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Heading stabilization of a mecanum wheel mobile robot using Kalman filter and SMC under variation condition

10.12928/telkomnika.v24i4.27654
Ardianto; Politeknik Negeri Jember Syaifur Rohman , Tunjung; Politeknik Negeri Jember Genarsih , Sihmaulana; Politeknik Negeri Jember Dwianto , Nuzula; Politeknik Negeri Jember Afianah , Salsabila; Politeknik Negeri Jember Liandra Putri , Nurul; Politeknik Negeri Jember Zainal Fanani , Mochamad; Politeknik Negeri Jember Irwan Nari , Ahmad; Politeknik Negeri Jember Rofi'i , Fendik; Politeknik Negeri Jember Eko Purnomo , Syamsiar; Politeknik Negeri Jember Kautsar , Angga; Politeknik Negeri Jember Dwinanda , Alfan; Politeknik Negeri Jember Ahmad Berlian
This paper presents a robust heading stabilization system for a mecanum wheel mobile robot by integrating a Kalman filter (KF) with sliding mode control (SMC). A two-state KF estimates the robot’s heading angle and gyroscope bias from ICM20948 inertial measurement unit (IMU) measurements, reducing sensor noise by 65% and compensating for bias drift of 0.3° per second. The estimated heading is regulated using SMC with a boundary layer to minimize chattering. Implemented on a Raspberry Pi 3B, the system was validated under varying surface friction conditions and external disturbances. The controller achieved heading stabilization with root mean square error (RMSE) between 0.380° and 0.589° across all surfaces, steady-state error within ±0.5°, and convergence within 2.0–2.6 seconds. Under severe disturbances causing heading deviations up to 238°, rapid recovery within 0.5 seconds was achieved with only 3.04° final steady state error. The results demonstrate the feasibility of implementing robust heading stabilization on low-cost embedded platforms for autonomous navigation applications.
Volume: 24
Issue: 4
Page: 1396-1408
Publish at: 2026-08-01

Design and evaluation of a simple load balancing prototype using the round robin algorithm in local networks

10.12928/telkomnika.v24i4.27560
Muh. Fahmi; Universitas Sulawesi Barat Rustan , Wawan; Universitas Sulawesi Barat Firgiawan , Wiwi; Universitas Sulawesi Barat Nopiana
Load balancing plays a crucial role in ensuring efficient workload distribution and maintaining stable performance in web service systems. This study presents the design and experimental evaluation of a round robin–based load balancing system implemented in a multi-client local area network (LAN) environment using NGINX as a centralized controller. The system consists of three physical machines, comprising one load balancer and two backend servers hosting identical web applications. Multiple clients generate simultaneous hypertext transfer protocol (HTTP) requests, which are distributed alternately to the backend servers using the default round robin mechanism provided by NGINX. Experimental evaluation was conducted under three workload scenarios of 50, 100, and 200 concurrent requests. The results show that the round robin algorithm consistently distributes requests evenly between the backend servers. The average response time increased from approximately 110 ms at 50 requests to 165 ms at 100 requests and 290 ms at 200 requests, indicating stable performance under light to moderate load conditions. These findings demonstrate that the proposed system is lightweight, modular, and easy to deploy in resource limited environments. The implementation is particularly suitable for campus-scale networks and small institutional settings, serving as a practical platform for local server deployment, academic applications, and experimental learning in networking and distributed systems.
Volume: 24
Issue: 4
Page: 1121-1130
Publish at: 2026-08-01

GenAI as an IoT programming assistant: a case study on automated debugging for air quality monitoring systems

10.12928/telkomnika.v24i4.27707
Steven; Pradita University Imanel Bawole , Handri; Pradita University Santoso
The rapid expansion of internet of things (IoT) technology has necessitated the development of user-friendly programming solutions for non–experts. While generative artificial intelligence (GenAI) offers the potential to democratize code development, its ability to assist in the intricate task of automated debugging, particularly regarding hardware integration remains a critical area of research. A design research approach was employed, employing a structured four – phase workflow: error analysis, diagnostic execution through prompting, iterative solution analysis, and functional verification. The methodology was applied to an experimental case study involving an air quality (AQ) monitoring system. The study tested the artificial intelligence (AI)’s capacity to debug C++ code intended for the Arduino integrated development environment (IDE). Gemini AI successfully identified and resolved three critical logic errors arising from mismanaged MQ135 calibration variables, incorrect loop sequencing, and data desynchronization between the organic light emitting diode (OLED) display and the internal status logic. GenAI proved effective as a programming assistant for resolving bugs in IoT applications. However, effective debugging still depends on well-structured prompts and a basic understanding of the underlying IoT hardware.
Volume: 24
Issue: 4
Page: 1278-1286
Publish at: 2026-08-01

