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All Journal Reputasi: Jurnal Rekayasa Perangkat Lunak IJIES (International Journal of Innovation in Enterprise System)
Tanwir Tanwir
Universitas Bumigora
Computer Science & IT Control & Systems Engineering Engineering

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Implementasi Konsultasi Stunting Balita Menggunakan Large Language Models (LLMs) Tanwir Tanwir; Khasnur Hidjah; Dyah Susilowati
Reputasi: Jurnal Rekayasa Perangkat Lunak Vol. 6 No. 1 (2025): Mei 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/reputasi.v6i1.8961

Abstract

Stunting pada balita merupakan masalah kesehatan kritis di Indonesia yang memerlukan intervensi berbasis teknologi untuk meningkatkan akses informasi nutrisi. Penelitian ini bertujuan mengembangkan chatbot konsultasi stunting berbasis Large Language Models (LLMs) guna menyediakan rekomendasi kesehatan yang akurat dan mudah diakses. Metode yang digunakan berupa Model LLaMA 3 di-fine-tuning menggunakan dataset Q&A spesifik stunting berisi 7.642 entri, kemudian dievaluasi dengan matrik ROUGE untuk mengukur kesesuaian semantik respons. Hasil menunjukkan model Stunting mencapai skor ROUGE-1 (72,24%), ROUGE-2 (64,54%), ROUGE-L (70,42%), dan ROUGE-Lsum (70,96%), secara signifikan melampaui model baseline seperti LLaMA3, Deepseek-R1, dan Mistral. Chatbot diimplementasikan dalam aplikasi web berbasis cloud dengan arsitektur terdistribusi, dilengkapi enkripsi SSL dan HTTPS untuk menjamin keamanan data. Sistem ini memungkinkan interaksi real-time antara pengguna dan model LLMs melalui antarmuka berbasis Gradio. Temuan penelitian mengonfirmasi potensi LLMs dalam menyederhanakan layanan kesehatan preventif, khususnya di daerah dengan sumber daya terbatas
Implementation of Auto-Scaling and Load Balancing on Proxmox VE Using Python and REST API Naufal Hanif; Dading Oktaviadi Resmiranta; M. Khaerul Ihsan; Tanwir Tanwir; I Putu Hariyadi; Ondi Asroni
IJIES (International Journal of Innovation in Enterprise System) Vol 10 No 2 (2026): International Journal of Innovation in Enterprise System - Article in Press
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijies.v10i02.10429

Abstract

Auto-scaling and load balancing are essential for maintaining web service availability and performance under fluctuating workloads. This research implements an automated auto-scaling and load balancing system on Proxmox Virtual Environment (Proxmox VE) leveraging Proxmox REST API through Python scripts. The system monitors web container CPU usage at 5-second intervals, triggering scale-out by cloning LXC containers when average CPU exceeds 75% and scale-in when it falls below 30%, while ensuring at least one container remains active. Testing on a laboratory topology consisting of 1 template container, 1 load balancer, and 4 backend containers (IP range: 192.168.100.251-192.168.100.254) demonstrates average provisioning time of 96.0 seconds (range: 85.5-117.7s) with breakdown: API Clone (82.4s), Network Config (0.0s), Container Start (6.8s), Content Customization (3.1s), and Load Balancer Update (3.6s). Comparative analysis between always-on (4 containers) and auto-scaling scenarios reveals 36.1% CPU savings (31.66 vs 49.57 CPU-hours), idle time reduction from 54.2% to 28.3%, and efficient resource utilization with the system operating predominantly on 1 container (85% uptime). This implementation proves that Proxmox API integration with Python-based automation provides a practical auto-scaling solution for private clouds without complex orchestration platforms like Kubernetes.
Co-Authors Dading Oktaviadi Resmiranta Dyah Susilowati I Putu Hariyadi Khasnur Hidjah M. Khaerul Ihsan Naufal Hanif Ondi Asroni