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Development of an educational SCADA training kit for electric railway system monitoring and control

10.11591/ijeecs.v41.i2.pp740-752
Krommavut Nongnuch , Saowalak Leelawongsarote , Tawan Khunarsa , Anucha Zahoh
The increasing dependence on supervisory control and data acquisition (SCADA) technology in electric railway systems underscores the need for practical and low-cost training platforms that reflect real supervisory control environments. Conventional educational tools often rely on software-only simulations or high-cost industrial equipment, resulting in a persistent gap between academic instruction and operational practice. This study presents an educational SCADA training kit designed specifically for railway power monitoring and control. The system replicates essential SCADA functions including real-time data acquisition, breaker operation, environmental monitoring, fault handling, and operator interface visualization through a modular hardware software architecture suitable for academic laboratories. Performance evaluation was conducted across multiple operational scenarios, including normal operation, induced faults, temperature variations, and emergency commands. Key performance indicators such as responsiveness, sensing accuracy, alarm reliability, and stability were measured over 50 repeated trials. Results show 98.7% responsiveness within a 200 ms threshold, sensor accuracy above 97.5%, and 100% alarm reliability across 25 fault events. Continuous testing confirmed stable operation without communication or actuation failures. These findings demonstrate that the proposed kit offers a reliable, scalable, and pedagogically valuable platform for teaching SCADA concepts in railway automation, while also supporting research and prototyping in supervisory control applications.
Volume: 41
Issue: 2
Page: 740-752
Publish at: 2026-02-01

Expert evaluation of a web-based grammatical competence module: Fuzzy Delphi method

10.11591/ijere.v15i1.35355
Nur Hidayah Md Yazid , Nur Ainil Sulaiman , Harwati Hashim
Web-based learning modules have been considered indispensable for English as a second language (ESL) learners to utilize autonomously. However, there are still not many reputable grammatical competence modules designed for the transition between secondary school and undergraduate levels. Thus, this study aimed to ascertain expert consensus on developing a web-based grammatical competence module for pre-university ESL learners. The Fuzzy Delphi method (FDM) was employed in this study to create the module. Four broad constructs, which are the design, technical aspects, content, pedagogy of the website were used as references in developing a survey as the instrument for the study. The features in the survey were evaluated by six selected experts based on established criteria for high-quality language learning websites. Data analysis was undertaken using a 5-point fuzzy scale and the Fuzzy Delphi approach Logic Software (FUDELO 1.0). Supported by the findings and a consensus rate of over 75%, a cut-off value (d) of ≤0.2, and a fuzzy score (A) of ≥α-cut value=0.5, expert consensus was reached for the four constructs. The findings support that the module is fitting for pre-university ESL learners and can be used as a supplementary grammar learning module. Empirical studies related to learner performance and engagement outcomes in the future must continue assessing the long-term effectiveness of the module and ensure its long-term efficacy in ESL learning.
Volume: 15
Issue: 1
Page: 784-794
Publish at: 2026-02-01

From algorithms to classrooms: a decade of artificial intelligence in education research

10.11591/ijere.v15i1.34427
Lim Seong Pek , Nahdatul Akma Ahmad , Faiz Zulkifli , Rabindra Dev Prasad , Ari Muzakir , Jun S. Camara
The education industry has seen a substantial transformation thanks to artificial intelligence (AI), which has improved administrative effectiveness, accessibility, and individualized learning. However, issues like moral dilemmas, digital justice, and policy inconsistencies still exist. From 2015 to 2024, this bibliometric research explores how AI is revolutionizing education. Personalized learning, improved accessibility, and expedited administrative procedures have all been made possible by AI; yet, issues with cost, digital equity, and ethics still exist. We used the Web of Science (WoS) database to conduct a comprehensive bibliometric analysis of 291 peer-reviewed articles that were indexed in the Social Sciences Citation Index (SSCI). The PRISMA methodology was used in the study to find and filter pertinent material. Thematic trends, citation patterns, and co-authorship networks were examined using bibliometric tools like VOSviewer. The progress of generative AI tools like ChatGPT, the importance of AI in democratizing education, and the integration of AI into curriculum building are some of the key discoveries. The report identifies significant nations, organizations, and researchers in AI education and emphasizes global research relationships. Our research raises ethical governance issues while shedding light on AI’s potential to promote individualized learning and increase student engagement. These findings support sustainable development goal (SDG) 4 on quality education by highlighting the need for responsible AI use to address the digital divide. This paper offers useful suggestions for academics, educators, and legislators to maximize AI’s promise while tackling its drawbacks.
Volume: 15
Issue: 1
Page: 500-510
Publish at: 2026-02-01

