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A model for flexible learning in graduate teacher education programs

10.11591/ijere.v15i4.39252
Marilyn U. Balagtas , Adonis P. David , Erminda C. Fortes , Arceli M. Amarles , Alvin B. Barcelona , Marla C. Pampango , Marjorie Naquita
This study aimed to develop a model for flexible learning (FL) appropriate to graduate teacher education programs (GTEP) based on the different practices of the graduate faculty and students in a teacher education institution (TEI) before and during the COVID-19 pandemic. A multimethods approach was employed, utilizing survey questionnaires, semi-structured interviews, and focus group discussions (FGD). Data were collected from 215 graduate students and 43 graduate faculty members who were selected through convenience sampling. The study resulted in the development of a model of FL for GTEP (MFL-GTEP), reflected in an outcome-based syllabus that highlights 10 areas of FL, all beginning with P: purpose, process, pedagogy, platform, people, place, pace, performance, product, and policy of learning. The MFL-GTEP promotes self-agency, self-regulation, and self-determination among education professionals pursuing GTEP. The challenges that graduate faculty and students experience in the implementation of FL are addressed in the (MFL-GTEP) to make the model more relevant, inclusive, and sustainable in a graduate teacher education program.
Volume: 15
Issue: 4
Page: 3193-3203
Publish at: 2026-08-01

Remaining useful life estimation for predictive battery maintenance with improved recurrent singular spectrum analysis algorithm

10.11591/ijece.v16i4.pp1817-1831
Chutipongse Boonyakitmaitree , Suchada Sitjongsataporn
As the global electric vehicle (EV) battery market is projected to reach a valuation of over USD 100 billion by the end of 2026, the demand for sophisticated battery management systems (BMS) has become more critical than ever. Accurate remaining useful life (RUL) prediction is essential for ensuring vehicle safety, optimizing maintenance, and evaluating retired batteries for second-life applications. However, existing prognostic methods often struggle to balance computational efficiency with predictive accuracy, especially during the early stages of battery usage. This research proposes combined weighted similarity-based and recurrent singular spectrum analysis (CWS-RSSA), a hybrid forecasting framework that integrates RSSA with a similarity-based approach through a weighted logistic switching mechanism. The algorithm is designed to be computationally lightweight, making it suitable for resource-constrained BMS hardware. The proposed method was validated using NASA and a large-scale dataset from MIT-Stanford consisting of 124 lithium-ion cells. Experimental results demonstrate that CWS-RSSA is capable of early-stage prediction with a relative error of 19.8%, whereas existing methods are unable to provide predictions. In later stages, once sufficient data becomes available, the algorithm achieves near-perfect accuracy with a negligible relative error on the NASA dataset and an average relative error of only 0.14% across the 124 MIT-Stanford batteries. Furthermore, the algorithm demonstrates robust performance in handling capacity regeneration phenomena. These findings suggest that CWS-RSSA represents a scalable and practical advancement for battery health management, supporting the transition toward a sustainable circular energy economy and providing a reliable foundation for second-life battery certification.
Volume: 16
Issue: 4
Page: 1817-1831
Publish at: 2026-08-01

Experimental validation of a low-cost microcontroller-based rack-level thermal control prototype

10.12928/telkomnika.v24i4.27838
Wandercleiton; University of Genoa Cardoso , Danyelle; University of Pavia Santos Ribeiro , Thiago Augusto; Federal Institute of Espírito Santo Pires Machado , Saulo Alexandre; Bastianello Consultancy Inacio , Elielton; Hidrovias do Brasil A. Cometti , Marcelo; Federal Institute of Espírito Santo Margon , Fernando; uConnect Telecom Baptista dos Santos Neves
The rapid growth of data centers (DCs), driven by digital transformation and the increasing adoption of artificial intelligence (AI), has intensified challenges related to thermal management and operational reliability. Cooling systems account for a substantial portion of total energy consumption and often show limited effectiveness in mitigating localized hotspots and dynamic temperature variations in high-density server environments. This study presents the development and experimental validation of a low-cost, microcontroller-based (MCU-based) localized thermal control system. The proposed architecture integrates a temperature sensor, an Arduino-based control unit, pulse-width modulation (PWM) driven fan actuation, and Ethernet communication for remote monitoring. The system was implemented in a standard 19-inch rack under controlled laboratory conditions using a simulated thermal load. Experimental results, based on the average of five independent tests, demonstrated that combined operation of the prototype with rack ventilation reduced the cooling time from 45 °C to 40 °C to 50 ± 2 s, compared to approximately 5 minutes with rack ventilation alone and more than 12 minutes under natural convection. The corresponding cooling rates were 0.10 °C/s, 0.015 °C/s, and 0.007 °C/s. These results indicate that simple, distributed thermal control strategies can effectively mitigate localized overheating and support rack-level thermal stability in data center microenvironments.
Volume: 24
Issue: 4
Page: 1131-1142
Publish at: 2026-08-01

