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30,185 Article Results

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

A move analysis of the discussion sections in English as a second language learners’ quantitative theses

10.11591/ijere.v15i1.34624
Mary Joy V. Herediano , Riziel E. Secretario , Arnold M. Sumpo , Ivy F. Amante , Rovy M. Banguis , Gay Emelyn L. Lariosa , Norhanie D. Macarao
Discussion section of research papers is one of the most essential sections because the authors demonstrate the knowledge contribution of their research findings to the existing literature. In this study, 16 quantitative theses analytical components written by the English language learners were gathered and analyzed. By utilizing qualitative research design focusing on move analysis, the researchers found out that Move 1 (background information), Move 2 (reporting results), Move 3 (summarizing the results), and Move 4 (commenting on the results) were identified as obligatory moves since they serve as the primary objectives of this explanatory segment. Move 6 (evaluating methodology) was recognized as a traditional move. Move 5 (summarizing the study) and Move 7 (deductions from the research) were noted as optional moves. Distinct linguistic characteristics and verbal signals were observed in the various moves, with the patterns of these steps identified as a structured arrangement in the results discussion. The results aim to help student writers recognize the rhetorical frameworks that should be included in the interpretive sections of quantitative theses.
Volume: 15
Issue: 1
Page: 740-750
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

Performance analysis of a multi-level inverter fed permanent magnet synchronous motor for electric vehicles

10.12928/telkomnika.v24i1.27234
Donepudi; Aditya University Tata Rao , Bhimaraju; Aditya University Pemmanaboidi Srihari Datta , Uma Phanendra; Aditya University Kumar Chaturvedula , Kondala; Aditya University Rao Parasa , Mummidi Parvateeswara; Aditya University Subba Raju
Electric vehicle (EV) drive systems utilizing permanent magnet synchronous motors (PMSMs) often encounter performance limitations due to switching losses, voltage stress, and harmonic distortion. To address these challenges, this paper presents a compact 31-level multilevel inverter (MLI) topology designed to enhance drive efficiency and power quality. The proposed inverter minimizes switching devices and driver circuits, resulting in reduced total harmonic distortion (THD), lower voltage stress, and improved waveform fidelity. Advanced control strategies are employed to further optimize performance. field-oriented control (FOC) ensures precise torque and flux regulation, while direct torque control (DTC) delivers rapid transient response. To mitigate torque ripple and variable switching frequency inherent in conventional DTC, adaptive predictive control (APC) is integrated to refine switching behavior and enhance dynamic stability. Simulation studies conducted in MATLAB/Simulink demonstrate the effectiveness of the proposed system, revealing significant improvements in torque smoothness, reduced THD (0.85%) and elevated efficiency under variable load conditions. This integrated solution offers a practical and scalable approach for next-generation EVs, contributing to greater reliability, energy utilization, and overall system performance.
Volume: 24
Issue: 1
Page: 302-312
Publish at: 2026-02-01

Design of Antasena: an AI-powered maritime surveillance and anomaly detection system for security decision support

10.11591/ijai.v15.i1.pp269-288
Arif Badrudin , Siswo Hadi Sumantri , Rudy Agus Gemilang Gultom , I Nengah Putra Apriyanto , Umi Laili Yuhana , Fitria Dwi Ratnasari
Indonesia’s vast maritime territory faces serious challenges from illegal fishing, smuggling, and habitat destruction. To address these, the Indonesian Navy (TNI-AL) developed Antasena, an artificial intelligence (AI)-powered smart dashboard integrating automatic identification system (AIS) data, satellite imagery, and conservation metrics. Antasena leverages advanced anomaly detection algorithms, achieving 95.3% accuracy, 94.7% precision, 94.2% recall, and a 96.8% receiver operating characteristic-area under the curve (ROC-AUC) score in identifying vessel anomalies, including unauthorized fishing and smuggling activities. Using the analyze, design, develop, implement, and evaluate (ADDIE) framework, the system supports real-time maritime surveillance and biodiversity monitoring in conservation zones. The main contributions of this study include the development of a user-centric AI-based dashboard for maritime anomaly detection, the integration of multi-source data with machine learning models, and validation through operational field tests with maritime authorities. Antasena offers a scalable and effective solution to strengthen maritime security and protect Indonesia’s marine resources.
Volume: 15
Issue: 1
Page: 269-288
Publish at: 2026-02-01

Distorted born iterative method reconstruction in high-noise environments using KNN-based machine learning denoising