Predictors of teachers’ readiness for inclusive education in Kazakhstan

10.11591/ijere.v15i1.36260
Dinara Ospankulova , Akbota Autayeva , Zhanna Paylozyan , Akmaral Rsaldinova , Aigul Baitursynova
Inclusive education (IE) is increasingly recognized as a key priority in modern educational systems; however, in Kazakhstan, there is limited evidence on the factors influencing teachers’ attitudes and readiness to implement it. This study explores public school teachers’ attitudes toward inclusive education (TATIE) and examines how personal, professional, and institutional factors affect these attitudes. A survey of 638 teachers from Almaty schools was conducted using a validated instrument, and correlation and regression analyses were employed to identify significant predictors. The results indicate that gender, teaching experience (TE), frequency of contact with students with disabilities (SWD), perceived school support, and participation in specialized training significantly influence teachers’ attitudes. Positive attitudes were particularly associated with direct professional experience and strong institutional support, highlighting the importance of targeted professional development and school-level measures. This study contributes to the literature by providing a comprehensive quantitative analysis specific to the Kazakhstani context and offers practical insights to guide policy and enhance the effective implementation of inclusive practices, ultimately improving the quality of education for students with special educational needs.
Volume: 15
Issue: 1
Page: 587-596
Publish at: 2026-02-01

Newchaos function from the composition of DTM and Gauss iterated map for digital image encryption

10.12928/telkomnika.v24i1.27551
Adrianus; Universitas Indonesia Yosia , Tokonyai Tawanda Jonathan; Universitas Indonesia Rabvemhiri , Suryadi; Universitas Indonesia MT
This manuscript introduces a novel chaotic discrete function, formulated through the composition of the dyadic transformation map (DTM) and the Gauss iterated map (GIM), and designated as DTGIM. The National Institute of Science and Technology (NIST) randomness test suite, bifurcation diagrams, and Lyapunov exponents are used to examine the chaotic characteristics of DTGIM. With ini tial condition x0 = 0.12345 and parameters α = −15 and β = 0.3, the func tion shows chaotic behavior in the bifurcation diagram and produces a positive Lyapunov exponent. Strong randomness is further confirmed by NIST tests, which achieve 100% for 32-bit binary sequences and 63.75% for 8-bit binary sequences. Additionally, we compare a number other chaotic discrete functions that also employ the composition method. These findings show that DTGIM is a viable option for applications involving chaos-based cryptography.
Volume: 24
Issue: 1
Page: 228-239
Publish at: 2026-02-01

Optimizing blood cell classification: evaluating feature dimensionality and validation strategies

10.12928/telkomnika.v24i1.27269
Ruaa H. Ali; Northern Technical University Al-Mallah , Marwa Mawfaq; Northern Technical University Mohamedsheet Al-Hatab , Maysaloon; Northern Technical University Abed Qasim
Manual blood cell classification is time consuming and may lead to inconsistent results. This study aims to assist pathologists in diagnosing hematological disorders using machine learning (ML) techniques for automated classification of blood cells in multi-color test images, distinguishing red blood cells (RBCs) and white blood cells (WBCs). Features were extracted using the InceptionV3 network, and several ML models were evaluated for classifying blood cells into eight categories. Two validation strategies: a 66%–34% train–test split and 20-fold cross-validation were applied. The effect of dimensionality reduction through principal component analysis (PCA) was also examined, reducing the feature space from 2,048 to 100 components. Among all models, support vector machine (SVM) achieved highest performance, with 93.4% accuracy and an area under the curve (AUC) of 0.996 without PCA, and 90.1% accuracy with an AUC of 0.991 after PCA. Although PCA slightly reduced accuracy, it improved computational efficiency. Overall, SVM provided the most accurate, stable, and generalizable classification results for automated blood cell analysis.
Volume: 24
Issue: 1
Page: 359-370
Publish at: 2026-02-01

Energy-efficient AI-enhanced secure routing for protecting IoT networks from advanced attacks

