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Design and implementation of an IoT-based PLC-HMI trainer for pneumatic control system education

10.12928/telkomnika.v24i5.27815
Chairul Gagarin; Universitas Trisakti Irianto , Singgi; Universitas Trisakti Fadly , Endang; Universitas Trisakti Djuana , Dianing; Universitas Trisakti Novita Nurmala Putri , Tyas; Universitas Trisakti Kartika Sari , Gema; Universitas Trisakti Takbir Yasir
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.
Volume: 24
Issue: 5
Page: 1779-1794
Publish at: 2026-10-01

Internet of things-based smart irrigation system using soil moisture and weather data

10.12928/telkomnika.v24i5.27889
Zakarie; Universiti Teknologi Malaysia (UTM) Abdi Mohamud , Rozeha; Universiti Teknologi Malaysia (UTM) Binti A. Rashid , Yazid; Universiti Teknologi Malaysia (UTM) Abubakar Sufyan
Water wastage in agriculture remains a significant challenge due to irrigation practices that often rely on fixed schedules rather than actual field conditions. This study presents an internet of things (IoT)-based smart irrigation system designed to improve water-use efficiency through real-time monitoring and automated irrigation control. The system integrates a capacitive soil moisture sensor with weather information obtained from an online application programming interface (API), while all data processing is performed locally on a Raspberry Pi edge device. A rule-based decision mechanism is used to classify soil conditions into dry, optimal, and wet categories and to determine appropriate irrigation actions based on soil moisture levels and rainfall forecasts. The system was implemented using low-cost and readily available components and tested under controlled conditions with soil moisture levels ranging from approximately 0% to above 85%. Experimental results showed consistent classification of critical dry, optimal, and critical wet conditions, enabling appropriate irrigation responses under different scenarios. In addition, email notifications were generated only during critical conditions, while no alerts were triggered under optimal moisture levels, demonstrating stable and reliable operation. The proposed system provides a practical and cost-effective solution for supporting efficient irrigation management and sustainable agricultural practices.
Volume: 24
Issue: 5
Page: 1690-1698
Publish at: 2026-10-01

A unified artificial intelligence-enabled intelligent home assistant robot for smart automation and safety

10.12928/telkomnika.v24i5.27862
Md. Taslim; University of Frontier Technology Bangladesh (UFTB) Arif , Abu Salyh; University of Frontier Technology Bangladesh (UFTB) Muhammad Mussa , Jul Jalal; Daffodil International University (DIU) Al-Mamur Sayor , Md.; University of Frontier Technology Bangladesh (UFTB) Ashiqussalehin , Md. Mehedi; University of Frontier Technology Bangladesh (UFTB) Hasan , Mst Deloara; University of Frontier Technology Bangladesh (UFTB) Khushi , Md. Moshiur; University of Frontier Technology Bangladesh (UFTB) Rahman
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.
Volume: 24
Issue: 5
Page: 1680-1689
Publish at: 2026-10-01

Leveraging AI and pedagogical competence to enhance learning interest and outcomes: evidence from Islamic university

10.11591/ijai.v15.i5.pp4001-4014
Cecep Wahyu Hoerudin , Susanto Susanto , Apri Wardana Ritonga , Ayu Desrani , Darwis Hude , Dzaki Aflah Zamani , Ahmad Ari Masyhuri
This study investigates the effects of artificial intelligence (AI)-supported intelligent educational systems and teacher pedagogical competence (TPC) on students’ learning interest (LI) and learning outcomes (LO) in an Islamic higher education context. AI is operationalized as AI-supported learning system features embedded in institutional learning platforms, particularly natural language processing (NLP)-based automated feedback and AI-driven recommendation functionalities that support adaptive and personalized learning. The study was conducted at the As-Syifa Institute, Subang, Indonesia, involving 366 undergraduate students from the first, third, and fifth semesters. Data were analyzed using partial least squares-structural equation modeling (PLS-SEM) with SmartPLS 4 to examine the structural relationships among AI system use, TPC, LI, and LO. The results demonstrate that AI-supported learning system features have significant positive effects on both LI and LO. TPC also shows a strong direct effect and mediates the relationship between AI-supported systems and student outcomes. These findings highlight the importance of human-AI integration, indicating that pedagogical competence plays a critical role in translating AI system functionalities into meaningful educational benefits. This study contributes to the AI-in-education literature by providing empirical evidence on pedagogically mediated AI-supported learning systems and offers insights for the design and evaluation of adaptive and intelligent learning environments in higher education.
Volume: 15
Issue: 5
Page: 4001-4014
Publish at: 2026-10-01