10.12928/telkomnika.v24i1.27401
Nguyen Quang; Vietnam Academy of Science and Technology Huy , Nguyen Truong; Vietnam Academy of Science and Technology Thang
Ultrasound tomography reconstruction using the distorted born iterative method (DBIM) is sensitive to measurement noise, which degrades image fidelity and slows convergence. We propose integrating a k-nearest neighbors (KNN) denoising step within each DBIM iteration to suppress noise adaptively while preserving structural edges. Simulations with a circular cylindrical target and transmit/receive geometry (12×12) were conducted at signal-to-noise ratio (SNR) levels of 6 dB, 3 dB, and 1 dB. Compared with conventional DBIM employing Tikhonov regularization, the KNN-filtered DBIM reduces normalized reconstruction error by up to 57.2% at 1 dB and shows faster error decay over successive iterations. The method is training-free, computationally lightweight, and preserves fine structural details. These properties make KNN-filtered DBIM attractive for noisy or resource-constrained imaging environments. Future work will validate the approach on experimental data and explore adaptive K selection.
Volume: 24
Issue: 1
Page: 206-218
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

Improved disturbance rejection of induction motor drives using PI–VGSTASM control and torque disturbance estimation

10.12928/telkomnika.v24i1.27459
Ngoc; Industrial University of Ho Chi Minh City Thuy Pham , Duc; Industrial University of Ho Chi Minh City Thuan Le , Thanh; Industrial University of Ho Chi Minh City Tinh Pham
Induction motor (IM) drives often suffer performance degradation under load variations and parameter uncertainties when using conventional proportional–integral (PI)- based field-oriented control (FOC). To address these issues, this study proposes a composite control framework combining a PI regulator in the speed loop with a Lyapunov-based variable-gain super twisting algorithm (VGSTA) for the inner current loops to enhance robustness against disturbances and parameter variations. In addition, a load torque observer is developed to estimate unknown disturbances in real time and generate an equivalent compensation current, thereby improving disturbance rejection. Unlike existing approaches, the proposed strategy achieves a balance between simplicity, robustness, and smooth control by integrating classical PI control with higher-order sliding mode techniques and adaptive observer dynamics. Furthermore, the controller and observer gains are optimized using particle swarm optimization (PSO) to improve convergence and reduce overshoot under uncertain conditions. Simulation results demonstrate accurate speed regulation, effective chattering reduction, and reliable operation under load and parameter variations. Due to its low computational complexity and high robustness, the proposed method is well suited for industrial drive systems and electric mobility applications.
Volume: 24
Issue: 1
Page: 329-342
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

Comparison methods in a decision support system for determining JavaScript frameworks

10.12928/telkomnika.v24i1.27241
Rofif Aghna; Sunan Kalijaga State Islamic University Yogyakarta Fakhri Diya , Agus; Sunan Kalijaga State Islamic University Yogyakarta Mulyanto
The selection of an appropriate JavaScript framework in web-based software development often leads to errors when the chosen framework is incompatible with the design. The ability to make decisions quickly, accurately, and precisely is therefore a key factor in successful software design. Addressing this need, the present study analyzes the accuracy of the analytical hierarchy process-weight product (AHP-WP), analytical hierarchy process-technique for order preference by similarity to ideal solution (AHP TOPSIS), and analytical hierarchy process-simple multi-attribute rating technique (AHP-SMART) methods in determining the most suitable JavaScript framework according to the International Organization for Standardization (ISO) 9126 classification. To evaluate accuracy, the mean absolute percentage error (MAPE) was applied as a cost function to measure the error percentage of each method. The analysis was conducted on ten popular JavaScript frameworks selected based on their popularity and usage trends. The evaluation considered six quality criteria: functionality, reliability, usability, efficiency, maintainability, and portability. The results show the ranking of each alternative for all methods. Accuracy measurement using MAPE revealed that the AHP-WP method produced the smallest error percentage (37.77645%), compared to AHP-TOPSIS (47.12566%) and AHP-SMART (46.4041%). Accordingly, the AHP-WP method is recommended for decision support system (DSS) development.
Volume: 24
Issue: 1
Page: 95-110
Publish at: 2026-02-01

Implementation of markerless augmented reality and cyber physical-social systems for smart tourism application

10.12928/telkomnika.v24i1.27414
Ilham; Institut Teknologi Sumatera Firman Ashari , Fanesa; Institut Teknologi Sumatera Hadi Permana , Muhammad; Universitas Muhammadiyah Malang Zainal Arifin , Purwono; Institut Teknologi Sumatera Prasetyawan
Lampung province holds substantial tourism potential that remains underutilized due to fragmented information and limited promotional strategies. This study introduces a smart tourism application integrating markerless augmented reality (AR) with cyber-physical-social systems (CPSS), representing the first implementation of its kind for location-based tourism in the region. The novelty lies in the hierarchical coordinate transformation architecture (HCTA), a multi-layer computational framework employing the Haversine formula to achieve high-precision mapping of geographic coordinates into AR-optimized perceptual views. The system was evaluated for geolocation accuracy, resource utilization, backend scalability, AR rendering robustness, and user experience. Results show strong performance: geolocation tests across seven destinations yielded a mean error rate of 1.5%; AR operations remained efficient with 8–10% central processing unit (CPU) and 140–160 MB random access memory (RAM) usage; and rendering was stable across 360° device orientation. Backend tests confirmed scalability, sustaining 56 requests per second with zero failures under 100 concurrent users. A user study with 20 participants using the user experience questionnaire-short (UEQ-S) revealed highly positive outcomes, with overall scores 2.275, all within the Excellent benchmark. These findings confirm that the application is not only technically robust and efficient but also engaging and enjoyable, offering a scalable framework for immersive smart tourism ecosystems.
Volume: 24
Issue: 1
Page: 71-94
Publish at: 2026-02-01