Decentralized multi-agent orchestration for legacy order-to cash optimization

10.12928/telkomnika.v24i4.27807
Rahul Kumar; University of Connecticut Thatikonda , Sucharitha; Point Park University Donepudi
Legacy enterprise resource planning (ERP) systems serve as the operational backbone of global commerce but often create bottlenecks due to their rigid, monolithic design. As organizations incorporate artificial intelligence (AI), these outdated systems struggle to support high-speed, parallel workflows, creating a significant integration challenge. This paper introduces a non intrusive modernization approach that overlays a decentralized multi-agent system (MAS) onto existing infrastructure without requiring invasive code changes. By developing a digital twin of the order-to-cash (O2C) process, we train autonomous agents through multi-agent reinforcement learning (MARL) to manage credit validation, inventory allocation, and fulfillment. We adapt the centralized training, decentralized execution (CTDE) framework to meet O2C constraints, enabling agents to learn globally optimal strategies while operating independently. Simulation results show that this architecture surpasses rule-based robotic process automation (RPA) baselines, increasing total throughput by 6.9% over a monolithic setup, though at a 6.3% error rate due to aggressive allocation policies. These results indicate that decentralized agent-based orchestration provides a scalable approach for modernizing legacy ERPs, offering increased agility without the risks associated with platform replacement.
Volume: 24
Issue: 4
Page: 1216-1223
Publish at: 2026-08-01

Classification of P300 event-related potentials using SNN, CNN and LSTM deep learning models

10.12928/telkomnika.v24i4.27659
Ahlaam; Bright Star University M. Saed , Ibtihal; College of Electrical and Electronics Technology Fawzi Elshami , Ali; University of Benghazi I. Elgayar
Accurate classification of P300 event-related potentials remains challenging due to the complex, non-stationary, and low signal-to-noise characteristics of electroencephalography (EEG) signals in brain-computer interface (BCI) systems. P300-based devices, such as the P300 speller, enable communication for patients with severe motor impairments, including those with locked-in syndrome; however, reliable brain signal classification is still a critical limitation. This study presents a comparative evaluation of deep learning models, including convolutional neural networks (CNN), long short-term memory (LSTM) networks, and spiking neural networks (SNN), for P300 signal classification. SNNs represent a biologically inspired paradigm that models the discrete, time-dependent behavior of neural spiking activity and offers advantages in terms of energy efficiency and hardware implementability. Experimental results demonstrate that CNN achieved the highest average classification accuracy (81.04%), followed closely by SNN (80.94%) and LSTM (80.60%). Although CNN slightly outperformed the other models, SNNs showed comparable accuracy while requiring fewer training samples and offering potential benefits for low power and real-time BCI systems. These findings highlight the trade-offs between classification performance and computational efficiency and underline the promise of SNNs as an efficient alternative for P300-based BCI applications.
Volume: 24
Issue: 4
Page: 1294-1306
Publish at: 2026-08-01

Integration of public electric vehicle charging stations into a single gateway platform for net zero emissions goals