Predictive and fault-tolerant virtual machine migration for energy-efficient cloud data centers

10.12928/telkomnika.v24i5.27706
Rakshan; Sri Venkateswara College of Engineering G. K. , P.; Sri Venkateswara College of Engineering Vinothiyalakshmi
Virtual machine (VM) migration is a key mechanism for improving energy efficiency, service continuity and reliability in cloud data centers. However, conventional migration strategies are largely reactive and fail to account for workload fluctuations and potential failures, often resulting in inefficient resource utilization and increased service-level agreement (SLA) violations. This paper proposes the predictive VM migration manager (PVMM), a unified framework that integrates workload forecasting and failure prediction for proactive migration control. PVMM combines a gated recurrent unit (GRU)-based model for short-term resource prediction with a machine-learning-based failure predictor to identify high-risk VMs. An adaptive dynamic threshold based on energy consumption (ADT-EC) detects host overload conditions, while an improved energy-aware best-fit (IEABF) heuristic selects optimal migration targets. Experimental results demonstrate that PVMM reduces unnecessary migration, improves SLA compliance and enhances overall energy efficiency and system reliability. These findings highlight the effectiveness of predictive, multi-objective migration strategies for next-generation cloud data center management.
Volume: 24
Issue: 5
Page: 1513-1525
Publish at: 2026-10-01

Hybrid neural network-augmented model predictive control for differential-drive robot tracking

10.12928/telkomnika.v24i5.27870
Taoufik; University Sidi Mohammed Ben Abdellah Belkebir , Hicham; Sidi Mohamed Ben Abdellah University Belkebir , Anass; Sidi Mohamed Ben Abdellah University Mansouri
Nonlinear model predictive control (NMPC) is widely adopted for mobile robot trajectory tracking, yet the interaction between transcription methods and learning-based enhancements remains unstudied for differential-drive platforms. This paper presents a unified CasADi/Interior Point OPTimizer (IPOPT) evaluation of fourth-order Runge-Kutta (RK4) multiple shooting (MS) and Hermite-Simpson (HS) direct collocation on a TurtleBot3 Burger across ten scenarios spanning five trajectory families, model mismatch, and sensor noise. A 𝜋-ambiguity in the standard 𝑠𝑖𝑛2 (𝛥𝜃) heading cost is identified and corrected, reducing figure-eight tracking error by approximately 95%. RK4 MS achieves superior baseline accuracy in 9 of 10 experiments (𝑝 < 0.01). Three hybrid neural approaches are evaluated: warm-starting reduces solve time by 16% with no accuracy loss; adaptive transcription selection outperforms the baseline in 3 of 10 experiments; and neural approximate control achieves sub-millisecond inference but degrades on unseen trajectories (51%-64% fallback rate). A generalization hierarchy emerges: solver-in-the-loop methods generalize without measurable degradation, whereas solver-replacement methods require trajectory-specific training. These findings suggest that solver involvement is a key factor influencing generalization in learning-augmented predictive control. All results are statistically validated across 500 simulations.
Volume: 24
Issue: 5
Page: 1768-1778
Publish at: 2026-10-01

Development of a T-instrument for soil resistivity measurement in agricultural applications

10.12928/telkomnika.v24i5.27943
Tedy; Institut Teknologi Bandung Setiawan , Satria; Institut Teknologi Bandung Bijaksana , Ulvienin; Institut Teknologi Bandung Harlianti
Geoelectric measurements are widely applied in natural resource exploration and groundwater studies. In this work, a prototype resistivity meter, referred to as the T-instrument, was developed for soil resistivity measurements in agricultural applications. The system is designed with an emphasis on portability, ease of operation, and field applicability. Measurements were conducted using the Wenner electrode configuration, and system performance was evaluated through comparison with a commercial resistivity meter (OYO McOhm). The results show a strong correlation coefficient of 0.96 between the two instruments, indicating good agreement. The instrument performs reliably within typical agricultural soil resistivity ranges and targets shallow soil depths relevant to crop root zones. However, further refinement is required to improve performance under high-resistivity conditions and optimize power efficiency.
Volume: 24
Issue: 5
Page: 1745-1754
Publish at: 2026-10-01