XGBoost modeling for sparse spare-parts demand forecasting

10.12928/telkomnika.v24i4.27777
Brian Qaedi; Institut Teknologi Sepuluh Nopember (ITS) Laksono Putra , Jerry Dwi; Institut Teknologi Sepuluh Nopember (ITS) Trijoyo Purnomo
Spare parts demand in many industrial systems is inherently sparse and intermittent. In practice, long periods of zero usage are common, even though inventory must still be maintained to ensure operational reliability. This situation increases holding costs and the risk of obsolescence, while also limiting the effectiveness of conventional forecasting techniques. This study demonstrates that a global XGBoost model trained across multiple spare-part items significantly outperforms item-specific models under sparse demand conditions. Using six years of historical spare-parts usage and procurement data from the energy sector, this study compares global and single-item extreme gradient boosting (XGBoost) modeling strategies. Forecast accuracy is evaluated using mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and median absolute error (MdAE), which is particularly suitable for zero-inflated demand patterns. The results consistently show that the global XGBoost model achieves lower errors across all metrics. In particular, the global model attains a markedly lower MdAE (0.00018), indicating greater robustness when demand is irregular and intermittent.
Volume: 24
Issue: 4
Page: 1287-1293
Publish at: 2026-08-01

Beyond coal: optimization of hybrid floating PV–hydropower systems for Indonesia’s decarbonization

10.12928/telkomnika.v24i4.27858
Firsta; IPB University Zukhrufiana Setiawati , Tania; IPB University June , Muh; IPB University Taufik , Rudi; Tanjungpura University Kurnianto
Indonesia’s failure to meet the 23% renewable energy target, resulting in continued reliance on fossil fuels, necessitated a multi-criteria evaluation of hybrid energy systems. This study aimed to simultaneously minimize the levelized cost of energy (LCOE), reduce carbon dioxide (CO2) emissions (decarbonization), and maximize the renewable energy penetration by integrating a coal-fired power plant (coal), hydroelectric power plant (hydro), and floating photovoltaic (FPV) power plant at sites in western Java (West Java and Banten provinces). Thirteen configurations were evaluated using European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 climate data (1991-2020) and hybrid optimization of multiple energy resources professional (HOMER Pro) simulations. The optimal coal hydro-FPV-Li-Ion hybrid configuration achieved 91.1% renewable energy penetration, 94.5% decarbonization, avoided ~4.1 million kg CO2 annually, delivered an LCOE of $0.1201/kWh with an internal rate of return (IRR) of 7.6%, return on investment (ROI) of 5.9%, and payback period of 8.7 years. This configuration outperformed the pumped hydro storage alternative, demonstrating 38% lower capital expenditure (CAPEX) and 49% faster payback period, confirming its economic suitability for Indonesia’s fiscal constraints. These findings indicate that a hybrid energy system with battery storage technology is a technically and economically viable pathway toward Indonesia’s decarbonization agenda.
Volume: 24
Issue: 4
Page: 1427-1439
Publish at: 2026-08-01

Design and construction of microcontroller-based exhaust emission measurement equipment for freight transportation

10.12928/telkomnika.v24i4.27739
Andi; Universitas Muhammadiyah Parepare Irmayani Pawelloi , Muh Huzaifah; Universitas Muhammadiyah Parepare Ashaba , Hakzah; Universitas Muhammadiyah Parepare Hakzah , Asrul; Universitas Muhammadiyah Parepare Amiruddin , Alauddin; Universitas Muhammadiyah Parepare Yunus , Muhammad; Universitas Muhammadiyah Parepare Zainal , Wahyuddin; Universitas Muhammadiyah Parepare Wahyuddin
Exhaust emissions from motor vehicles, especially freight transportation, are one of the main causes of air pollution in cities. This research aims to develop a portable and low-cost microcontroller-based vehicle emission measurement system. The system uses Arduino Uno with MQ-7 and MQ-2 sensors to detect carbon monoxide (CO) and hydrocarbons (HC) concentrations in real-time, with the measurement results displayed on the liquid crystal displays (LCD) screen. Validation is carried out by comparing the measurement results of the tool with a calibrated gas analyzer as a standard tool. The test results showed an error rate of 0.29%–1.79% for CO and 1.85%–3.84% for HC, as well as sensor stability after about 360 seconds of heating. With its compact design, easy to operate, and low cost, this system has the potential to be an alternative vehicle emission monitoring tool for field inspection and testing activities at vehicle workshops.
Volume: 24
Issue: 4
Page: 1385-1395
Publish at: 2026-08-01

