkan hasil yang baik dalam penggunaan Pyramidal Bottleneck CNN untuk mempelajari fitur spatio dan temporal pada kedipan mata. Kemudian tahun 2023 dikembangkan model yang lebih ringan dengan Depth-wise Separable Convolution, sehingga parameter latih bisa diperkecil. Oleh karena itu, Penelitian ini bertujuan mengadaptasi arsitektur tersebut untuk kasus keterbukaan mata. Kedipan mata berlangsung cukup singkat dan hanya sepersekian detik, sehingga belum cukup membantu untuk mendeteksi kantuk. Model tersebut berhasil dilatih pada data primer yang terbatas dan dibandingkan dengan model baseline. Model terbaik secara keseluruhan menghasilkan F1 score 0.75, Precision 0.63, dan Recall 0.93. Model tersebut bisa berjalan diatas CPU dengan rata-rata 12 FPS (Frame Per Second). Hasil recall yang cukup tinggi menunjukkan model tersebut bisa menangkap momen mata tertutup cukup banyak, hal ini cukup krusial karena kehilangan momen mata tertutup akan fatal akibatnya, meskipun harus mengorbankan precision atau masih tinggi false positive-nya.
Co-Authors Abdul Rohman Supriyono Ahmad Rifqi Angga Maulana Purba Arie Gunawan Bella Adinda Putri Deny Hidayatullah Dhema, Salestinus Petrus Dhieka Avrilia Lantana Djarot Hindarto Endah Tri Esti Handayani Eri Mardiani Fachry, Fachry Fajar Mahardika Fauziah Fauziah Fauziah Frenda Farahdinna Hindarto, Djarot Krisna Nuresa Qodri Lili Dwi Yulianto Linda Perdana Wanti Maharrani, Ratih Hafsarah Mardiani, Eri Muhammad Abdul Mu'in Muhammad Abdul Muin Muhammad Abdul Muin Muhammad Nur Faiz Muhammad Nur Faiz, Muhammad Nur Nur Moniroh Nur Rahmansyah Nurfaiz, Kelfin Oman Somantri Perdana, Muhammad Rizky Prasetyo, Yoga Dwi Putro, Prayogo Dwi Cahyo Rahmansyah, Nur Rahmawan Bagus Trianto Ratih Hafsarah Maharrani Ratih Ratih Sari Ningsih Suhatmojo, Guing Tri Sutikman, Sutikman Winarsih Winarsih Yulianto, Lili Dwi