Temporal convolutional network for Indonesian word by word sign language translator on MediaPipe framework

10.11591/ijai.v15.i5.pp4081-4095
Sarah Purnamawati , Albert Albert , Anggi Yohanes Pardede , Iqbal Fakhriza , Farhad Nadi , Mohammad Fadly Syahputra , Romi Fadillah Rahmat
Sign language is a language created to help the deaf-mute communicate with others. In Indonesia, there are two sign languages: Indonesian sign language system or sistem isyarat bahasa Indonesia (SIBI) and Indonesian sign language or bahasa isyarat Indonesia (BISINDO), but the deaf community prefers to use BISINDO. Deaf-mute people use sign language to communicate, but normal people usually avoid learning it because they feel it's unnecessary for daily communication. This will certainly be a problem for the deaf-mute because people who do not learn sign language will certainly have difficulty understanding what they are trying to convey. Therefore, an application that can translate sign language is needed to help overcome communication problems between the deaf-mute and normal people. This research focuses on BISINDO translation using a temporal convolutional network (TCN) with MediaPipe methods. The amount of data used consists of 2,700 videos representing 27 word-level signs in BISINDO; this number increases to 32,400 after undergoing the data augmentation process. The model built was tested in translating BISINDO at the word level in simulation, and a thorough comparison between TCN and bidirectional long short-term memory (BiLSTM); both of them were running on the MediaPipe platform separately. From the experiment, it is shown that the performance of TCN exceeds BiLSTM with an accuracy rate as high as 88.64%.
Volume: 15
Issue: 5
Page: 4081-4095
Publish at: 2026-10-01

Generative artificial intelligence personalization and consumer behavior analytics

10.11591/ijai.v15.i5.pp4919-4932
Endang Tjahjaningsih , Elen Puspitasari , Felix Andreas Sutanto , Dewi Handayani Untari Ningsih , Dwi Budi Santoso
Batik micro, small and medium enterprises (MSMEs) sustain Indonesia's textile heritage and local livelihoods but struggle with customer retention due to conventional marketing and limited artificial intelligence (AI) adoption. The shift to digital commerce requires affordable, culturally relevant AI strategies. This study proposes an integrated framework combining recency, frequency, monetary (RFM) analytics, K-means clustering (K = 5), random forest churn prediction, and transformer-based generative AI for personalized promotions delivered via WhatsApp business API. Evaluated over 12 months with 20 batik MSMEs in Central Java (300 customers, 873 transactions), the framework employed temporal data splitting (9 months training, 3 months testing) and A/B testing against generic campaigns. Human evaluation assessed message relevance and cultural appropriateness, while all data were anonymized with informed consent. Results show improvements of +17% in retention prediction accuracy, +23% in click-through rate, and +21% in purchase frequency versus baselines (p < 0.05, Cohen's d = 0.68, power > 0.8). The system costs below USD 20 per MSME per month, offering a scalable, human-centered pathway to strengthen loyalty in emerging markets.
Volume: 15
Issue: 5
Page: 4919-4932
Publish at: 2026-10-01

Engineering orchestration: adaptive and human-centred design for complexity, collaboration, and resilience

10.12928/telkomnika.v24i5.3777
Tole; Universitas Ahmad Dahlan Sutikno
Engineering design is undergoing a profound transformation as electrical and computer engineering systems become increasingly interconnected, intelligent, and embedded within dynamic socio-technical environments. Building upon systems thinking, future engineering can no longer rely solely on deterministic optimization or fixed design assumptions but must instead embrace adaptive, resilient, and human-centred approaches capable of responding to uncertainty and continuous change. This editorial argues that these developments fundamentally redefine engineering design as the orchestration of adaptive socio-technical systems that continuously align technological intelligence, human expertise, resilience, and lifecycle evolution. Adaptive engineering extends traditional design principles through continuous learning, feedback, and system reconfiguration, while human centred engineering ensures that technological capabilities remain aligned with human judgement, values, and operational needs. Resilience further enables engineering systems to anticipate, absorb, recover from, and adapt to evolving disturbances across increasingly complex operating environments. Engineering excellence therefore depends not merely on optimizing technological performance but on orchestrating these complementary capabilities into coherent engineering ecosystems capable of generating enduring value. This evolving perspective establishes a conceptual foundation for the future of electrical and computer engineering, where engineering orchestration enables adaptive design, human collaboration, resilient innovation, and ultimately responsible and sustainable engineering practice.
Volume: 24
Issue: 5
Page: 1449-1456
Publish at: 2026-10-01

