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INDONESIA
IJIES (International Journal of Innovation in Enterprise System)
Published by Universitas Telkom
ISSN : -     EISSN : 25803050     DOI : https://doi.org/10.25124/
Core Subject : Engineering,
Control & Systems Engineering Engineering
International Journal of Innovation in Enterprise Systems (IJIES) is a peer-reviewed international journal which publishes original articles of significant value in all areas of innovation in Enterprise Systems. The journal covers research articles, research-in-brief, and review articles comprising all discipline related to Enterprise Systems, particularly Information Systems & Technology, and the relevant multidisciplinary domain such as Industrial Engineering and Management. The journal welcomes relevant publishable articles including technical innovation, practical IS adoption, management innovation, etc, which is considered as high valued work in this area.
Arjuna Subject : Teknik (semua kategori) - Tenik Industri dan Manufaktur
Articles 15 Documents
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Diagnosing Object-Oriented Programming Difficulties: Association Rule Mining and Block-Based Scaffolding Andre Rangga Gintara; Lala Septem Riza; Wahyudin
IJIES (International Journal of Innovation in Enterprise System) Vol 10 No 1 (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.v10i01.10530

Abstract

Logical thinking is fundamental for Object-Oriented Programming, yet vocational students frequently struggle with its abstract concepts. This study aims to overcome these cognitive barriers through a data-driven diagnostic approach and targeted intervention. Utilizing Educational Data Mining techniques, specifically Association Rule Mining, this research analyzed pretest patterns to map learning difficulties among 35 software engineering students in Indonesia. A Research and Development method with a One-Group Pretest-Posttest design was employed. The analysis revealed a "cognitive domino effect," identifying that failures in advanced topics are rooted in specific prerequisite weaknesses. Based on these findings, a remedial intervention using a Scaffolding model assisted by Block-Based Programming was implemented. The results demonstrated that this data-driven intervention significantly enhanced students' logical thinking skills (p<0.001), with a substantial increase in N-Gain scores. It is concluded that integrating algorithmic diagnosis with visual scaffolding effectively bridges the gap between abstract concepts and practical coding skills, offering a scalable model for vocational education.
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.
Diagnosing Defects and Waste in an SME Bakery Using Lean Six Sigma Anggun Nindy Fatliana; Muhammad Hudzaly Hatala; Nuraini Rahmad; Vira Aulia
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.v10i02.10514

Abstract

Product-quality problems create material losses and operational inefficiencies for resource-constrained food SMEs. This study diagnoses product defects and production waste at Ronalee Bakery, an Indonesian bakery SME, using selected Lean Six Sigma tools. Aggregate production and defective unit records from January to March 2024 were analyzed together with process observations and operational accounts. The DMAIC framework was used to organize the diagnostic process: the Define, Measure, and Analyze stages were applied to identify problems and their potential causes, whereas the Improve and Control stages were limited to proposed actions. Of the 101,421 units produced, 3,367 were recorded as defective, corresponding to an aggregate defective-product rate of 3.32%, which exceeded the company’s target of 2%. The assessment identified burnt bread, under-risen bread, torn or hollow texture, filling leakage, and non-standard weight, along with several relevant waste categories, including waiting, overproduction, overprocessing, inventory, and transportation. Potential causes were associated with operator capability, raw material and formulation consistency, oven condition, production planning, and facility layout. Proposed actions include standardized operating procedures, operator training, preventive maintenance, demand-based production planning, and layout adjustments. These findings provide a structured diagnostic basis for the future implementation and evaluation of quality-improvement actions in resource constrained bakery SMEs.
Random Forest-Based AI Decision Support System for Palm Oil Production Planning Nazli Rahmeisi
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.v10i02.10771

Abstract

Palm oil milling operations are frequently affected by fluctuating fresh fruit bunch (FFB) availability, variations in Oil Extraction Rate (OER), and limitations in production planning under dynamic operational and environmental conditions. In many mills, production decisions still rely heavily on operator experience and manually recorded information, resulting in inconsistent and less structured planning processes. This study develops an interpretable Artificial Intelligence-based Decision Support System (AI-DSS) that integrates a Random Forest Regression model into a web-based decision support platform to support enterprise-level palm oil production planning. The predictive model was developed using operational and environmental data collected from a palm oil mill and evaluated through 10-fold cross-validation. The proposed Random Forest Regression model achieved an average coefficient of determination (R²) of 0.94 ± 0.02 and an average Root Mean Square Error (RMSE) of 1.06 ± 0.09, demonstrating strong predictive capability for modeling nonlinear relationships among production variables. The trained Random Forest model was integrated into a web-based decision support framework that transforms predicted OER values into structured operational recommendations while preserving managerial decision-making. Feature importance analysis further enhances system interpretability by identifying Fresh Fruit Bunch (FFB) intake and operational hours as the most influential variables affecting OER. The proposed AI-DSS demonstrates the practical feasibility of integrating predictive analytics, explainable machine learning, and enterprise-level decision support to facilitate more structured, transparent, and data-driven production planning in palm oil mill operations.
From Ergonomic Design to Intelligent Healthcare: A Bibliometric Analysis of Elderly‑Oriented Research Yusuf Nugroho Doyo Yekti; Ilma Mufidah; Dino Caesaron; Murman Dwi Prasetio
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.v10i02.10881

Abstract

The purpose of this study is to provide a bibliometric perspective on the research landscape of ergonomic equipment for the elderly, with particular attention to publication trends, collaboration networks, and thematic evolution. Publications indexed in the Scopus database between 2016 and 2026 were systematically identified and screened in accordance with the PRISMA framework. The analysis evaluates publication growth, leading journals and countries, citation performance, and emerging research themes. Bibliometrix (R package) and VOSviewer were employed to examine the intellectual structure of the literature and explore the evolution of its research themes. The results demonstrate sustained growth in publication activity throughout the study period, reflecting increasing attention to ergonomic approaches for supporting healthy aging. Applied Ergonomics is identified as the most productive journal in this area, while the United States and China remain the dominant contributors to research activity in the field. The thematic evolution analysis highlights a noticeable transition in research priorities, from early emphasis on elderly care, universal design, and ergonomic design to broader ergonomic-oriented studies, and more recently toward advanced technology-based applications, including artificial intelligence and virtual reality. The findings highlight the need for future studies to focus on innovative healthcare technologies for older adults, while ensuring that ergonomic and human-centered design considerations remain integral to their development. The insights presented in this study may assist scholars, policy stakeholders, and practitioners in navigating the evolving knowledge landscape while encouraging sustainable innovation for aging populations.

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