Privacy-preserving messaging for medical device networks

10.12928/telkomnika.v24i4.27794
Rachmad; Universitas Brawijaya Andri Atmoko , Salnan; Universitas Brawijaya Ratih Asriningtias , Akas; Politeknik Negeri Jember Bagus Setiawan , Devasis; Acharya Institute of Technology Pradhan , Ismail; Karabuk University Rakip Karas
Internet of medical things (IoMT) deployments rely on lightweight messaging, but the message queuing telemetry transport (MQTT) protocol still exposes sensitive metadata through plaintext topic names and stable client identifiers. In healthcare settings, this visibility can reveal patient identity, location, and monitored condition even when payloads are encrypted. This paper presents a gateway-based privacy architecture that replaces semantic MQTT topics and client identifier (ClientIDs) with rotating pseudonyms managed by a topic and ID privacy manager (TPM). The design uses hash-based message authentication code using secure hash algorithm 256-bit (HMAC-SHA256) for pseudonym generation, advanced encryption standard-galois/counter mode (AES-GCM) for payload protection, and a short overlap phase that preserves message delivery during rotation without modifying the broker. Experiments from 5 to 10,000 patients show a consistent 5.0x increase in topic diversity, 2.50 μs per message cryptographic overhead, 0.063 ms maximum latency overhead, and zero packet loss. These results indicate that practical IoMT deployments can improve metadata privacy while still meeting real-time clinical communication requirements.
Volume: 24
Issue: 4
Page: 1320-1330
Publish at: 2026-08-01

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

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

An improved harvested energy management mechanism for wireless sensor networks

10.12928/telkomnika.v24i4.27486
Abdelmalek; University of Ibn-Khaldoun Bengheni , Messaoud; Ahmed Ben Yahia El Wancharissi University Hameurlaine
Wireless sensor networks (WSN) play a vital role in monitoring and communication applications, but their performance is often constrained by limited battery power. Energy harvesting (EH) technologies have emerged as a promising solution to extend network lifetime by supplying supplementary energy from the environment. However, efficiently balancing harvested and consumed energy remains a significant challenge. This paper introduces an improved harvested energy management mechanism (IHE2M) that dynamically adjusts the duty cycle of sensor nodes based on residual energy availability. Unlike traditional approaches that rely on fixed duty cycles, IHE2M allows each node to determine its sleep and active periods adaptively, reducing collisions, idle listening, and unnecessary retransmissions. The mechanism was evaluated through OMNeT++/MiXiM simulations and compared with existing schemes such as EH2M and dynamic source routing (DSR). Results demonstrate that IHE2M achieves higher packet delivery ratios, lower latency, and better throughput while reducing average energy consumption per node. The findings confirm that IHE2M provides a more sustainable and efficient solution for energy harvesting WSN, improving reliability and extending network lifetime.
Volume: 24
Issue: 4
Page: 1102-1112
Publish at: 2026-08-01

Evaluation of the efficiency of delay tolerant network routing protocols for smart environment development in Makassar

10.12928/telkomnika.v24i4.27102
Abdul; Universitas Handayani Makassar Latief Arda , Symsu; Universitas Handayani Makassar Alam , Agussalim; Universitas Pejuang Nasional Veteran Surabaya Agussalim , Matalangi; Universitas Kristen Indonesia Paulus Matalangi
Delay tolerant network (DTN) has become a promising communication paradigm for internet of things (IoT)-based smart environments, where intermittent connectivity and heterogeneous node mobility challenge reliable data delivery. However, selecting an appropriate DTN routing protocol remains difficult because existing protocols offer different trade-offs among delivery ratio, latency, communication overhead, hop count, and energy consumption. Although many studies have evaluated DTN routing protocols, few have examined their performance under heterogeneous urban mobility conditions representing emerging smart cities such as Makassar. This study compares four DTN routing protocols Epidemic, probabilistic routing protocol using history of encounters and transitivity version 2 (PRoPHETv2), spray and wait, and MaxProp using The ONE Simulator with a Makassar-inspired mobility scenario involving 50–125 nodes over a 12-hour simulation. Performance was evaluated using delivery ratio, average latency, overhead ratio, average hop count, and estimated energy consumption. The results show that MaxProp achieved the highest delivery ratio (0.69 at 125 nodes), whereas spray and wait consistently produced the lowest latency (≈118 s), overhead ratio (< 45,000), and average hop count (≈2). In contrast, epidemic generated excessive overhead and energy consumption because of uncontrolled message replication. These findings indicate that spray and wait is the most resource-efficient protocol, while MaxProp is preferable for applications prioritizing delivery reliability.
Volume: 24
Issue: 4
Page: 1143-1156
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

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

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
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