10.11591/ijeecs.v41.i2.pp731-739
Leelavathi R. , Vidya A.
This paper proposes artificial intelligence-enhanced secure routing (AIRS), a lightweight AI-enhanced secure routing protocol for internet of things (IoT) networks operating under advanced routing attacks. Unlike existing approaches that treat intrusion detection and routing separately, AIRS tightly integrates anomaly scoring into trust-aware routing decisions using a compact random forest model designed for constrained nodes. The anomaly detector is trained offline on simulated IoT traffic features and deployed for real-time inference during routing. Extensive Cooja simulations demonstrate that AIRS improves intrusion detection accuracy and packet delivery while reducing energy consumption compared to secure-RPL and trust-LEACH. The current validation is limited to simulation environments, and real-world testbed evaluation is left for future work.
Volume: 41
Issue: 2
Page: 731-739
Publish at: 2026-02-01

Advancements in physical layer key generation: a review on channel reciprocity and IoT security techniques

10.12928/telkomnika.v24i1.27340
Syed Shafaq; Southeast University Ali Shah , Ajab; University of Science and Technology Bannu Noor , Ruiyue; Changchun University of Science and Technology Liang , Rahmat Ullah; FAST National University of Computer and Emerging Science Zadran
With the burgeoning internet of things (IoT), securing communication becomes paramount. Traditional cryptography does not meet computational needs and brute-force attacks. This review explores the state-of-the-art physical layer secret key generation (PLKG) that takes advantage of the inherent reciprocity and randomness of wireless channels. We investigate cutting-edge techniques such as feature extraction networks, domain adversarial training, and deep learning-based approaches, evaluating their effects on the security and efficiency of key generation. In addition to these methods, the review addresses real-world challenges such as multi-user scenarios, reconciliation overhead, and inconsistent channel measurement. We believe that improved key generation rates and security can be achieved through the use of millimeter wave technology and full-duplex communication. To strengthen the robustness of key generation, the paper concludes by suggesting future directions, such as incorporating more random sources, such as physiological signals and sensor data. This comprehensive overview offers deep insights into the state-of-the-art and paves the way for reliable communication in ever more complicated IoT settings.
Volume: 24
Issue: 1
Page: 196-205
Publish at: 2026-02-01

Information technology value engineering through partial adjustment valuation theory

10.12928/telkomnika.v24i1.27478
Lukman; Telkom University Abdurrahman , Candiwan; Telkom University Candiwan
The paper proposes a systems management approach that utilizes information technology (IT) treatment as a framework to help firms enhance future performance by optimising key parameters. The method certifies a valuation approach that enables businesses to better manage their IT infrastructure and improve performance. A case study of A case study of PT Telekomunikasi Indonesia (Telkom) and PT XL Axiata (XL) (2004–2018) shows the method’s effectiveness. Once the IT value is identified, specific parameters can be engineered to improve performance without changing other variables. The approach uses a partial adjustment valuation model, enabling performance gains at lower costs. The results show significant improvements in both firms’ performance values and ratios compared to their originals. This supports adopting a cost leadership strategy, making IT based businesses more efficient, cost-effective, and better performing across financial, business, and strategic dimensions.
Volume: 24
Issue: 1
Page: 111-125
Publish at: 2026-02-01

Deep learning-based power amplifier linearization in OFDM systems with unknown channel state information

10.12928/telkomnika.v24i1.27236
Meryem Mamia; University of Tlemcen Benosman , Mohammed Yassine; University of Tlemcen Bendimerad , Fethi Tarik; University of Tlemcen Bendimerad
This paper presents an end-to-end deep learning-based approach for orthogonal frequency-division multiplexing (OFDM) communication systems impaired by nonlinear power amplifiers (PAs) and channel fading. The PA nonlinearity is modeled using the modified Rapp model, and simulations are performed on a 64-subcarrier OFDM system with a cyclic prefix (CP) of 8 and 16-quadrature amplitude modulation (16-QAM). The proposed autoencoder-based OFDM–PA (AE-OFDM-PA) system jointly optimizes the transmitter and receiver through end-to-end learning, enabling simultaneous compensation of both PA nonlinearities and channel distortions without requiring explicit channel state information (CSI) estimation. Instead, the model leverages embedded pilot sequences to learn the implicit CSI representation directly from data, allowing the receiver to correct amplitude and phase distortions adaptively. Simulation results demonstrate that AE-OFDM-PA significantly outperforms conventional OFDM and OFDM-PA systems, achieving over 70× block error rate (BLER) improvement compared with the uncompensated OFDM-PA system at an input back-off (IBO) of 3 dB. Furthermore, the proposed method achieves approximately 11.5 dB adjacent channel leakage ratio (ACLR) improvement over the classical memory polynomial digital predistortion (DPD) technique, while slightly reducing the peak-to-average power ratio (PAPR). Overall, AE-OFDM-PA provides a robust, spectrally efficient, and low-complexity solution for nonlinear and fading environments with unknown or varying CSI.
Volume: 24
Issue: 1
Page: 1-13
Publish at: 2026-02-01