10.12928/telkomnika.v24i4.27786
Fajar; STMIK AMIK Bandung Sidiq Arrizal , Shafira; STMIK AMIK Bandung Febriani
The rapid adoption of electric vehicles (EVs) in Indonesia necessitates robust charging infrastructure. However, a major challenge is the highly fragmented network of public EV charging stations (SPKLU). Various operators utilize disparate protocols, resulting in isolated data silos and user range anxiety. This study proposes a nationally scalable application programming interface (API)-based gateway for cross-operator SPKLU integration. The system uses an event-driven internet of things (IoT) push mechanism. A standardized JavaScript object notation (JSON) API ensures interoperability and normalizes heterogeneous SPKLU data into a unified national dashboard. Pilot testing across 20 SPKLU locations in 10 major Indonesian cities validated cross-regional network reliability. The results demonstrate successful real-time status synchronization with a low margin of error (MoE) of 4.65%. To mitigate minor discrepancies caused by temporary network latency, a user interface (UI)-level timestamp transparency feature is proposed. Furthermore, by utilizing dynamic, region specific grid emission factors, the system accurately recorded an aggregated electricity consumption of approximately 3.5 million kWh in June 2025. This translates to an estimated 2.7 million kgCO₂eq in carbon emissions. Ultimately, this centralized platform eliminates information asymmetry for users. It also lays a crucial foundation for future smart grid integration, artificial intelligence (AI)-based demand forecasting, and Indonesia’s net zero emission (NZE) targets.
Volume: 24
Issue: 4
Page: 1113-1120
Publish at: 2026-08-01

Decision-tree-based machine learning for detecting coffee agroforestry using SPOT-7

10.12928/telkomnika.v24i4.27747
I Made; IPB University Khrisna Yoga Devandra , I Nengah; IPB University Surati Jaya , Tatang; IPB University Tiryana
This study develops a decision-tree-based machine-learning (ML) approach to identify coffee agroforestry plants using SPOT-7 satellite imagery. The algorithm was developed by examining the combination of image indices derived from SPOT-7 and biophysical variables. Detection using spectral variables is often hampered by spectral similarity between vegetation cover classes. This study found that a ML method that combines spectral and biophysical variables can significantly improve overall accuracy, from 60.4% (using conventional spectral variables alone) to 94% (using integrated spectral-biophysical variables). For detecting and identifying agroforestry coffee classes typically found under tree canopies, the addition of the “land cover” variable published by the Ministry of Environment and Forestry contributes significantly to the classification of agroforestry coffee. Important variables identified in this model are normalized difference vegetation index (NDVI), visible difference vegetation index (VDVI), normalized red-green vegetation index (NRGI), elevation, and land cover.
Volume: 24
Issue: 4
Page: 1307-1319
Publish at: 2026-08-01

NLP-driven hate speech detection on TikTok: a case study from UIN Sunan Ampel Surabaya

10.12928/telkomnika.v24i4.27419
Achmad; UIN Sunan Ampel Surabaya Teguh Wibowo , Aris; UIN Sunan Ampel Surabaya Fanani , Mujib; UIN Sunan Ampel Surabaya Ridwan , Bramasta; UIN Sunan Ampel Surabaya Kurnia Aji
This study examines hate speech detection in TikTok comments using natural language processing (NLP) techniques within the student community of UIN Sunan Ampel Surabaya. A dataset of 10,000 comments associated with the hashtag #PBAKUINSA2023 was analyzed using a lexicon-based sentiment analysis approach implemented through the TextBlob library, combined with Indonesian text preprocessing techniques, including tokenization, normalization, stopword removal, and stemming using the Sastrawi library. The results indicate that the proposed approach achieved an accuracy of 0.85, with precision of 0.88, recall of 0.83, and an F1-score of 0.854. Most comments were classified as neutral, while 31.8% were positive, and only a small proportion were negative. These findings suggest that discussions related to campus activities tend to be neutral or supportive. However, the findings also reveal that sentiment polarity does not always directly correspond to hate speech, as certain harmful expressions may appear neutral in lexicon-based analysis. This limitation highlights the need for more context-aware approaches. Overall, the proposed method provides an efficient solution for monitoring online discourse in academic environments.
Volume: 24
Issue: 4
Page: 1157-1167
Publish at: 2026-08-01

Enhancing emotional resilience in pre-service early childhood educators through digital learning environments

