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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

Reconfigurability of graphene-based hexagonal patch operating in the Ku band

10.12928/telkomnika.v24i4.27737
Hassna; Moulay Ismail University Agoumi , Seddik; Moulay Ismail University Bri , Youssef; Mohammed V University El Amraoui , Adil; Moulay Ismail University Saadi
This paper presents a novel graphene-based impedance reconfigurability approach for Ku-band hexagonal patch antennas, demonstrated at both the single-element and 4×4 array levels. Unlike conventional metallic or diode based reconfigurable antennas, frequency tuning is achieved by exploiting the tunable surface conductivity of graphene integrated into E-shaped slots, without altering the antenna geometry or employing active lumped components. Antenna performance is evaluated using full-wave electromagnetic simulations for two graphene states (“on” state and “off” state), representing distinct surface impedance conditions. The single element exhibits dual resonances at 14.81 GHz and 15 GHz, with reflection coefficients of -52.56 dB and -38.18 dB, bandwidths of 783 MHz and 517 MHz, and a peak gain of 7.8 dBi. The 4×4 array exhibits multiple resonances between 12.5–16.8 GHz (off state) and 12.6–17.15 GHz (on state), achieving bandwidths up to 2750 MHz and a maximum gain of 13.64 dBi. These results demonstrate a scalable, geometry-preserving reconfigurable antenna solution for compact Ku-band systems.
Volume: 24
Issue: 4
Page: 1372-1384
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

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

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

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

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

Acoustic and vibration side channel analysis on post-quantum cryptography using image-based deep learning

10.12928/telkomnika.v24i4.27791
Abdul; Muhammadiyah University of North Maluku Haris Muhammad , Gamaria; Muhammadiyah University of North Maluku Mandar , Adelina; Muhammadiyah University of North Maluku Ibrahim
Post-quantum cryptography (PQC) is designed to resist quantum-era attacks; however, practical implementations remain vulnerable to physical side channel leakage. This work proposes an image-based acoustic–vibration side-channel analysis framework to assess non-invasive leakage in PQC systems. Acoustic and vibration signals from secret-dependent executions are modeled and transformed into time–frequency spectrograms using short time fourier transform (STFT). The dataset comprises 1,545 samples (1,236 training and 309 testing), acquired at 16 kHz and segmented into 2.5-second windows. Leakage classification is performed using convolutional neural networks (CNNs) and vision transformers (ViTs) under single-modality and multimodal fusion settings. Results show that acoustic signals yield strong leakage, achieving up to 100% accuracy with CNN, while vibration signals reach up to 98.75%. Multimodal fusion improves training stability and overall performance, and ViT models demonstrate better generalization across modalities. These findings confirm that multimodal spectrogram based deep learning is effective for PQC side-channel analysis and underscore the need for rigorous physical security evaluation in real-world PQC implementations.
Volume: 24
Issue: 4
Page: 1241-1252
Publish at: 2026-08-01

A hybrid retrieval augmented generation framework for automated educational document understanding and intelligent response generation

10.11591/ijece.v16i4.pp1964-1975
Basavesh D. , Jayashree Nagaraj
New students often struggle when short articles clash with thick textbooks. Still, even though large language models offer some teaching support, standard online setups lack focused accuracy - sometimes making things up - and risk user data control. Here comes an idea: build a tightly tested, self- contained system that aligns learning materials automatically without needing the internet, keeping information private by design. One look at two setups shows how they handle local reasoning differently. Instead of using both encoder and decoder parts, one system skips the encoder entirely. That simpler design grabs full context through ChromaDB without shrinking the data first. Meanwhile, the older type crunches input down, losing meaning along the way. Even though it runs fast - just under a second - errors pop up often, four out of five responses drifting off course. On the flip side, the new method builds correct code nearly every time, adds clear explanations tied to lesson goals, yet takes more than fourteen seconds to reply. Slower? Yes. More accurate? Clearly. What stands out is how compressed models running locally can still catch up in understanding classroom content. Another key point emerges: building tutors powered by artificial intelligence (AI) becomes safer when data never leaves the device and outside services are not needed at all.
Volume: 16
Issue: 4
Page: 1964-1975
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

A study on exploring the effects of the school climate and teacher’s accountability on academic performance of students in early childhood care and education

