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TELKOMNIKA (Telecommunication Computing Electronics and Control)
Published by Universitas Ahmad Dahlan
ISSN : 16936930     EISSN : 23029293     DOI : 10.12928
Core Subject : Science,
Computer Science & IT
Submitted papers are evaluated by anonymous referees by single blind peer review for contribution, originality, relevance, and presentation. The Editor shall inform you of the results of the review as soon as possible, hopefully in 10 weeks. Please notice that because of the great number of submissions that TELKOMNIKA has received during the last few months the duration of the review process can be up to 14 weeks. Communication Engineering, Computer Network and System Engineering, Computer Science and Information System, Machine Learning, AI and Soft Computing, Signal, Image and Video Processing, Electronics Engineering, Electrical Power Engineering, Power Electronics and Drives, Instrumentation and Control Engineering, Internet of Things (IoT)
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 3,484 Documents
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Design and implementation of an IoT-based PLC-HMI trainer for pneumatic control system education Chairul Gagarin Irianto; Singgi Fadly; Endang Djuana; Dianing Novita Nurmala Putri; Tyas Kartika Sari; Gema Takbir Yasir
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27815

Abstract

Industrial automation education requires practical learning media that integrate control systems, visualization, and physical processes. However, many pneumatic training systems used in laboratories still operate as standalone devices and lack system integration and remote monitoring capability. This study presents the design and implementation of an internet of things (IoT)-based programmable logic controller (PLC) and human-machine interface (HMI) trainer for pneumatic control education. The proposed platform integrates PLC control, HMI visualization, pneumatic actuators, and IoT monitoring within a unified learning environment. A modular PLC software architecture was developed to support multiple pneumatic learning scenarios using a single control program. Functional testing was conducted to verify system reliability, actuator operation, and communication performance. An educational evaluation using pre-test and post-test assessments was also performed. The results show that the developed trainer operates according to the intended design specifications and improves student understanding. The average student score increased from 62 in the pre-test to 84 in the post-test, representing an improvement of approximately 35.5% in learning performance. These results demonstrate that the proposed PLC–HMI–IoT trainer effectively supports practical learning in pneumatic automation education.
Analysis of software-defined network on Proxmox with quality of-service testing using Open vSwitch Shafira Febriani; Riezkan Aprianda Firmansyah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27549

Abstract

This study designs and implements a virtual network based on software defined networking (SDN) in the Proxmox Virtual Environment (VE) using Open vSwitch (OVS) and OpenDaylight (ODL) controller, with performance testing of quality of service (QoS). The parameters tested include rate limiter, throughput, packet loss, and forwarding rate. Proxmox VE is selected for its centralized virtualization and clustering capabilities, while OVS manages network traffic among virtual machines (VMs). ODL is chosen for its compatibility with OpenFlow and OVS database (OVSDB) protocols, essential for OVS management. Testing uses iPerf3 to generate network traffic and analyze performance, including database service access simulation to mimic typical cloud application traffic loads. Forwarding rate measures the system’s packet forwarding capability per unit time, providing a comprehensive network performance overview. Results contribute to optimized SDN-based virtual network implementations in Proxmox environments and provide scientific and practical references for developing QoS-based virtual networking systems.
Machine learning-based joint optimization for LoRaWAN reliability and energy efficiency Nana Rusdiana; Novi Indriyani; Adi Nugroho; Riri Fitri Sari; Dino Budi Prakoso
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27916

Abstract

Dense long-range wide area network (LoRaWAN) deployments often experience reduced packet delivery reliability (PDR) and increased energy consumption due to suboptimal adaptive data rate (ADR) mechanisms. This study proposes a machine learning (ML) based joint optimization method to improve PDR or reliability while minimizing node energy consumption. The proposed solution employs random forest (RF) and light gradient boosting machine (LightGBM) models, which are suitable for resource constrained end devices (ED). Using combinations of data rate (DR), frequency channel, and transmission power (TxPower) from 500 ED, the model extracts 14 features for parameter optimization. Unlike conventional ADR, the proposed approach can learn complex relationships beyond the received signal strength indicator (RSSI) and signal-to-noise ratio (SNR) parameters. The model achieves higher prediction accuracy within a 5 km coverage area. Network simulator 3 (NS-3) simulation results demonstrate better performance and energy efficiency compared to conventional ADR. The proposed approach improves PDR by 6.1–11.6% and reduces node energy consumption by 20–25%, providing a better balance between communication reliability and energy efficiency in scalable LoRaWAN networks.
A unified artificial intelligence-enabled intelligent home assistant robot for smart automation and safety Md. Taslim Arif; Abu Salyh Muhammad Mussa; Jul Jalal Al-Mamur Sayor; Md. Ashiqussalehin; Md. Mehedi Hasan; Mst Deloara Khushi; Md. Moshiur Rahman
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27862

Abstract

Intelligent home assistant systems are increasingly required to enhance safety, efficiency, and user convenience in smart environments. This paper presents LUNA, a unified artificial intelligence (AI)-enabled home assistant robot de signed for smart automation and safety in indoor settings. The system integrates autonomous navigation, You Only LookOnceversion8(YOLOv8)-basedobject detection, gesture-based interaction, real-time gas and fire hazard detection, mo bile application control, and automated floor cleaning within a single low-cost embedded platform. Unlike existing systems that focus on isolated functional ities, the proposed approach combines perception, mobility, safety monitoring, and user interaction in a coordinated framework suitable for real-world appli cations, particularly for elderly and disabled users. The system is implemented using Arduino, Raspberry Pi, and ESP32 to enable efficient sensing, processing, communication, and actuation. Experimental results demonstrate robust perfor mance, achieving 93.5% navigation accuracy, 91.2% object detection accuracy, 2.5 s emergency response time, 1.2 s control latency, and 88.7% cleaning effi ciency. These results indicate that LUNA is an effective and practical solution for next-generation smart home assistance.

Page 349 of 349 | Total Record : 3484

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