Real-time intelligent virtual assistant based on retrieval augmented generation

10.11591/ijai.v15.i1.pp237-246
I Ketut Resika Arthana , Ni Putu Novita Puspa Dewi , Gede Arna Jude Saskara , I Made Ardwi Pradnyana , Luh Indrayani
Improving user experience in accessing information on organizational websites remains a challenge. Users often face complex navigation and multi step searches that slow information retrieval. This study introduces the real time intelligent virtual assistant (RIVA), which integrates large language models (LLMs) with the retrieval-augmented generation (RAG) framework to support real-time interaction with website content. The system was implemented on the Universitas Pendidikan Ganesha (Undiksha) website using a WordPress content management system (CMS) and developed following the design science research (DSR) approach, which includes six stages: problem identification, solution objectives, design and development, demonstration, evaluation, and communication. The retrieval-augmented generation assessment (RAGAS) evaluation indicated that the combined model of text-embedding-ada-002 and semantic chunking yielded the best results, with context precision=0.83, context recall=0.90, response relevancy=0.91, faithfulness=0.83, and answer correctness=0.85. User experience questionnaire (UEQ) testing performed well, particularly in the novelty and stimulation dimensions. These results demonstrate that RIVA can provide users with access to relevant and engaging information. As a result, future research will focus on improving retrieval and developing adaptive semantic chunking for structured and complex data.
Volume: 15
Issue: 1
Page: 237-246
Publish at: 2026-02-01

Optimization and techno-economic analysis of hybrid renewable systems in Nigeria

10.12928/telkomnika.v24i1.27499
Lambe; Kwara State University Mutalub Adesina , Jamiu; Kwara State University Lawal , Olalekan; Kwara State University Ogunbiyi , Abdulwaheed; Kwara State University Musa , Bilkisu; Kwara State University Jimada Ojuolape , Monsurat; Kwara State University Omolara Balogun , Bashiru; Kwara State University Olalekan Ariyo
Rising electricity demand, fossil fuel depletion, and environmental concerns highlight the need for sustainable rural electrification. The Elenjere community in Kwara State, Nigeria, depends on costly diesel generation and limited grid access, creating an urgent demand for reliable and affordable alternatives. This study designs and optimizes a hybrid renewable energy system (HRES) for the community using hybrid optimization model for electric renewables (HOMER) Pro simulation. The proposed system combines photovoltaic (PV), wind turbines (WT), battery storage (BAT), inverter (INV), and a diesel generator (DG) as backup. Field data on load demand, solar radiation, and wind speed were used for realistic modeling. System performance was evaluated using levelized cost of energy (LCOE), net present cost (NPC), and system capital cost (SCC). Results show the PV/WT/BAT/INV/GEN configuration achieved the lowest LCOE of USD 0.455/kWh, an NPC of USD 2.98 million, and 86.2% renewable penetration, significantly reducing diesel use. Sensitivity analysis revealed that reducing battery costs and increasing PV capacity could lower the LCOE to USD 0.227–0.325/kWh. The study demonstrates how modest wind resources (4.19 m/s at 10 m) complement PV in low-wind regions while addressing inflation realism (25.5% discount rate, foreign exchange (FX) volatility). Future work will include dynamic control simulation and lifecycle analysis to enhance scalability and sustainability.
Volume: 24
Issue: 1
Page: 343-358
Publish at: 2026-02-01

Developing tuberculosis drug information system using a throwaway prototype: Udayana Hospital case study

