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All Journal SWARNA Jurnal Kecerdasan Buatan dan Teknologi Informasi IJIES (International Journal of Innovation in Enterprise System)
Dading Oktaviadi Resmiranta
Universitas Bumigora
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice

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COMPARATIVE ANALYSIS OF RANDOM FOREST AND SUPPORT VECTOR MACHINE FOR FOOD CALORIE LEVEL CLASSIFICATION Dading Oktaviadi Resmiranta; Tanwir; I Gede Yogi Pratama; Naufal Hanif; Azral Satriani; Khairan Marzuki
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.450

Abstract

The rapid escalation of global metabolic health concerns emphasizes the critical urgency for advanced technological solutions that facilitate precise and automated monitoring of daily caloric intake. This research conducts a rigorous comparative analysis to evaluate the predictive performance and computational efficiency of Random Forest (RF) and Support Vector Machine (SVM) algorithms in classifying food calorie levels. The methodology commenced with a comprehensive data preprocessing phase involving multi-strategy missing value imputation and the discretization of caloric values into ordinal categories. Feature selection was meticulously executed using linear regression coefficients to identify high-impact nutritional variables. To ensure a robust evaluation, the dataset was partitioned using an 80:20 ratio for training and testing, complemented by cross-validation to minimize bias and variance. Experimental results indicated that the Random Forest (RF) demonstrated superior classification capabilities, achieving a peak accuracy of 94.8% alongside balanced precision and recall scores. Statistical evaluation via confusion matrices further revealed that Random Forest exhibited enhanced generalization across high-dimensional nutritional features compared to the geometric approach of Support Vector Machine (SVM). Furthermore, the analysis of computational overhead provided critical insights into the real-time deployment feasibility of each model. Ultimately, the findings suggest that the Random Forest serves as a robust engine for personalized dietary management systems, offering a reliable framework for future developments in preventive digital healthcare. By successfully bridging machine learning with nutritional science, this study establishes a benchmark for high-accuracy food classification essential for modern health-centric mobile applications.
Branding and Digital Promotion to Increase Kub Ziae's Competitiveness in the Sembalun Tourism Area Ondi Asroni; Angga Radlisa Samsudin; Dading Oktaviadi Resmiranta; Muhammad Innuddin; Naufal Hanif; Muhammad Tahir
SWARNA: Jurnal Pengabdian Kepada Masyarakat Vol. 5 No. 4 (2026): SWARNA : Jurnal Pengabdian Kepada Masyarakat, April, 2026
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi 45 Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/swarna.v5i4.2000

Abstract

KUB ZIAE Sembalun Bumbung is a souvenir center located on the main tourist route of Sembalun, offering products such as black garlic, organic red rice, Rinjani coffee, and dried beans. However, this business faces challenges including weak brand identity, low visibility, and the absence of digital presence. Limited digital literacy among managers also results in reliance on conventional promotion methods. This community service program aimed to enhance KUB ZIAE’s competitiveness through two key strategies: (1) strengthening offline visual identity with signboards, banners, and product labels; and (2) developing digital assets such as Google Business Profile, Instagram, and Facebook accounts, along with content creation training. A participatory approach was applied, including needs assessment, co-design, intensive training, and on-site mentoring. The results showed improved business visibility with the installation of signboards and banners, while new product labels increased consumer trust. On the digital side, the Google Business account successfully appeared in search results, Instagram posts reached hundreds of accounts, and members gained skills in creating and managing promotional content. Although challenges remain, such as limited digital literacy and internet connectivity, the intervention effectively enhanced visibility, expanded market reach, and strengthened KUB ZIAE’s image as a leading souvenir center in the Rinjani tourism area
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 Angga Radlisa Samsudin Azral Satriani I Gede Yogi Pratama I Putu Hariyadi Khairan marzuki M. Khaerul Ihsan Muhammad Innuddin Muhammad Tahir Naufal Hanif Ondi Asroni Tanwir Tanwir Tanwir