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All Journal Media Jurnal Informatika Jurnal Begawe Teknologi Informasi
Pahrul Irfan
Universitas Mataram
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Utilization of Open Source File Sharing Using Nextcloud for Data Exchange at the Archival and Library Office of Mataram City: PEMANFAATAN OPEN SOURCE FILE SHARING MENGGUNAKAN NEXTCLOUD DALAM BERTUKAR DATA PADA DINAS KEARSIPAN DAN PERPUSTAKAAN KOTA MATARAM Muhammad Eysar Assazily; Mohammad Zaenuddin Hamidi; Firmanda Rizky Arinanta; Pahrul Irfan
Jurnal Begawe Teknologi Informasi (JBegaTI) Vol. 7 No. 1 (2026): JBegaTI
Publisher : Program Studi Teknik Informatika, Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jbegati.v7i1.1480

Abstract

Pengelolaan arsip digital di instansi pemerintahan sering menghadapi kendala dalam efisiensi, keamanan, dan keteraturan data. Kegiatan ini bertujuan untuk mengoptimalkan proses pertukaran dan pengelolaan arsip digital di Dinas Kearsipan dan Perpustakaan Kota Mataram melalui pemanfaatan platform open source Nextcloud. Sistem ini diterapkan sebagai solusi file sharing terpusat bernama Kotak Diarpus yang mendukung kolaborasi dan keamanan data antarpegawai. Untuk meningkatkan efisiensi operasional, dikembangkan serangkaian skrip otomatisasi berbasis Python yang diintegrasikan dengan Task Scheduler guna menjalankan proses kompresi, ekstraksi, pencadangan, dan pemantauan aktivitas file secara terjadwal tanpa intervensi manual. Implementasi sistem otomatisasi ini diuji melalui konfigurasi dan pengujian langsung di lingkungan instansi. Hasil menunjukkan bahwa seluruh fungsi berjalan stabil dan efektif dalam menjaga kontinuitas data. Penerapan Nextcloud dan otomatisasi berbasis Python terbukti mampu meningkatkan efisiensi, keamanan, dan keberlanjutan pengelolaan arsip digital, sekaligus meminimalkan risiko kehilangan data. Hasil kegiatan ini diharapkan menjadi dasar pengembangan sistem digital terintegrasi yang lebih andal pada instansi pemerintah.
Local Wisdom-Based Treatment Recommendation System for Tropical Diseases Using Bayesian Network and Association Rule Mining Muhammad Haris Nasri; Rifqi Hammad; Pahrul Irfan; I Nyoman Switrayana; Rahayun Amrullah Husaini
Media Jurnal Informatika Vol 18 No 1 (2026): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v18i1.6290

Abstract

Tropical diseases remain a major public health problem in Indonesia, particularly in regions with limited access to healthcare facilities, leading communities to rely on traditional medicine based on local wisdom. However, the integration of traditional and modern treatment knowledge in intelligent recommendation systems remains limited. This study aimed to develop a tropical disease treatment recommendation system by integrating Bayesian Network (BN) and Association Rule Mining (ARM). Traditional and modern treatment knowledge were collected from scientific literature and expert interviews, validated by medical practitioners and traditional medicine experts, and incorporated into the system. A quantitative and experimental approach was conducted using a dataset of 150 tropical disease cases comprising dengue fever (42 cases), malaria (35), leptospirosis (28), tuberculosis (30), and leprosy (15). The dataset included 47 symptom attributes, 34 traditional treatment attributes, and 12 modern treatment attributes. Bayesian Network was used to model probabilistic relationships among symptoms, diagnoses, and treatments, while the Apriori algorithm in ARM was applied with minimum support and confidence thresholds of 0.3 and 0.7, respectively. Experimental evaluation on 30 testing cases showed that the integrated BN-ARM model achieved 86.7% accuracy and an F1-score of 86.0%, outperforming standalone BN (82.0% accuracy; F1-score 82.5%) and ARM (79.0% accuracy; F1-score 78.8%). The system generated accurate and contextually relevant treatment recommendations by combining local wisdom and modern medical knowledge.
Co-Authors Firmanda Rizky Arinanta I Nyoman Switrayana Mohammad Zaenuddin Hamidi Muhammad Eysar Assazily Muhammad Haris Nasri Rahayun Amrullah Husaini Rifqi Hammad