Contour-guided convexity defect geometry for precise ROI extraction in dorsal hand vein recognition

10.11591/ijece.v16i5.pp2622-2640
Habib Kadem , Merah Mostefa
Dorsal hand vein biometrics depend strongly on accurate region-of-interest (ROI) extraction, yet conventional convexity-defect-based methods remain vulnerable to contour noise, skin texture, and shadow-induced artifacts. This paper introduces contour-guided convexity defect geometry (CG-CDG), a geometry-driven ROI extraction framework in which a candidate convexity defect is accepted as an anatomical valley only if it jointly satisfies two independent criteria: chord-contour topological consistency, ensuring anatomical relevance, and a minimum enclosed-area threshold, ensuring physical plausibility. Unlike prior convexity-defect-based methods, which accept a candidate valley on the basis of a single depth- or angle-based criterion evaluated in isolation, CG-CDG applies this joint plausibility test before defining the region of interest, a square ROI centered on the hand centroid and aligned with the dominant valley orientation. Experiments on a public dorsal hand vein database of 1,024 images from 138 subjects show that, combined with HOG descriptors and City Block distance, CG-CDG achieves an equal error rate (EER) of 0.0266, with a 95% bootstrap confidence interval of [0.0241, 0.0289] over the matching scores (B = 1,000 resamples), the narrowest interval among the four ROI extraction methods compared under the primary evaluation setting. A post-hoc Wilcoxon signed-rank test with Bonferroni correction confirms that this improvement is statistically significant over all competing methods (p < 0.001). On this dataset, geometric validation substantially reduces intra-class variance and yields statistically validated performance without requiring any training data, making CG-CDG a strong candidate both as a standalone, training-free biometric solution and as an anatomically grounded preprocessing stage for hybrid deep-learning pipelines.
Volume: 16
Issue: 5
Page: 2622-2640
Publish at: 2026-10-01

Robust resource allocation in multi-cell UE-specific RIS-assisted D2D relay networks under imperfect CSI

10.11591/ijece.v16i5.pp2575-2594
Kayode Popoola , Ayodeji Ajani , Stuart Nicholson , Muheeb Ahmed , Srilatha Narayangari Pamuri , Ibrahim Bala Alhassan
Device-to-device (D2D) communication enhances spectral efficiency but remains constrained by limited transmission range, underlay interference, and the half-duplex overhead of conventional relays. User equipment-specific reconfigurable intelligent surfaces (UE-RIS) offer a promising alternative by enabling passive beamforming to strengthen D2D links without additional spectrum consumption. However, existing studies typically assume perfect channel state information (CSI) and single-cell operation, limiting their applicability to practical deployments. This paper proposes a robust multi-cell resource allocation (RMRA) framework for UE-RIS-assisted D2D relay networks under imperfect CSI. A hybrid uncertainty model is adopted, combining statistical Gauss-Markov CSI errors for intra-cell links with bounded norm-ball uncertainty for inter-cell links. The joint optimisation of resource reuse, transmit power allocation, and RIS phase configuration is formulated as a stochastic mixed-integer nonlinear program that maximises network spectral efficiency while satisfying outage and quality-of-service constraints. To efficiently solve the problem, a three-stage algorithm is proposed comprising distance-pruned Hungarian assignment, robust power control using Bernstein-type inequality and S-procedure based semidefinite programming, and soft actor-critic (SAC) based passive beamforming. Simulation results show that RMRA achieves a 94% D2D access rate at light load and over 75% at full load, improves sum spectral efficiency by 34.7% and 70.2% over AF relaying and direct D2D, respectively, attains 118.5 bits/s/Hz/W energy efficiency, and maintains 30.2 bits/s/Hz under severe CSI uncertainty.
Volume: 16
Issue: 5
Page: 2575-2594
Publish at: 2026-10-01

Impact of optimal power flow on power quality in a low voltage three-phase network: application to a real case in Lubumbashi (DR Congo)