10.11591/ijere.v15i4.39637
Gulmira Mombiyeva , Ulbossyn Kyyakbayeva , Perizat Dautkaliyeva , Zhanna Zhunisbekova , Zhanar Saduova , Zhaxygul Issayeva
Emotional resilience (ER) is a critical competence for pre-service teachers, enabling them to manage stress, adapt to change, and maintain professional functioning. Digital learning (DL) environments present both challenges and opportunities for developing ER, yet empirical studies on structured interventions in these contexts remain limited, particularly in early childhood education (ECE). This study examined the effect of a structured DL intervention on the development of ER among pre-service teachers in ECE contexts in Kazakhstan. A quasi-experimental pretest-posttest design with non-equivalent groups was employed, involving 165 female pre-service teachers. The intervention included scaffolded digital tasks, reflective exercises, and instructor-led feedback. Baseline ER scores were comparable between groups. Following the intervention, participants in the experimental group (EG) demonstrated significantly higher ER compared to those in the control group (CG). These results indicate that structured DL interventions can effectively enhance ER by fostering adaptive coping, self-regulation, and problem-solving skills. Embedding scaffolded digital tasks combined with reflection and feedback into teacher education programs may support the preparation of resilient, reflective, and emotionally competent educators.
Volume: 15
Issue: 4
Page: 3279-3291
Publish at: 2026-08-01

Balancing support and bureaucracy: the enabling–constraining dynamics of research funding policies in a Philippine state university

10.11591/ijere.v15i4.38882
John Michael D. Aquino , Rushid Jay S. Sancon
Research funding policies has a critical role in shaping research productivity and governance in higher education institutions (HEIs). However, limited empirical studies have examined how these policies are experienced by faculty members and administrators, particularly in state universities within developing country contexts. This study explores the implementation of research funding and assistance guidelines in a Philippine state university using a phenomenological approach. Data were collected from 34 participants (15 administrators and 19 faculty members) through semi-structured interviews and document analysis and were analyzed thematically. The findings reveal an enabling–constraining dynamic in the implementation of research funding policies. Institutional support, including funding, incentives, and structured processes, enhances faculty motivation, research engagement, and planning quality. Conversely, bureaucratic complexity, procedural rigidity, and delays in fund release constrain efficiency, limit participation, and discourage sustained engagement. Anchored in self-determination theory (SDT) and structuration theory, the study demonstrates how research funding policies simultaneously influence faculty motivation and are shaped by institutional structures. These findings highlight the need for streamlined processes, consistent implementation, and faculty-centered support systems. The study contributes to the literature by providing empirical evidence of the enabling–constraining paradox of research funding policies and offers insights for improving research governance in higher education.
Volume: 15
Issue: 4
Page: 2750-2763
Publish at: 2026-08-01

Developing primary student innovators through local wisdom and integrated learning

10.11591/ijere.v15i4.39994
Suwicha Wansudon , Trai Unyapoti , Phatcharida Inthama
Elementary science, technology, engineering, arts, and mathematics (STEAM) education often struggles with abstract, disconnected lessons, leading to students engaging in “making without learning”. Addressing this gap, this study evaluated a novel STEAM learning package that uniquely integrates regional local wisdom with educational board games to enhance the innovator competencies and STEAM process knowledge of fifth-grade students. Employing a rigorous pretest-posttest quasi-experimental design, the study involved 240 students across four diverse school jurisdictions in Bangkok. To address common methodological limitations in educational research, a linear mixed model (LMM) was utilized to isolate the intervention’s true effect from nested school contexts. The results demonstrated that the experimental group achieved significantly higher innovator competencies and STEAM knowledge compared to the control group. Crucially, the variance analysis revealed that school administrative structure and resources had a negligible impact on student outcomes. This underscores the study’s strongest novelty: combining cultural heritage with gamification not only provides a highly effective pedagogical scaffold but also serves as an equitable instructional tool, successfully bridging the gap between abstract science and meaningful application across diverse educational environments.
Volume: 15
Issue: 4
Page: 3479-3488
Publish at: 2026-08-01

Fast-decoupled power flow optimization in 20 kV systems

10.12928/telkomnika.v24i4.27567
Abrar; Lancang Kuning University Tanjung , David; Lancang Kuning University Setiawan
Power flow optimization in electrical power distribution systems is crucial for maintaining voltage stability and energy efficiency. 20 kV distribution systems often face voltage profile instability issues due to high power losses, where conventional power flow analysis methods are sometimes inefficient in handling load fluctuations in medium-voltage networks. This study proposes power flow calculation optimization using the fast-decoupled approach to evaluate and improve voltage profiles quickly and accurately, as well as integrating reactive power compensation through strategic capacitor bank placement. Simulation results show that under existing conditions, the system experiences significant voltage drops, especially at Bus 27 (17.740 kV) and Bus 53 (7.640 kV). After implementing a 2×900 kVAr capacitor bank, the voltage profile increased dramatically: Bus 27 rose to 19.160 kV and Bus 52 reached 19.020 kV. This proves that the integration of the fast decoupled method and reactive power compensation is effective in minimizing voltage deviation and improving the operational stability of the distribution system.
Volume: 24
Issue: 4
Page: 1440-1448
Publish at: 2026-08-01

