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Beyond adoption: measuring the success of mandatory information systems through an integrated ECM and ISSM

10.11591/ijece.v16i4.pp2031-2041
Muhammad Rosyid Ridlo , Muhammad Fachri Shandika Iman , Reny Yuliati
The successful implementation of mandatory organizational information systems depends not only on system adoption but also on user satisfaction. However, most post-adoption evaluation studies have focused on voluntary systems, leaving mandatory public sector deployments substantially underexplored. This study evaluates the determinants of employee satisfaction with the Coretax Administration System, a nationwide integrated tax platform implemented by the Directorate General of Taxes in Indonesia. To provide a comprehensive explanation of post-adoption evaluation, this research integrates the Expectation Confirmation Model (ECM) and the Information System Success Model (ISSM), examining how system quality, information quality, and service quality influence confirmation and perceived usefulness, which in turn determine user satisfaction. Using a quantitative approach, data were collected from 292 employees actively using the system and analyzed through Partial Least Square Structural Equation Modeling (PLS-SEM). The results demonstrate that the integrated model exhibits strong predictive power, explaining 76.4% of the variance in user satisfaction. System quality emerged as the most influential determinant, significantly affecting confirmation and perceived usefulness, which subsequently drives satisfaction. Meanwhile, information quality and service quality showed selective effects, indicating that technical reliability plays a more critical role than supportive features in a mandatory environment. The findings offer actionable guidance for policymakers and IS architects engaged in large-scale compulsory digital transformation initiatives in the public sector.
Volume: 16
Issue: 4
Page: 2031-2041
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

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

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

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

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

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

Enhancing undergraduates’ career preparedness: environmental determinants and mediating role of resilience

10.11591/ijere.v15i4.39733
Ainullutfi Azzman , Teoh Sian Hoon , Leele Susana Jamian , Nurhayani Romeo , Mohammad Hafiz Mohd Yusof , Koo Ah Choo
Current environmental changes, combined with personal challenges, require further study to address the existing knowledge gap in career preparedness. Hence, this study aims to examine determinants of undergraduates’ career preparedness by integrating environmental and psychological factors within an extended theoretical framework. Specifically, this study investigates the effects of resilience as a mediator in the relationship between environmental factors and career preparedness. This study used stratified purposive sampling to capture final-year undergraduates from universities in three main structures, namely research universities, non-research public universities, and private universities. A total of 409 undergraduates participated in this study. A correlational research design was employed for the investigation of the relationships. Structural equation modeling (SEM) was used to test the hypothesized relationships and mediation effects. The results indicated that environmental factors were a robust predictor of resilience (effect size, f2=1.377) and exerted a small but significant direct effect (f2=0.051) on career preparedness. Furthermore, resilience strongly predicted career preparedness (f2=0.450). Overall, the findings show that resilience mediates the relationship between environmental factors and career preparedness. This study contributes to the literature by demonstrating that resilience-based activities, such as career planning and goal-setting sessions, should be focused on undergraduates who are participating in a constructive environment.
Volume: 15
Issue: 4
Page: 2823-2839
Publish at: 2026-08-01

Intelligent routing-based attack detection in Internet of Things networks using artificial intelligence

10.11591/ijece.v16i4.pp2169-2181
Huda Saloom Sultan , Asseel Jabbar Almahdi , Murteza Hanoon Tuama , Athar Hussein Mohammed
The fast-growing Internet of Things (IoT) networks have posed considerable security risks because of decentralized network designs, dynamic topologies, and inadequate computation capabilities. Current intrusion detection strategies are primarily traffic-based, but without paying attention to routing-layer dynamics, which are paramount in multi-hop IoT systems. To overcome this drawback, this paper suggests a smart routing-conscious attack detection model which combines routing-layer monitoring with methods of artificial intelligence to improve the security of IoT networks. The suggested framework constantly compares routing metrics, such as packet loss, change in hop count, end to end delay and energy consumption to detect malicious routing behavior in real time. Two types of artificial neural networks, feedforward neural network (FFNN) and convolutional neural network (CNN) are used to categorize routing activities as normal or malicious. The experimentation on simulation was carried out by using NS-2 in a dynamic multi-hop IoT environment where routing-based DoS attacks were implemented. The experimental results reveal that CNN model had a higher detection accuracy of 85.76% with lower execution time of 17 s compared to the FFNN model which had an accuracy of 82.76% and an execution time of 18 s. Moreover, the suggested framework enhanced reliability of routing by minimizing the packet loss and communication delay and having low routing overhead. These results support the hypothesis that routing-aware intelligence can be used to enhance AI-based intrusion detection to create an adaptive, routing-aware, and resource-efficient security solution to decentralized networks of IoT devices.
Volume: 16
Issue: 4
Page: 2169-2181
Publish at: 2026-08-01

Artificial intelligence usage in the teaching-learning process: perception and challenges