10.11591/ijece.v16i5.pp2304-2320
David Milambo Kasumba , Guy Nkulu Wa Ngoie , Hyacinthe Tungadio Diambomba , Jean-Paul Katond Mbay , Bonaventure Banza WA Banza
Low-voltage distribution systems (LVDS) in rapidly growing Sub-Saharan African cities frequently experience severe phase imbalance, voltage deviations, and high technical losses due to overloaded and poorly balanced feeders. Despite the increasing availability of advanced optimization techniques, their application to real low-voltage networks in developing countries remains limited. This study investigates the impact of an unbalanced three-phase optimal power flow (OPF) framework on the power quality and operational performance of a real low-voltage distribution network located in Kamalondo, Lubumbashi (Democratic Republic of the Congo). The network model was parameterized using field measurements collected between September and December 2024. The optimization problem was formulated as a mixed-integer nonlinear programming (MINLP) model and solved using the interior point method implemented in Pandapower. The proposed framework simultaneously minimizes active power losses and mitigates phase imbalance while respecting voltage and thermal operating constraints. Simulation results demonstrate significant improvements in network performance. The minimum phase-to-neutral voltage increased from 198 V to 210 V, while the maximum voltage decreased from 232 V to 226 V, improving compliance with power quality standards. The maximum current phase was reduced by 15%, and total active power losses decreased by 30%. Furthermore, the voltage unbalance factor (VUF) was reduced from 8% to 3% through optimized phase allocation and power redistribution. These results demonstrate that unbalanced three-phase OPF constitutes an effective and practical solution for improving power quality, reducing technical losses, and enhancing the operational reliability of heavily loaded low-voltage networks in developing urban environments.
Volume: 16
Issue: 5
Page: 2304-2320
Publish at: 2026-10-01

Information architecture debt: Why legacy platform schema decisions constrain enterprise AI capability

10.11591/ijece.v16i5.pp2473-2482
Mihir Shah
Enterprise platforms accumulate a specific category of technical debt this paper terms information architecture debt. Schema decisions optimized for transactional efficiency in earlier computing eras produce structural constraints that limit what artificial intelligence can accomplish on those platforms, largely independent of which models are selected or how they are orchestrated. This paper positions information architecture debt as a category distinct from code debt and infrastructure debt, and it argues that the distinction matters because remediation locality and coordination cost differ sharply. A five-indicator diagnostic framework is proposed, covering duplicate canonical entities, broken semantic chains, provenance gaps, enforcement asymmetry, and consumer assumption divergence. The framework supports a capability ceiling hypothesis: substrate defects impose an upper bound on AI outcomes that no model choice appears able to exceed. A sequenced remediation approach follows, prioritizing entity resolution, semantic alignment, and provenance instrumentation by AI-capability impact rather than ease of fix. Grounded in practitioner experience structuring enterprise information architecture across multi-language, multi-system platforms, the framework offers leaders diagnostic and sequencing tools, and it establishes a conceptual foundation for later quantitative and sector-specific refinement.
Volume: 16
Issue: 5
Page: 2473-2482
Publish at: 2026-10-01

A compact tri-band THz patch antenna using slot-loaded radiator and defected ground structure

10.11591/ijece.v16i5.pp2549-2558
Tran-Thi Bich Ngoc , Truong Thi Phuong Nhi
Terahertz (THz)- based antennas are essential for future 6G sensing and short range communications, especially when multi-band flexibility and compact di mensions are required. This paper presents a compact microstrip patch an tenna designed for THz applications with tri-band operation. The antenna is formed out of a rectangular patch filled with slots, a defective ground structure (DGS), and a microstrip feedline. Slot loading and the DGS generate various current paths, exciting several resonant modes and enhancing impedance match ing across the resonant frequency range. The proposed design is implemented on a 30 µm thick RO3003 substrate with a relative permittivity of 3 and loss tan gent of 0.001. The antenna has physical dimensions of 0.27×0.28×0.03 mm3. Simulation results show the antenna operates at three frequencies with a remark ably low return loss of up to-61.35 dB over the range of 1.59-3.2 THz. It offers tri-band gains of 6.01 dBi, 7.30 dBi, and 7.31 dBi, with a voltage standing wave ratio (VSWR) close to the ideal value of 1 for most bands. Furthermore, field distributions and power breakdown calculations are performed to better under stand the radiation mechanisms and efficiency characteristics at THz frequen cies. The antenna radiated power across the band, achieving up to 86.5% of the power accepted. These results indicate that the suggested design provides tri-band capabilities while being a simple, compact and efficient candidate for the demand for improved antennas in next-generation wireless communication systems.
Volume: 16
Issue: 5
Page: 2549-2558
Publish at: 2026-10-01
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