10.11591/ijere.v15i4.36631
Kalpana Nagar , G. S. Prakasha
This study investigates parental perceptions of school climate, teachers’ accountability, and students’ academic performance, as well as the relationship among these three factors. The descriptive research uses the demographic background of parents as a stratified sampling method to collect primary data. The statistical procedure shows that Pearson’s product-moment correlations between school climate and teachers’ accountability affect the academic achievement, with effect sizes of r=0.632, r=.646, which significantly correlate with each other. Regression analysis reveals the variability of the effect sizes of school climate and teachers’ accountability on academic achievement, with the values of β=0.070 and β=0.115. The independent variable, school climate and teachers’ accountability, explains 45.4% of the variability of academic performance of the students in early childhood care and education. The present study recommends that training teachers would link their accountability and measures to track the supportive climate of school to improve the student’s academic performance. Future research should conduct longitudinal studies to examine how relationships between school climates and teachers’ accountability affect different student populations and their academic performance.
Volume: 15
Issue: 4
Page: 2883-2890
Publish at: 2026-08-01

Developing a 2-DOF robotic arm kit for embodied AI learning in middle school technology education

10.11591/ijere.v15i4.39254
Dasol Kim , Sooin Kim
Artificial intelligence (AI) literacy is increasingly emphasized in technology education, yet many middle-school classroom activities remain screen-based and offer limited opportunities to experience the full AI pipeline in an authentic design context. To address this gap, this study developed a low-cost, classroom-ready 2-degree-of-freedom (2-DOF) robotic arm kit and an eight-lesson AI-integrated instructional unit in which students assembled the robot arm, collected and labeled image data, trained and improved a classification model, and applied model outputs to robot-arm control. A quasi-experimental pretest–posttest control-group design was employed with 98 ninth-grade students in four intact classes (experimental group, n=46; comparison group, n=52). Quantitative data were analyzed using ANCOVA with pretest scores as covariates, and qualitative data from student reflection reports were analyzed thematically. The experimental group significantly outperformed the comparison group on value of AI, efficacy of AI, AI literacy, and total scores. Qualitative findings further showed that students recognized both benefits and risks of AI and proposed multi-layered safety strategies involving physical safeguards, operational rules, and data/model management. These findings suggest that embedding the full AI pipeline in a tangible engineering-design task can support embodied, responsibility-oriented AI learning in middle school technology education.
Volume: 15
Issue: 4
Page: 3060-3074
Publish at: 2026-08-01

The effect of systematically created success situations on learning motivation among primary school students

10.11591/ijere.v15i4.39540
Sabila Abzhanova , Gulnar Uaisova , Madina Togatay , Aliya Jakhayeva , Nazymkul Altayeva , Saule Zhorayeva
Learning motivation is a critical factor influencing students’ academic engagement, persistence, and long-term educational outcomes. In primary education, motivation is particularly important, as early learning experiences shape children’s attitudes toward school and their willingness to participate in academic activities. The aim of this study was to examine the impact of systematically created success situations on learning motivation among primary school students in Kazakhstan. The study involved 72 participants. A quasi-experimental design was employed, with students participating in a six-week intervention structured to provide frequent success experiences, while a comparison group continued regular classroom instruction. Motivation was assessed before and after the intervention using selected subscales of the intrinsic motivation inventory, including interest/enjoyment, perceived competence, and effort/importance. The results indicated significant improvements in all measured dimensions of motivation for students who experienced the intervention compared with the comparison group. Increases were observed in students’ perceived competence, engagement with learning activities, and willingness to invest effort. These findings suggest that systematically creating opportunities for success is an effective approach to enhancing learning motivation in primary education and provide practical guidance for classroom implementation.
Volume: 15
Issue: 4
Page: 3405-3414
Publish at: 2026-08-01

Teachers’ perspectives on pedagogical challenges of AI integration in English language teaching

10.11591/ijere.v15i4.39226
Parthiban Ganesan , Karthikeyan Padmanathan , Thiyagu Kaliappan , Raja Kumar Subburaj , Durgaprasad Sahoo
Artificial intelligence (AI) tools are rapidly being integrated into English language teaching (ELT). However, limited empirical evidence exists regarding the pedagogical challenges teachers encounter during classroom implementation. This study investigates whether demographic variables influence ELT teachers’ perceptions of AI effectiveness and related pedagogical challenges. A quantitative survey design was employed, involving 200 ELT teachers with prior AI exposure. Data were collected using a validated Likert-scale instrument (α=0.81) and analyzed using independent samples t-tests, one-way ANOVA with Tukey post hoc analysis, and Chi-square (χ²) tests. Results revealed significant gender differences in perceived effectiveness (t=2.504, p=0.044) and pedagogical challenges (t=2.627, p=0.018), with female teachers reporting higher mean scores. Locality differences were significant only for perceived effectiveness (F=4.610, p0.05) or teaching experience (χ²=4.266, p>0.05). Educational level taught was significantly associated with perceived effectiveness (χ²=13.816, p
Volume: 15
Issue: 4
Page: 3439-3450
Publish at: 2026-08-01
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