10.11591/ijere.v15i4.38964
Ishani Basak , Benny Thomas , Shinto Thomas
Artificial intelligence (AI) has advanced in the post-pandemic era and is unavoidable in teaching and learning. Teachers’ perceptions, as the primary gatekeepers, are essential for ensuring quality education and inclusive classrooms, with AI as a collaborator. While some teachers resist these technological shifts, others are actively adapting an AI-assisted teaching approach. We conducted this study to understand the reasons for teachers’ resistance (challenges and difficulties) and how they perceive the use of AI in the teaching, learning, and assessment process, because the first step in effective incorporation is having a favorable attitude towards it. Hence, this study explored the perceptions of 15 secondary private school teachers, selected through purposive sampling, regarding the incorporation of AI into teaching, learning, and assessment processes, as well as the challenges they faced. The researchers developed an in-depth interview schedule and conducted interviews to understand participants’ perceptions and challenges. The data is analyzed following the thematic analysis steps by Braun and Clarke. Thematic analysis revealed that teachers demonstrated a positive understanding towards the pedagogical relevance of AI, rather than merely having a favorable perception. Furthermore, teachers predominantly viewed AI as an additional tool to enhance the effectiveness of knowledge transactions and instructional design. The challenges include infrastructure accessibility and professional training; time management for preparation, skill updating, and fulfilling varied teaching and other responsibilities; the inability to verify the accuracy of information; and parental mindset. This study offers insights for developing AI-aided teacher training and relevant curricula for schools.
Volume: 15
Issue: 4
Page: 3124-3140
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

Digital divide and fairness perceptions of computer-based testing in Vietnam

10.11591/ijere.v15i4.37322
Tran Thi Hoa Tien , Phan Thi Thuy An , Do Huy Duc , Le Ha Giang , Ngo Thi Minh Hanh , Tran Thi Ngoc Anh
Equity concerns are central to large-scale shifts toward computer-based testing (CBT), yet evidence from developing systems remains limited on whether institutional supports can offset household digital inequalities. This study surveyed 1,197 Vietnamese high school students (grades 10–12) using a 20-item CBT acceptance scale (α=.94) and two single-item fairness indicators. Overall acceptance was moderately positive (M=3.54, SD=0.74), whereas fairness perceptions were markedly weaker: only 31.7%–38.2% agreed that CBT ensures fair scoring and 55.1%–66.7% worried that CBT could create inequality, reflecting unequal fairness perceptions across areas, with concerns strongest in rural settings. Group comparisons showed small but consistent advantages in acceptance for students with prior CBT experience and school-level computer access (g≈.16–.20), while home computer ownership had negligible association (g≈.01). Acceptance did not differ by residential area (η²≈.001), but fairness concerns varied across areas. These findings suggest that institutional exposure and governance practices—rather than household device ownership—are the most actionable levers for equitable CBT implementation. Practical implications include strengthening school-based access and practice opportunities, transparent proctoring and contingency procedures, and integrating fairness monitoring into CBT dashboards.
Volume: 15
Issue: 4
Page: 3075-3085
Publish at: 2026-08-01

Academic stress and academic performance among early childhood education students in Peru

10.11591/ijere.v15i4.38904
Alvaro Camposano-Cordova , Juan Antonio Picoy-Gonzales , Néstor Jesús Caro-Seminario , Clorinda Barrionuevo-Torres , José Antonio Nieto-Custodio , Maria Clarisa Tovar-Torres-De-Aguilar , Melina Penélope Tolentino-Cotrina , Nelly Olga Zela-Payi , Jesus Mery Arias-Huanuco , Wendy Nelly Bada-Laura , Rosaura Huarcaya-Taype , Gladys Margarita Espinoza Herrera , Christian Luis Torres-Acevedo , Daniel Joel
Academic stress is widely assumed to impair academic performance in higher education; however, empirical evidence remains inconsistent, particularly in professional teacher-training programs. This study examined the relationship between perceived academic stress and academic performance among 116 undergraduate students (18–25 years) enrolled in an early childhood education program at a public university in Peru. A quantitative cross-sectional correlational design was employed. Academic stress was measured using the perception of academic stress scale, while academic performance was obtained from official grade point averages (GPA), ensuring objective assessment. Data were analyzed using multiple regression and non-parametric tests (Mann–Whitney U and Kruskal–Wallis H). Results indicated moderate stress levels (M=3.65) and stable academic performance (M GPA=14.72). Academic stress did not significantly predict GPA, whereas age emerged as the only significant positive predictor. Gender and academic cycle showed no significant effects. These findings suggest that academic stress may not necessarily undermine performance and highlight the potential role of maturity and adaptive capacities in supporting resilience among future teachers.
Volume: 15
Issue: 4
Page: 3392-3404
Publish at: 2026-08-01

Formation of professional competencies of future biology teachers based on STEM technologies

10.11591/ijere.v15i4.38133
Symbat Ibadulla , Kalampyr Zhumagulova , Gulmira Zhussipova , Makhabbat Amanbayeva , Gani Issayev , Ardak Bostanova
This study developed an integrated pedagogical model incorporating Python programming, Arduino biosensors, and virtual reality (VR) applications aimed at biology teacher training. The 20-week study involved 124 participants (62 in the experimental group (EG) and 62 in the control group (CG)), with baseline competency levels assessed at the outset. Through collaborative projects, participants created technological applications integrated with biological content. Subject knowledge scores in the EG increased by 42.2% (from 22.3 ± 4.1 to 31.7 ± 3.2), compared to a 14.9% increase in the CG (from 22.1 ± 3.9 to 25.4 ± 3.7) (p 
Volume: 15
Issue: 4
Page: 3542-3556
Publish at: 2026-08-01
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