10.12928/telkomnika.v24i1.27073
Rini; Udayana University Noviyani , Luh Arida Ayu; Udayana University Rahning Putri , I Nyoman; Udayana University Gede Budiana , Luh; Udayana University Gede Astuti , I Made; Udayana University Oka Widyantara , Ida Ayu; Udayana University Alit Widhiartini , Ida Bagus; Universitas Udayana Teaching Hospital Nyoman Maharjana , Sagung; Udayana University Chandra Yowani , I Gusti Ngurah; Udayana University Anom Cahyadi Putra
Tuberculosis (TB) remains a major health problem in Indonesia, and efficient drug management is essential to ensure continuous treatment and prevent resistance. At Udayana University Hospital, manual recording and reporting often caused delays and errors, while integration with the National Tuberculosis Information System (SITB) was limited. This study developed a TB drug information system using the throwaway prototype model to address these challenges and enhance hospital workflow efficiency. The system implementation demonstrated measurable improvements in operational performance, with data entry errors reduced by 83% and the average recording time per patient shortened by 35% compared to the previous manual process. User feedback confirmed improved usability, accuracy, and reliability in supporting hospital workflows and timely reporting. In conclusion, the proposed system effectively improved the accuracy and efficiency of TB drug management while addressing hospital level operational challenges. This study demonstrates the applicability of the throwaway prototype model in healthcare information-system development and provides insights for scaling and integration with national TB programs.
Volume: 24
Issue: 1
Page: 49-70
Publish at: 2026-02-01

Optimizing planar micro-transformer performance

10.12928/telkomnika.v24i1.27276
Tahar; University of Science and Technology of Oran USTO-MB Alili , Fatima Zohra; University of Science and Technology of Oran USTO-MB Medjaoui , Azzedine; Nour El Bachir University Center Hamid , Abderahim; National Polytechnic School of Oran Maurice Audin Mokhefi , Yacine; Nour El Bachir University Center Guettaf , Hocine; Nour El Bachir University Center Guentri
Faced with new requirements for isolated switching power supplies with high efficiency and power density, planar transformer technology has emerged as a serious alternative to wound components. The work presented in this paper addresses the issue of developing planar transformers in the context of low-power electronics, where volume and weight constraints are paramount. The flat shape of the coils and the interlacing of the windings do not allow for control of magneto-thermal phenomena. Although scientific literature offers numerous simulation tools to aid in the design of such transformers, it must be noted that they do not allow for a rigorous account of these phenomena. In this paper, methods and a geometric and electrical sizing tool in planar technology are used for the design of flyback direct current to direct current (DC/DC) converters. Methods for dimensioning and estimating temperature rise are presented and compared in order to develop calculation tools for design purposes. This study enabled us to observe the distribution of the magnetic field, the role of ferrite, the distribution of currents and voltages in the coils, and the distribution of temperature in our device. It should be noted that conductive and convective heat transfer processes were considered in steady state.
Volume: 24
Issue: 1
Page: 313-328
Publish at: 2026-02-01

Performance enhancement of embedded object detection via neural hardware acceleration

10.12928/telkomnika.v24i1.27448
Alwin; STMIK Indonesia Mandiri Hartono Limaran , Agung; STMIK Indonesia Mandiri Wicaksono , Patah; STMIK Indonesia Mandiri Herwanto
This paper presents the first benchmarking of you only look once version 11 (YOLO11) on the Rockchip RK3566 neural processing unit (NPU) within the Orange Pi 3B platform. Performance was compared between the quad-core ARM Cortex-A55 CPU and the integrated NPU using the COCO2017 dataset, evaluating latency, energy, and accuracy. NPU acceleration achieved >80% latency reduction and ≈ 94% lower per-inference energy consumption, with speedup of up to 16.7× while maintaining accuracy within 0.03 mean average precision (mAP) of the baseline. Average power remained nearly constant (3.60 W central processing unit (CPU) vs. 3.59 W NPU), indicating that the efficiency gains stem from reduced inference time rather than lower wattage. Limitations included unstable INT8 quantization due to unsupported operators and calibration-range mismatch, as well as minor CPU-side overhead in preprocessing and non-maximum suppression. The findings confirm that the RK3566 NPU delivers substantial efficiency gains without accuracy loss, enabling compact and low-cost platforms to sustain modern object-detection workloads. This demonstrates that affordable NPUs can provide reliable, real time artificial intelligence (AI) inference for embedded vision, internet of things (IoT), and robotics applications.
Volume: 24
Issue: 1
Page: 126-141
Publish at: 2026-02-01
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