A blended learning model for creative thinking development

10.11591/ijere.v15i4.38784
Nuttapong Prasertsung , Alisa Songsriwittaya
Creative thinking is a critical competency in higher education in the industry 5.0 era. Although blended learning (BL) and design thinking (DT) are widely adopted, existing instructional approaches often lack explicit mechanisms for process-level quality regulation across the creative process, leading to inconsistent and insufficiently structured outcomes. To address this gap, this study develops and validates the blended learning design thinking plan-do-check-act (BLDTPDCA) learning model, which embeds the plan-do-check-act (PDCA) cycle as a stage-based quality regulation mechanism within the DT process, rather than as a separate tool. The model incorporates a task–modality alignment linking face-to-face learning with the empathize, define, and ideate stages, and online learning with the prototype and testing stages to support continuous feedback. The study employed a model development and validation design. Five experts evaluated the model using a 19-item index of item-objective congruence (IOC), indicating high content validity (IOC=0.95). The findings demonstrate that the model enables systematic, process-level quality regulation within creative learning activities. This study contributes to the literature by reconceptualizing PDCA as an embedded regulatory mechanism within DT and by providing a structured, theory-informed framework for aligning instructional processes with measurable creative thinking development in higher education.
Volume: 15
Issue: 4
Page: 3228-3240
Publish at: 2026-08-01

Enhancing higher education quality through strategic human resource planning: evidence from international colleges

10.11591/ijere.v15i4.38651
Tubagus Achmad Darodjat , Supot Rattanapun
This research attempts to improve the quality of education at International College, Rajamangala University of Technology (IC-RMUT), applying qualitative research methods that describe human resources planning (HRP). Interviews, observations, and document review were used to collect data from 27 purposively selected informants at nine RMUT universities- administrators, lecturers, and support staff members of International Colleges. Data collection was conducted through interviews, observation, and review of documentation. The analysis technique uses a qualitative data analysis model with the broadest stages of data collection, analysis, and conclusions. Results of the research relate to preparing HRP and adhering to the vision and mission of the IC-RMUT. Then reviewing and reformulating periodically according to student developments and factors that can influence HRP in its implementation. The paper examines obstacles faced in implementing HRP such as the large number of changes in leadership and lecturers. It also looks at the inconsistent application of the human resources management (HRM) concept and how it affects one target and another activity and ultimately HRP activities become undirected.
Volume: 15
Issue: 4
Page: 3004-3014
Publish at: 2026-08-01

Academic capital and institutional stratification: determinants of graduate success in Moroccan higher education

10.11591/ijere.v15i4.39123
Youness Ezzaaime , Said Maizzou , Ali Raigat , Abdeljabbar Abdouni
In spite of the massification of higher education after the implementation of the licence–master–doctorate (LMD) reform, the graduation rates from open-access faculties stay low (under 30%), and the factors driving these low performance rates are not well understood. This study attempts to explain factors influencing the stratification of academic achievement of students graduating from Hassan I University (2012–2017), through the analysis of the three factors: pre-university academic capital, institutional selectivity, and socioeconomic resources. On the baccalaureate grade, we use ordinary least squares (OLS) regression, analysis of variance (ANOVA), t-tests, and chi-square analyses to find that it is the strongest predictor with β = 0.481 and r = 0.595 (p < 0.001), which is better than international benchmarks. The segregation effect by institution is the highest among the groups’ effects, with selective institutions having an effect of over 2.5 grade points (when controlling for student inputs), indicated by the large group effect (η2 = 0.275). Receipt of scholarships is a positive predictor of performance (β = 0.213 with p = 0.031); the employment in term-time is selected. 45.9% of grade variance is accounted for by the full model. We believe that academic disparities stem from secondary schooling and is exacerbated by disparities in faculty quality in the open market, and therefore secondary faculty preparation and open-access faculty resources must be addressed.
Volume: 15
Issue: 4
Page: 3253-3263
Publish at: 2026-08-01
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