Advanced microwave imaging and artificial neural networks for early detection and localization of breast tumors

10.12928/telkomnika.v24i1.27126
Abdelfettah; University of Mustapha Stambouli Miraoui , Lotfi; School of Applied Sciences Tlemcen Merad , Djalal; LARATIC Laboratory at National Institute of Telecommunications and Information Technology and Communication (ENSTTIC) Ziani-Kerarti
This study investigates the detection and localization of breast tumors based on dielectric property differences between cancerous and normal tissues. A microwave imaging technique integrated with artificial neural networks (ANNs) is proposed as a noninvasive alternative to conventional screening methods such as mammography and magnetic resonance imaging (MRI). A breast model with a 2.5 mm spherical tumor was designed using CST Microwave Studio. Simulation results show that the ANN achieves a detection rate close to 100%, providing negative outputs for tumor-free cases and positive outputs for cases with tumors. Additionally, ANN outputs strongly correlate with the actual tumor positions in the simulated environment. These findings suggest that microwave imaging combined with ANNs offers a cost effective, radiation-free, and patient-friendly solution for the early detection and localization of breast cancer, with promising potential for clinical translation.
Volume: 24
Issue: 1
Page: 240-248
Publish at: 2026-02-01

Technology levels in artificial intelligence robotics and industrial automation: impacts and implications

10.12928/telkomnika.v24i1.27253
Ratna; Universitas Esa Unggul Yulika Go , Agnes; National Research and Innovation Agency (BRIN) Sondita Payani , Siti; Universitas Hasanuddin Rabiatul Adawiyah , Ogi; National Research and Innovation Agency (BRIN) Gumelar
Robotics technology has progressed rapidly since its debut in 1922, evolving from simple programmable automation to highly sophisticated systems. This study employs a hybrid methodology, combining qualitative analysis of key robotic components manipulators, controllers, end effectors, and geometric configurations with quantitative comparison of performance metrics to classify robots according to their technological level (low-tech versus high tech). The findings show clear distinctions across these levels. Low-tech robots typically achieve positioning accuracy of about 0.025 mm and rely mainly on single electric motor actuation, making them suitable for simple, repetitive tasks. In contrast, high-tech robots can perform complex operations with positioning accuracy of up to 3 mm, integrating multiple actuation systems such as electric, pneumatic, and hydraulic mechanisms for enhanced flexibility and control. Moreover, high-tech robots exhibit greater manipulative capabilities and advanced control systems that enable multi axis and adaptive operations not feasible for low-tech counterparts. These results demonstrate how the technological level directly shapes a robot’s precision, actuation complexity, and functional range, providing a clear framework for selecting appropriate robotic solutions in both industrial and research settings.
Volume: 24
Issue: 1
Page: 175-183
Publish at: 2026-02-01

Optimized IMC with GWO algorithm and variable switching function for voltage regulation of SEPIC converter

10.12928/telkomnika.v24i1.27330
Reza; Shahrood University of Technology Fazeli , Mohammad; Shahrood University of Technology Haddad Zarif , Mahmoud; Islamic Azad University Zadehbagheri , Tole; Universitas Ahmad Dahlan Embedded System and Power Electronics Research Group Sutikno
With the growing application of single-ended primary-inductor converter (SEPIC) converters in power electronic systems, precise output voltage regulation under uncertainties and nonlinear conditions remains a significant challenge. Although internal model control (IMC) effectively addresses issues arising from unstable zeros and fixed time delays in non-minimum phase systems, its performance can degrade under large transient errors or sudden disturbances, leading to control signal saturation and instability. In this study, a modified IMC scheme is proposed, which integrates a variable switching function into the control structure. This addition enhances the robustness of the system by dynamically adapting the control effort to mitigate abrupt changes in the control signal and stabilize the output voltage. Furthermore, it prevents controller saturation during large-signal deviations, thereby improving transient response and maintaining system stability. The design parameters of the controller are optimized using the gray wolf algorithm to achieve an optimal balance between voltage overshoot, settling time, and closed-loop stability. Simulation results under various operating conditions confirm the superior performance of the proposed control method compared to conventional IMC.
Volume: 24
Issue: 1
Page: 258-270
Publish at: 2026-02-01
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